Publications
Materials Informatics - High Performance Data Mining - Healthcare Informatics - Social Media Analytics - Astroinformatics - Climate Informatics - Bioinformatics - Miscellaneous
Materials Informatics
Y. Mao, S. Keshavarz, M. N. T. Kilic, K. Wang, Y. Li, A. C. E. Reid, W. Liao, A. Choudhary, and A. Agrawal, “A deep learning-based crystal plasticity finite element model,” Scripta Materialia, vol. 254, p. 116315, 2025. [url] [bib]
@article{MKK25, title = {A deep learning-based crystal plasticity finite element model}, author = {Mao, Yuwei and Keshavarz, Shahriyar and Kilic, Muhammed Nur Talha and Wang, Kewei and Li, Youjia and Reid, Andrew CE and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, journal = {Scripta Materialia}, volume = {254}, pages = {116315}, year = {2025}, publisher = {Elsevier} }K. Choudhary, D. Wines, K. Li, K. F. Garrity, V. Gupta, A. H. Romero, J. T. Krogel, K. Saritas, A. Fuhr, P. Ganesh, and others, “JARVIS-Leaderboard: A large scale benchmark of materials design methods,” npj Computational Materials, vol. 10, no. 1, p. 93, 2024. [url] [bib]
@article{CWL24, title = {JARVIS-Leaderboard: A large scale benchmark of materials design methods}, author = {Choudhary, Kamal and Wines, Daniel and Li, Kangming and Garrity, Kevin F and Gupta, Vishu and Romero, Aldo H and Krogel, Jaron T and Saritas, Kayahan and Fuhr, Addis and Ganesh, Panchapakesan and others}, journal = {npj Computational Materials}, volume = {10}, number = {1}, pages = {93}, year = {2024}, publisher = {Nature Publishing Group UK London} }A. L. Day, C. B. Wahl, V. Gupta, R. D. Reis, W. Liao, C. A. Mirkin, V. P. Dravid, A. Choudhary, and A. Agrawal, “Machine Learning-Enabled Image Classification for Automated Electron Microscopy,” Microscopy and Microanalysis, p. ozae042, 2024. [url] [bib]
@article{DWG24, title = {Machine Learning-Enabled Image Classification for Automated Electron Microscopy}, author = {Day, Alexandra L and Wahl, Carolin B and Gupta, Vishu and Reis, Roberto Dos and Liao, W. and Mirkin, Chad A and Dravid, Vinayak P and Choudhary, Alok and Agrawal, Ankit}, journal = {Microscopy and Microanalysis}, pages = {ozae042}, year = {2024}, publisher = {Oxford University Press US} }A. L. Day, C. B. Wahl, R. dos Reis, W. Liao, Y. Li, M. N. T. Kilic, C. A. Mirkin, V. P. Dravid, A. Choudhary, and A. Agrawal, “Rapid Image Segmentation Pipeline to Support Multimodal STEM Acquisition,” Microscopy and Microanalysis, vol. 30, no. Supplement 1, pp. 442–443, 2024. [url] [bib]
@article{DWR24a, title = {Rapid Image Segmentation Pipeline to Support Multimodal STEM Acquisition}, author = {Day, Alexandra L and Wahl, Carolin B and dos Reis, Roberto and Liao, W. and Li, Youjia and Kilic, Muhammed Nur Talha and Mirkin, Chad A and Dravid, Vinayak P and Choudhary, Alok and Agrawal, Ankit}, year = {2024}, journal = {Microscopy and Microanalysis}, volume = {30}, number = {Supplement 1}, pages = {442-443}, publisher = {Oxford Academic} }A. L. Day, C. B. Wahl, R. dos Reis, W. Liao, V. P. Dravid, A. Choudhary, and A. Agrawal, “Automated Nanoparticle Image Processing Pipeline for AI-Driven Materials Characterization,” in Proceedings of 33rd ACM International Conference on Information and Knowledge Management (CIKM), 2024, pp. 4462–4469. [url] [bib]
@inproceedings{DWR24b, author = {Day, Alexandra L and Wahl, Carolin B and dos Reis, Roberto and Liao, W. and Dravid, Vinayak P and Choudhary, Alok and Agrawal, Ankit}, title = {Automated Nanoparticle Image Processing Pipeline for AI-Driven Materials Characterization}, booktitle = {Proceedings of 33rd ACM International Conference on Information and Knowledge Management (CIKM)}, pages = {4462-4469}, year = {2024} }V. Gupta, K. Choudhary, B. DeCost, F. Tavazza, C. Campbell, W. Liao, A. Choudhary, and A. Agrawal, “Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets,” npj Computational Materials, vol. 10, p. 1, 2024. [url] [bib]
@article{GCD24, author = {Gupta, Vishu and Choudhary, Kamal and DeCost, Brian and Tavazza, Francesca and Campbell, Carelyn and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets}, journal = {npj Computational Materials}, year = {2024}, volume = {10}, pages = {1} }V. Gupta, Y. Li, A. Peltekian, M. N. T. Kilic, W. Liao, A. Choudhary, and A. Agrawal, “Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks,” Journal of Cheminformatics, vol. 16, no. 1, p. 17, 2024. [url] [bib]
@article{GLP24, title = {Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks}, author = {Gupta, Vishu and Li, Youjia and Peltekian, Alec and Kilic, Muhammed Nur Talha and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, journal = {Journal of Cheminformatics}, volume = {16}, number = {1}, pages = {17}, year = {2024}, publisher = {Springer} }K. Wang, V. Gupta, C. S. Lee, Y. Mao, M. N. T. Kilic, Y. Li, Z. Huang, W. Liao, A. Choudhary, and A. Agrawal, “XElemNet: Towards explainable AI for deep neural networks in materials science,” Scientific Reports, vol. 14, p. 25178, 2024. [url] [bib]
@article{WGL24, title = {XElemNet: Towards explainable AI for deep neural networks in materials science}, author = {Wang, Kewei and Gupta, Vishu and Lee, Claire Songhyun and Mao, Yuwei and Kilic, Muhammed Nur Talha and Li, Youjia and Huang, Zanhua and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, journal = {Scientific Reports}, volume = {14}, pages = {25178}, year = {2024}, publisher = {Springer Nature} }A. Yan, M. N. T. Kilic, A. Agrawal, R. dos Reis, and V. Dravid, “Neural Network Models Towards Space Group Determination Using Dynamically Simulated EBSD and TKD Patterns,” Microscopy and Microanalysis, vol. 30, no. Supplement 1, pp. 388–390, 2024. [url] [bib]
@article{YKA24, title = {Neural Network Models Towards Space Group Determination Using Dynamically Simulated EBSD and TKD Patterns}, author = {Yan, Alfred and Kilic, Muhammed Nur Talha and Agrawal, Ankit and dos Reis, Roberto and Dravid, Vinayak}, journal = {Microscopy and Microanalysis}, volume = {30}, number = {Supplement 1}, year = {2024}, pages = {388-390}, publisher = {Oxford Academic} }V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data,” in Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA), 2024. To appear. [bib]
@inproceedings{GLC24, author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data}, booktitle = {Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA)}, pages = {}, year = {2024}, note = {To appear} }M. N. T. Kilic, Y. Mao, V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Tackling the Nonlinearity Problem in Inverse Modeling: Mixture Density Network-Backed Quantized AutoEncoder,” in Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA), 2024. To appear. [bib]
@inproceedings{KMG24, author = {Kilic, Muhammed Nur Talha and Mao, Yuwei and Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Tackling the Nonlinearity Problem in Inverse Modeling: Mixture Density Network-Backed Quantized AutoEncoder}, booktitle = {Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA)}, pages = {}, year = {2024}, note = {To appear} }K. Wang, Y. Mao, M. Hasan, M. M. Billah, M. N. T. Kilic, V. Gupta, W. Liao, A. Choudhary, P. Acar, and A. Agrawal, “Deep Learning Based Inverse Modeling for Materials Design: From Microstructure and Property to Processing,” in Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA), 2024. To appear. [bib]
@inproceedings{WMH24, author = {Wang, Kewei and Mao, Yuwei and Hasan, Mahmudul and Billah, Md Maruf and Kilic, Muhammed Nur Talha and Gupta, Vishu and Liao, W. and Choudhary, Alok and Acar, Pinar and Agrawal, Ankit}, title = {Deep Learning Based Inverse Modeling for Materials Design: From Microstructure and Property to Processing}, booktitle = {Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA)}, pages = {}, year = {2024}, note = {To appear} }A. Agrawal and A. Choudhary, “Artificial Intelligence for Accelerating Materials Discovery,” in Artificial Intelligence for Science: A Deep Learning Revolution, A. Choudhary, T. Hey, and G. Fox, Eds. World Scientific, 2023, pp. 431–443. [url] [bib]
@inbook{AC23, author = {Agrawal, Ankit and Choudhary, Alok}, editor = {Choudhary, Alok and Hey, Tony and Fox, Geoffrey}, booktitle = {Artificial Intelligence for Science: A Deep Learning Revolution}, title = {Artificial Intelligence for Accelerating Materials Discovery}, publisher = {World Scientific}, year = {2023}, pages = {431-443} }V. Gupta, K. Choudhary, Y. Mao, K. Wang, F. Tavazza, C. Campbell, W. Liao, A. Choudhary, and A. Agrawal, “MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction,” Journal of Chemical Information and Modeling, vol. 63, no. 7, pp. 1865–1871, 2023. [url] [bib]
@article{GCM23, author = {Gupta, Vishu and Choudhary, Kamal and Mao, Yuwei and Wang, Kewei and Tavazza, Francesca and Campbell, Carelyn and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction}, journal = {Journal of Chemical Information and Modeling}, year = {2023}, volume = {63}, number = {7}, pages = {1865-1871} }K. Gumpula, N. Koloskov, D. Grzenda, V. Hewes, A. Aurisano, G. Cerati, A. Day, J. Kowalkowski, C. Lee, K. Wang, W. Liao, M. Spiropulu, A. Agrawal, J. Vlimant, L. Gray, T. Klijnsma, P. Calafiura, S. Conlon, S. Farrell, X. Ju, and D. Murnane, “Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers,” Journal of Physics: Conference Series, vol. 2438, p. 012091, 2023. [url] [bib]
@article{GKG23, author = {Gumpula, K and Koloskov, N and Grzenda, D and Hewes, V and Aurisano, A and Cerati, G and Day, A and Kowalkowski, J and Lee, C and Wang, K and Liao, W and Spiropulu, M and Agrawal, A and Vlimant, J and Gray, L and Klijnsma, T and Calafiura, P and Conlon, S and Farrell, S and Ju, X and Murnane, D}, title = {Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers}, journal = {Journal of Physics: Conference Series}, year = {2023}, volume = {2438}, pages = {012091} }V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data,” in Proceedings of International Joint Conference on Neural Networks (IJCNN), 2023, pp. 1–8. [url] [bib]
@inproceedings{GLC23, author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data}, booktitle = {Proceedings of International Joint Conference on Neural Networks (IJCNN)}, pages = {1-8}, year = {2023} }V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Evolution of artificial intelligence for application in contemporary materials science,” MRS Communications, pp. 1–10, 2023. [url] [bib]
@article{GLC23b, author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Evolution of artificial intelligence for application in contemporary materials science}, journal = {MRS Communications}, year = {2023}, pages = {1-10} }V. Gupta, Y. Lyu, D. Suarez, Y. Mao, W. Liao, A. Choudhary, W. K. Liu, G. Cusatis, and A. Agrawal, “Physics-based Data-Augmented Deep Learning for Enhanced Autogenous Shrinkage Prediction on Experimental Dataset,” in Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing (IC3), 2023, pp. 188–197. [url] [bib]
@inproceedings{GLS23, author = {Gupta, Vishu and Lyu, Yuhui and Suarez, Derick and Mao, Yuwei and Liao, W. and Choudhary, Alok and Liu, Wing Kam and Cusatis, Gianluca and Agrawal, Ankit}, title = {Physics-based Data-Augmented Deep Learning for Enhanced Autogenous Shrinkage Prediction on Experimental Dataset}, booktitle = {Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing (IC3)}, pages = {188-197}, year = {2023} }V. Gupta, A. Peltekian, W. Liao, A. Choudhary, and A. Agrawal, “Improving deep learning model performance under parametric constraints for materials informatics applications,” Scientific Reports, vol. 13, no. 1, p. 9128, 2023. [url] [bib]
@article{GPL23, title = {Improving deep learning model performance under parametric constraints for materials informatics applications}, author = {Gupta, Vishu and Peltekian, Alec and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, journal = {Scientific Reports}, volume = {13}, number = {1}, pages = {9128}, year = {2023} }Y. Mao, M. Hasan, C. Lee, M. N. T. Kilic, V. Gupta, W. Liao, A. Choudhary, P. Acar, and A. Agrawal, “A deep learning framework for time-series processing-microstructure-property prediction,” in Proceedings of 22nd IEEE International Conference on Machine Learning and Applications (ICMLA), 2023, pp. 890–893. [url] [bib]
@inproceedings{MHL23, author = {Mao, Yuwei and Hasan, Mahmudul and Lee, Claire and Kilic, Muhammed Nur Talha and Gupta, Vishu and Liao, W. and Choudhary, Alok and Acar, Pinar and Agrawal, Ankit}, title = {A deep learning framework for time-series processing-microstructure-property prediction}, booktitle = {Proceedings of 22nd IEEE International Conference on Machine Learning and Applications (ICMLA)}, pages = {890--893}, year = {2023} }Y. Mao, M. Hasan, A. Paul, V. Gupta, K. Choudhary, F. Tavazza, W. Liao, A. Choudhary, P. Acar, and A. Agrawal, “An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems,” npj Computational Materials, vol. 9, p. 111, 2023. [url] [bib]
@article{MHP23, author = {Mao, Yuwei and Hasan, Mahmudul and Paul, Arindam and Gupta, Vishu and Choudhary, Kamal and Tavazza, Francesca and Liao, W. and Choudhary, Alok and Acar, Pinar and Agrawal, Ankit}, title = {An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems}, journal = {npj Computational Materials}, year = {2023}, volume = {9}, pages = {111} }Y. Mao, S. Keshavarz, V. Gupta, A. Reid, W. Liao, A. Choudhary, and A. Agrawal, “AI for Learning Deformation Behavior of a Material: Predicting Stress-Strain Curves 4000x Faster Than Simulations,” in Proceedings of International Joint Conference on Neural Networks (IJCNN), 2023, pp. 1–8. [url] [bib]
@inproceedings{MKG23, author = {Mao, Yuwei and Keshavarz, Shahriyar and Gupta, Vishu and Reid, Andrew and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {AI for Learning Deformation Behavior of a Material: Predicting Stress-Strain Curves 4000x Faster Than Simulations}, booktitle = {Proceedings of International Joint Conference on Neural Networks (IJCNN)}, pages = {1-8}, year = {2023} }Y. Mao, H. Lin, C. X. Yu, R. Frye, D. Beckett, K. Anderson, L. Jacquemetton, F. Carter, Z. Gao, W. Liao, A. N. Choudhary, K. Ehmann, and A. Agrawal, “A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures,” Journal of Intelligent Manufacturing, vol. 24, pp. 315–329, 2023. [url] [bib]
@article{MLY23, author = {Mao, Yuwei and Lin, Hui and Yu, Christina Xuan and Frye, Roger and Beckett, Darren and Anderson, Kevin and Jacquemetton, Lars and Carter, Fred and Gao, Zhangyuan and Liao, W. and Choudhary, Alok N. and Ehmann, Kornel and Agrawal, Ankit}, title = {A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures}, journal = {Journal of Intelligent Manufacturing}, year = {2023}, volume = {24}, pages = {315-329} }C. Wahl, A. Day, V. Gupta, R. R. dos Reis, W. Liao, C. Mirkin, A. Choudhary, V. P. Dravid, and A. Agrawal, “Machine Learning Enabled Image Classification for Automated Data Acquisition in the Electron Microscope,” Microscopy and Microanalysis, vol. 29, pp. 1909–1910, 2023. [url] [bib]
@article{WDG23, author = {Wahl, Carolin and Day, Alexandra and Gupta, Vishu and dos Reis, Roberto R and Liao, W. and Mirkin, Chad and Choudhary, Alok and Dravid, Vinayak P and Agrawal, Ankit}, title = {Machine Learning Enabled Image Classification for Automated Data Acquisition in the Electron Microscope}, journal = {Microscopy and Microanalysis}, year = {2023}, volume = {29}, pages = {1909-1910} }K. Choudhary, B. DeCost, C. Chen, A. Jain, F. Tavazza, R. Cohn, C. W. Park, A. Choudhary, A. Agrawal, S. J. L. Billinge, E. Holm, S. P. Ong, and C. Wolverton, “Recent advances and applications of deep learning methods in materials science,” npj Computational Materials, vol. 8, p. 59, 2022. [url] [bib]
@article{CDC22, author = {Choudhary, Kamal and DeCost, Brian and Chen, Chi and Jain, Anubhav and Tavazza, Francesca and Cohn, Ryan and Park, Cheol Woo and Choudhary, Alok and Agrawal, Ankit and Billinge, Simon J. L. and Holm, Elizabeth and Ong, Shyue Ping and Wolverton, Chris}, title = {Recent advances and applications of deep learning methods in materials science}, journal = {npj Computational Materials}, year = {2022}, volume = {8}, pages = {59} }V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “BRNet: Branched Residual Network for Fast and Accurate Predictive Modeling of Materials Properties,” in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), 2022, pp. 343–351. [url] [bib]
@inproceedings{GLC22, author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {BRNet: Branched Residual Network for Fast and Accurate Predictive Modeling of Materials Properties}, booktitle = {Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)}, pages = {343-351}, year = {2022} }V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Which Deep Learning Framework Should I Use: A Comparative Study for Deep Regression Modeling,” in Proceedings of 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022, pp. 72–77. [url] [bib]
@inproceedings{GLC22b, author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Which Deep Learning Framework Should I Use: A Comparative Study for Deep Regression Modeling}, booktitle = {Proceedings of 2022 International Conference on Computational Science and Computational Intelligence (CSCI)}, pages = {72-77}, year = {2022} }M. Hasan, Y. Mao, K. Choudhary, F. Tavazza, A. Choudhary, A. Agrawal, and P. Acar, “Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures,” Integrating Materials and Manufacturing Innovation, vol. 11, pp. 230–240, 2022. [url] [bib]
@article{HMC22, author = {Hasan, M. and Mao, Y. and Choudhary, K. and Tavazza, F. and Choudhary, A. and Agrawal, A. and Acar, P.}, title = {Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures}, journal = {Integrating Materials and Manufacturing Innovation}, year = {2022}, volume = {11}, pages = {230-240} }D. Jha, V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Moving closer to experimental level materials property prediction using AI,” Scientific Reports, vol. 12, p. 11953, 2022. [url] [bib]
@article{JGL22, author = {Jha, Dipendra and Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Moving closer to experimental level materials property prediction using AI}, journal = {Scientific Reports}, year = {2022}, volume = {12}, pages = {11953} }Y. Mao, Z. Yang, D. Jha, A. Paul, W. Liao, A. Choudhary, and A. Agrawal, “Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design,” Integrating Materials and Manufacturing Innovation, vol. 11, pp. 637–647, 2022. [url] [bib]
@article{MYJ22, author = {Mao, Yuwei and Yang, Zijiang and Jha, Dipendra and Paul, Arindam and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design}, journal = {Integrating Materials and Manufacturing Innovation}, year = {2022}, volume = {11}, pages = {637-647} }A. Sardeshmukh, S. Reddy, B. P. Gautham, and A. Agrawal, “Machine Learning for Materials Science (MLMS),” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022, pp. 4902–4903. [url] [bib]
@inproceedings{SRG22, author = {Sardeshmukh, Avadhut and Reddy, Sreedhar and Gautham, B. P. and Agrawal, Ankit}, title = {Machine Learning for Materials Science (MLMS)}, booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)}, pages = {4902-4903}, year = {2022} }V. Gupta, K. Choudhary, F. Tavazza, C. Campbell, W. Liao, A. Choudhary, and A. Agrawal, “Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data,” Nature Communications, vol. 12, no. 6595, 2021. [url] [bib]
@article{GCT21, author = {Gupta, Vishu and Choudhary, Kamal and Tavazza, Francesca and Campbell, Carelyn and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data}, journal = {Nature Communications}, year = {2021}, volume = {12}, number = {6595} }J. Hewes, A. Aurisano, G. Cerati, J. Kowalkowski, C. Lee, W. Liao, A. Day, A. Agrawal, M. Spiropulu, J.-R. Vlimant, L. Gray, T. Klijnsma, P. Calafiura, S. Conlon, S. Farrell, X. Ju, and D. Murnane, “Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers,” in Proceedings of 25th International Conference on Computing in High Energy and Nuclear Physics (CHEP), 2021, p. 03054. [url] [bib]
@inproceedings{HAC21, author = {Hewes, Jeremy and Aurisano, Adam and Cerati, Giuseppe and Kowalkowski, Jim and Lee, Claire and Liao, W. and Day, Alexandra and Agrawal, Ankit and Spiropulu, Maria and Vlimant, Jean-Roch and Gray, Lindsey and Klijnsma, Thomas and Calafiura, Paolo and Conlon, Sean and Farrell, Steve and Ju, Xiangyang and Murnane, Daniel}, title = {Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers}, booktitle = {Proceedings of 25th International Conference on Computing in High Energy and Nuclear Physics (CHEP)}, pages = {03054}, year = {2021} }D. Jha, V. Gupta, L. Ward, Z. Yang, C. Wolverton, I. Foster, W. Liao, A. N. Choudhary, and A. Agrawal, “Enabling Deeper Learning on Big Data for Materials Informatics Applications,” Scientific Reports, vol. 11, no. 4244, 2021. [url] [bib]
@article{JGW21, author = {Jha, Dipendra and Gupta, Vishu and Ward, Logan and Yang, Zijiang and Wolverton, Christopher and Foster, Ian and Liao, W. and Choudhary, Alok N. and Agrawal, Ankit}, title = {Enabling Deeper Learning on Big Data for Materials Informatics Applications}, journal = {Scientific Reports}, year = {2021}, volume = {11}, number = {4244} }D. Jha, K. V. L. V. Narayanachari, R. Zhang, D. T. Keane, W. Liao, A. Choudhary, Y.-W. Chung, M. J. Bedzyk, and A. Agrawal, “Enhancing Phase Mapping for High-throughput X-ray Diffraction Experiments using Fuzzy Clustering,” in Proceedings of International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2021, pp. 507–514. [url] [bib]
@inproceedings{JNZ21a, author = {Jha, Dipendra and Narayanachari, KVLV and Zhang, Ruifeng and Keane, Denis T. and Liao, W. and Choudhary, Alok and Chung, Yip-Wah and Bedzyk, Michael J. and Agrawal, Ankit}, title = {Enhancing Phase Mapping for High-throughput X-ray Diffraction Experiments using Fuzzy Clustering}, booktitle = {Proceedings of International Conference on Pattern Recognition Applications and Methods (ICPRAM)}, pages = {507-514}, year = {2021} }D. Jha, K. V. L. V. Narayanachari, R. Zhang, J. Liao, D. T. Keane, W. Liao, A. Choudhary, Y.-W. Chung, M. J. Bedzyk, and A. Agrawal, “An Incremental Phase Mapping Approach for X-ray Diffraction Patterns using Binary Peak Representations,” in SDM Workshop on Domain-Driven Data Mining (DDDM), 2021, p. 11. [url] [bib]
@inproceedings{JNZ21b, author = {Jha, Dipendra and Narayanachari, KVLV and Zhang, Ruifeng and Liao, Justin and Keane, Denis T. and Liao, W. and Choudhary, Alok and Chung, Yip-Wah and Bedzyk, Michael J. and Agrawal, Ankit}, title = {An Incremental Phase Mapping Approach for X-ray Diffraction Patterns using Binary Peak Representations}, booktitle = {SDM Workshop on Domain-Driven Data Mining (DDDM)}, pages = {11}, year = {2021} }Z. Yang, T. Watari, D. Ichigozaki, A. Mitsutoshi, H. Takahashi, Y. Suga, W. Liao, A. Choudhary, and A. Agrawal, “Heterogeneous feature fusion based machine learning on shallow-wide and heterogeneous-sparse industrial datasets,” in ICPR Workshop on Industrial Machine Learning (IML), 2021, pp. 566–577. [url] [bib]
@inproceedings{YWI21, author = {Yang, Zijiang and Watari, Tetsushi and Ichigozaki, Daisuke and Mitsutoshi, Akita and Takahashi, Hiroaki and Suga, Yoshinori and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Heterogeneous feature fusion based machine learning on shallow-wide and heterogeneous-sparse industrial datasets}, booktitle = {ICPR Workshop on Industrial Machine Learning (IML)}, pages = {566-577}, year = {2021} }A. Agrawal, K. Gopalakrishnan, and A. Choudhary, “Materials Image Informatics Using Deep Learning,” in Handbook on Big Data and Machine Learning in the Physical Sciences, vol. 1: Big Data Methods in Experimental Materials Discovery, World Scientific, 2020, pp. 205–230. [url] [bib]
@inbook{AGC20, author = {Agrawal, Ankit and Gopalakrishnan, Kasthurirangan and Choudhary, Alok}, booktitle = {Handbook on Big Data and Machine Learning in the Physical Sciences}, title = {Materials Image Informatics Using Deep Learning}, volume = {1: Big Data Methods in Experimental Materials Discovery}, publisher = {World Scientific}, year = {2020}, series = {World Scientific Series on Emerging Technologies}, pages = {205-230} }K. Choudhary, K. F. Garrity, A. C. E. Reid, B. DeCost, A. J. Biacchi, A. R. H. Walker, Z. Trautt, J. Hattrick-Simpers, A. G. Kusne, A. Centrone, A. Davydov, J. Jiang, R. Pachter, G. Cheon, E. Reed, A. Agrawal, X. Qian, V. Sharma, H. Zhuang, S. V. Kalinin, B. G. Sumpter, G. Pilania, P. Acar, S. Mandal, K. Haule, D. Vanderbilt, K. Rabe, and F. Tavazza, “The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design,” npj Computational Materials, vol. 6, no. 173, 2020. [url] [bib]
@article{CGR20, author = {Choudhary, Kamal and Garrity, Kevin F. and Reid, Andrew C. E. and DeCost, Brian and Biacchi, Adam J. and Walker, Angela R. Hight and Trautt, Zachary and Hattrick-Simpers, Jason and Kusne, A. Gilad and Centrone, Andrea and Davydov, Albert and Jiang, Jie and Pachter, Ruth and Cheon, Gowoon and Reed, Evan and Agrawal, Ankit and Qian, Xiaofeng and Sharma, Vinit and Zhuang, Houlong and Kalinin, Sergei V. and Sumpter, Bobby G. and Pilania, Ghanshyam and Acar, Pinar and Mandal, Subhasish and Haule, Kristjan and Vanderbilt, David and Rabe, Karin and Tavazza, Francesca}, title = {The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design}, journal = {npj Computational Materials}, year = {2020}, volume = {6}, number = {173} }Z. Yang, D. Jha, A. Paul, W. Liao, A. Choudhary, and A. Agrawal, “A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design,” in Proceedings of 2020 NeurIPS workshop on Machine Learning for Engineering Modeling, Simulation, and Design (ML4Eng), 2020, pp. 1–8. [url] [bib]
@inproceedings{YJP20, author = {Yang, Zijiang and Jha, Dipendra and Paul, Arindam and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design}, booktitle = {Proceedings of 2020 NeurIPS workshop on Machine Learning for Engineering Modeling, Simulation, and Design (ML4Eng)}, pages = {1-8}, year = {2020} }Z. Yang, S. Papanikolaou, A. C. E. Reid, W. Liao, A. N. Choudhary, C. Campbell, and A. Agrawal, “Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations,” Scientific Reports, vol. 10, no. 8262, 2020. [url] [bib]
@article{YPR20, author = {Yang, Zijiang and Papanikolaou, Stefanos and Reid, Andrew C. E. and Liao, W. and Choudhary, Alok N. and Campbell, Carelyn and Agrawal, Ankit}, title = {Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations}, journal = {Scientific Reports}, year = {2020}, volume = {10}, number = {8262} }A. Agrawal and A. Choudhary, “Deep materials informatics: Applications of deep learning in materials science,” MRS Communications, vol. 9, no. 3, pp. 779–792, 2019. [url] [bib]
@article{AC19, author = {Agrawal, Ankit and Choudhary, Alok}, title = {Deep materials informatics: Applications of deep learning in materials science}, journal = {MRS Communications}, year = {2019}, volume = {9}, number = {3}, pages = {779-792} }A. Agrawal, A. Saboo, W. Xiong, G. Olson, and A. Choudhary, “Martensite Start Temperature Predictor for Steels Using Ensemble Data Mining,” in Proceedings of 6th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019, pp. 521–530. [url] [bib]
@inproceedings{ASX19, author = {Agrawal, Ankit and Saboo, Abhinav and Xiong, Wei and Olson, Greg and Choudhary, Alok}, title = {Martensite Start Temperature Predictor for Steels Using Ensemble Data Mining}, booktitle = {Proceedings of 6th IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, pages = {521-530}, year = {2019} }D. Jha, K. Choudhary, F. Tavazza, W. Liao, A. Choudhary, C. Campbell, and A. Agrawal, “Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning,” Nature Communications, vol. 10, no. 5316, 2019. [url] [bib]
@article{JCT19, author = {Jha, Dipendra and Choudhary, Kamal and Tavazza, Francesca and Liao, W. and Choudhary, Alok and Campbell, Carelyn and Agrawal, Ankit}, title = {Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning}, journal = {Nature Communications}, year = {2019}, volume = {10}, number = {5316} }D. Jha, A. G. Kusne, R. Al-Bahrani, N. Nguyen, W. Liao, A. Choudhary, and A. Agrawal, “Peak Area Detection Network for Directly Learning Phase Regions from Original X-ray Diffraction Patterns,” in Proceedings of International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1–8. [url] [bib]
@inproceedings{JKA19, author = {Jha, Dipendra and Kusne, Aaron Gilad and Al-Bahrani, Reda and Nguyen, Nam and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Peak Area Detection Network for Directly Learning Phase Regions from Original X-ray Diffraction Patterns}, booktitle = {Proceedings of International Joint Conference on Neural Networks (IJCNN)}, pages = {1-8}, year = {2019} }D. Jha, L. Ward, Z. Yang, C. Wolverton, I. Foster, W. Liao, A. Choudhary, and A. Agrawal, “IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery,” in Proceedings of 25th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2019, pp. 2385–2393. [url] [bib]
@inproceedings{JWY19, author = {Jha, Dipendra and Ward, Logan and Yang, Zijiang and Wolverton, Christopher and Foster, Ian and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery}, booktitle = {Proceedings of 25th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD)}, pages = {2385-2393}, year = {2019} }A. Paul, P. Acar, W. Liao, A. N. Choudhary, V. Sundararaghavan, and A. Agrawal, “Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation,” Computational Materials Science, vol. 160, pp. 334–351, 2019. [url] [bib]
@article{PAL19, author = {Paul, Arindam and Acar, Pinar and Liao, W. and Choudhary, Alok N. and Sundararaghavan, Veera and Agrawal, Ankit}, title = {Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation}, journal = {Computational Materials Science}, year = {2019}, volume = {160}, pages = {334-351} }A. Paul, A. Furmanchuk, W. Liao, A. Choudhary, and A. Agrawal, “Property Prediction of Organic Donor Molecules for Photovoltaic Applications using Extremely Randomized Trees,” Molecular Informatics, vol. 38, p. 1900038, 2019. [url] [bib]
@article{PFL19, author = {Paul, Arindam and Furmanchuk, Alona and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Property Prediction of Organic Donor Molecules for Photovoltaic Applications using Extremely Randomized Trees}, journal = {Molecular Informatics}, year = {2019}, volume = {38}, pages = {1900038} }A. Paul, D. Jha, R. Al-Bahrani, W. Liao, A. Choudhary, and A. Agrawal, “Transfer Learning Using Ensemble Neural Nets for Organic Solar Cell Screening,” in Proceedings of International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1–8. [url] [bib]
@inproceedings{PJA19, author = {Paul, Arindam and Jha, Dipendra and Al-Bahrani, Reda and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Transfer Learning Using Ensemble Neural Nets for Organic Solar Cell Screening}, booktitle = {Proceedings of International Joint Conference on Neural Networks (IJCNN)}, pages = {1-8}, year = {2019} }A. Paul, M. Mozaffar, Z. Yang, W. Liao, A. Choudhary, J. Cao, and A. Agrawal, “A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes,” in Proceedings of 6th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019, pp. 541–550. [url] [bib]
@inproceedings{PMY19, author = {Paul, Arindam and Mozaffar, Mojtaba and Yang, Zijiang and Liao, W. and Choudhary, Alok and Cao, Jian and Agrawal, Ankit}, title = {A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes}, booktitle = {Proceedings of 6th IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, pages = {541-550}, year = {2019} }Z. Yang, R. Al-Bahrani, A. Reid, S. Papanikolaou, S. Kalidindi, W. Liao, A. Choudhary, and A. Agrawal, “Deep learning based domain knowledge integration for small datasets: Illustrative applications in materials informatics,” in Proceedings of International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1–8. [url] [bib]
@inproceedings{YAR19, author = {Yang, Zijiang and Al-Bahrani, Reda and Reid, Andrew and Papanikolaou, Stefanos and Kalidindi, Surya and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Deep learning based domain knowledge integration for small datasets: Illustrative applications in materials informatics}, booktitle = {Proceedings of International Joint Conference on Neural Networks (IJCNN)}, pages = {1-8}, year = {2019} }Z. Yang, T. Watari, D. Ichigozaki, K. Morohoshi, Y. Suga, W. Liao, A. Choudhary, and A. Agrawal, “Data-driven insights from predictive analytics on heterogeneous experimental data of industrial magnetic materials,” in Proceedings of 2019 ICDM Workshop on Learning and Mining with Industrial Data (LMID), 2019, pp. 806–813. [url] [bib]
@inproceedings{YWI19, author = {Yang, Zijiang and Watari, Tetsushi and Ichigozaki, Daisuke and Morohoshi, Kei and Suga, Yoshinori and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Data-driven insights from predictive analytics on heterogeneous experimental data of industrial magnetic materials}, booktitle = {Proceedings of 2019 ICDM Workshop on Learning and Mining with Industrial Data (LMID)}, pages = {806-813}, year = {2019} }Z. Yang, Y. C. Yabansu, D. Jha, W. Liao, A. N. Choudhary, S. R. Kalidindi, and A. Agrawal, “Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches,” Acta Materialia, vol. 166, pp. 335–345, 2019. [url] [bib]
@article{YYJ19, author = {Yang, Zijiang and Yabansu, Yuksel C. and Jha, Dipendra and Liao, W. and Choudhary, Alok N. and Kalidindi, Surya R. and Agrawal, Ankit}, title = {Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches}, journal = {Acta Materialia}, year = {2019}, volume = {166}, pages = {335-345} }A. Agrawal and A. Choudhary, “An online tool for predicting fatigue strength of steel alloys based on ensemble data mining,” International Journal of Fatigue, vol. 113, pp. 389–400, 2018. [url] [bib]
@article{AC18, author = {Agrawal, Ankit and Choudhary, Alok}, title = {An online tool for predicting fatigue strength of steel alloys based on ensemble data mining}, journal = {International Journal of Fatigue}, year = {2018}, volume = {113}, pages = {389-400} }A. Furmanchuk, J. E. Saal, J. W. Doak, G. B. Olson, A. Choudhary, and A. Agrawal, “Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach,” Journal of Computational Chemistry, vol. 39, no. 4, pp. 191–202, 2018. [url] [bib]
@article{FSD18, author = {Furmanchuk, Al'ona and Saal, James E. and Doak, Jeff W. and Olson, Gregory B. and Choudhary, Alok and Agrawal, Ankit}, title = {Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach}, journal = {Journal of Computational Chemistry}, volume = {39}, number = {4}, issn = {1096-987X}, pages = {191--202}, keywords = {thermoelectric properties, nonstoichiometric materials, data mining, prediction, Seebeck coefficient, web application}, year = {2018} }K. Gopalakrishnan, H. Gholami, A. Vidyadharan, A. Choudhary, and A. Agrawal, “Crack Damage Detection in Unmanned Aerial Vehicle Images of Civil Infrastructure Using Pre-trained Deep Learning Model,” International Journal for Traffic and Transport Engineering, vol. 8, p. 1, 2018. [url] [bib]
@article{GGV18, author = {Gopalakrishnan, Kasthurirangan and Gholami, Hoda and Vidyadharan, Akash and Choudhary, Alok and Agrawal, Ankit}, title = {Crack Damage Detection in Unmanned Aerial Vehicle Images of Civil Infrastructure Using Pre-trained Deep Learning Model}, journal = {International Journal for Traffic and Transport Engineering}, year = {2018}, volume = {8}, pages = {1} }D. Jha, S. Singh, R. Al-Bahrani, W. Liao, A. N. Choudhary, M. D. Graef, and A. Agrawal, “Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials using Convolutional Neural Networks,” Microscopy and Microanalysis, vol. 24, no. 5, pp. 497–502, 2018. [url] [bib]
@article{JSA18, author = {Jha, Dipendra and Singh, Saransh and Al-Bahrani, Reda and Liao, W. and Choudhary, Alok N. and Graef, Marc De and Agrawal, Ankit}, title = {Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials using Convolutional Neural Networks}, journal = {Microscopy and Microanalysis}, year = {2018}, volume = {24}, number = {5}, pages = {497-502} }D. Jha, L. Ward, A. Paul, W. Liao, A. Choudhary, C. Wolverton, and A. Agrawal, “ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition,” Scientific Reports, vol. 8, no. 17593, 2018. [url] [bib]
@article{JWP18, author = {Jha, Dipendra and Ward, Logan and Paul, Arindam and Liao, W. and Choudhary, Alok and Wolverton, Chris and Agrawal, Ankit}, title = {ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition}, journal = {Scientific Reports}, year = {2018}, volume = {8}, number = {17593} }K. Kim, L. Ward, J. He, A. Krishna, A. Agrawal, P. Voorhees, and C. Wolverton, “Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds,” Physical Review Materials, vol. 2, no. 123801, 2018. [url] [bib]
@article{KWH18, author = {Kim, Kyoungdoc and Ward, Logan and He, Jiangang and Krishna, Amar and Agrawal, Ankit and Voorhees, Peter and Wolverton, Christopher}, title = {Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds}, journal = {Physical Review Materials}, year = {2018}, volume = {2}, number = {123801} }X. Li, Z. Yang, L. C. Brinson, A. Choudhary, A. Agrawal, and W. Chen, “A Deep Adversarial Learning Methodology for Designing Microstructural Material Systems,” in Proceedings of the ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC 2018), 2018, p. V02BT03A008. [url] [bib]
@inproceedings{LYB18, author = {Li, Xiaolin and Yang, Zijiang and Brinson, L Catherine and Choudhary, Alok and Agrawal, Ankit and Chen, Wei}, title = {A Deep Adversarial Learning Methodology for Designing Microstructural Material Systems}, booktitle = {Proceedings of the ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC 2018)}, pages = {V02BT03A008}, year = {2018} }M. Mozaffar, A. Paul, R. Al-Bahrani, S. Wolff, A. Choudhary, A. Agrawal, K. Ehmann, and J. Cao, “Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks,” Manufacturing Letters, vol. 18, pp. 35–39, 2018. [url] [bib]
@article{MPA18, author = {Mozaffar, Mojtaba and Paul, Arindam and Al-Bahrani, Reda and Wolff, Sarah and Choudhary, Alok and Agrawal, Ankit and Ehmann, Kornel and Cao, Jian}, title = {Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks}, journal = {Manufacturing Letters}, year = {2018}, volume = {18}, pages = {35-39} }A. Paul, P. Acar, R. Liu, W. Liao, A. Choudhary, V. Sundararaghavan, and A. Agrawal, “Data Sampling Schemes for Microstructure Design with Vibrational Tuning Constraints,” American Institute of Aeronautics and Astronautics (AIAA) Journal, vol. 56, no. 3, pp. 1239–1250, 2018. [url] [bib]
@article{PAL18, author = {Paul, Arindam and Acar, Pinar and Liu, Ruoqian and Liao, W. and Choudhary, Alok and Sundararaghavan, Veera and Agrawal, Ankit}, title = {Data Sampling Schemes for Microstructure Design with Vibrational Tuning Constraints}, journal = {American Institute of Aeronautics and Astronautics (AIAA) Journal}, year = {2018}, volume = {56}, number = {3}, pages = {1239-1250} }A. Paul, D. Jha, R. Al-Bahrani, W. Liao, A. Choudhary, and A. Agrawal, “CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations,” in Proceedings of 2018 NIPS workshop on Machine Learning for Molecules and Materials (MLMM), 2018, pp. 1–13. [url] [bib]
@inproceedings{PJA18, author = {Paul, Arindam and Jha, Dipendra and Al-Bahrani, Reda and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations}, booktitle = {Proceedings of 2018 NIPS workshop on Machine Learning for Molecules and Materials (MLMM)}, pages = {1-13}, year = {2018} }Z. Yang, X. Li, L. C. Brinson, A. Choudhary, W. Chen, and A. Agrawal, “Microstructural Materials Design via Deep Adversarial Learning Methodology,” Journal of Mechanical Design, vol. 140, no. 11, p. 10, 2018. [url] [bib]
@article{YLB18, author = {Yang, Zijiang and Li, Xiaolin and Brinson, L Catherine and Choudhary, Alok and Chen, Wei and Agrawal, Ankit}, title = {Microstructural Materials Design via Deep Adversarial Learning Methodology}, journal = {Journal of Mechanical Design}, year = {2018}, volume = {140}, number = {11}, pages = {10} }Z. Yang, Y. C. Yabansu, R. Al-Bahrani, W. Liao, A. N. Choudhary, S. R. Kalidindi, and A. Agrawal, “Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets,” Computational Materials Science, vol. 151, pp. 278–287, 2018. [url] [bib]
@article{YYB18, author = {Yang, Zijiang and Yabansu, Yuksel C. and Al-Bahrani, Reda and Liao, W. and Choudhary, Alok N. and Kalidindi, Surya R. and Agrawal, Ankit}, title = {Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets}, journal = {Computational Materials Science}, year = {2018}, volume = {151}, pages = {278-287} }K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, and A. Agrawal, “Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection,” Construction and Building Materials, vol. 157, pp. 322–330, 2017. [url] [bib]
@article{GKC17, author = {Gopalakrishnan, Kasthurirangan and Khaitan, Siddhartha K. and Choudhary, Alok and Agrawal, Ankit}, title = {Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection}, journal = {Construction and Building Materials}, year = {2017}, volume = {157}, pages = {322-330} }A. G. Gagorik, B. Savoie, N. Jackson, A. Agrawal, A. Choudhary, M. A. Ratner, G. C. Schatz, and K. L. Kohlstedt, “Improved Scaling of Molecular Network Calculations: The Emergence of Molecular Domains,” The Journal of Physical Chemistry Letters, vol. 8, no. 2, pp. 415–421, 2017. [url] [bib]
@article{GSJ17, author = {Gagorik, Adam G. and Savoie, Brett and Jackson, Nick and Agrawal, Ankit and Choudhary, Alok and Ratner, Mark A. and Schatz, George C. and Kohlstedt, Kevin L.}, title = {Improved Scaling of Molecular Network Calculations: The Emergence of Molecular Domains}, journal = {The Journal of Physical Chemistry Letters}, year = {2017}, pages = {415-421}, volume = {8}, number = {2} }R. Liu, Y. C. Yabansu, Z. Yang, A. N. Choudhary, S. R. Kalidindi, and A. Agrawal, “Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures,” Integrating Materials and Manufacturing Innovation, vol. 6, no. 2, pp. 160–171, 2017. [url] [bib]
@article{LYY17, author = {Liu, Ruoqian and Yabansu, Yuksel C. and Yang, Zijiang and Choudhary, Alok N. and Kalidindi, Surya R. and Agrawal, Ankit}, title = {Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures}, journal = {Integrating Materials and Manufacturing Innovation}, year = {2017}, pages = {160-171}, volume = {6}, number = {2} }L. Ward, R. Liu, A. Krishna, V. I. Hegde, A. Agrawal, A. Choudhary, and C. Wolverton, “Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations,” Physical Review B, vol. 96, no. 2, p. 024104, 2017. [url] [bib]
@article{WLK17, title = {Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations}, author = {Ward, Logan and Liu, Ruoqian and Krishna, Amar and Hegde, Vinay I and Agrawal, Ankit and Choudhary, Alok and Wolverton, Chris}, journal = {Physical Review B}, volume = {96}, number = {2}, pages = {024104}, year = {2017}, publisher = {American Physical Society} }A. Agrawal and A. Choudhary, “Perspective: Materials informatics and big data: Realization of the ‘fourth paradigm’ of science in materials science,” APL Materials, vol. 4, no. 053208, pp. 1–10, 2016. [url] [bib]
@article{AC16APLMat, author = {Agrawal, Ankit and Choudhary, Alok}, title = {Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science}, journal = {APL Materials}, year = {2016}, pages = {1-10}, volume = {4}, number = {053208} }A. Agrawal and A. Choudhary, “A Fatigue Strength Predictor for Steels Using Ensemble Data Mining,” in Proceedings of 25th ACM International Conference on Information and Knowledge Management (CIKM) (Demo), 2016, pp. 2497–2500. [url] [bib]
@inproceedings{AC16cikm, author = {Agrawal, Ankit and Choudhary, Alok}, title = {A Fatigue Strength Predictor for Steels Using Ensemble Data Mining}, booktitle = {Proceedings of 25th ACM International Conference on Information and Knowledge Management (CIKM) (Demo)}, pages = {2497-2500}, year = {2016} }A. Agrawal, B. Meredig, C. Wolverton, and A. Choudhary, “A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining,” in Proceedings of IEEE International Conference on Data Mining (ICDM) (Demo), 2016, pp. 1276–1279. [url] [bib]
@inproceedings{AMW16icdm, author = {Agrawal, Ankit and Meredig, Bryce and Wolverton, Christopher and Choudhary, Alok}, title = {A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining}, booktitle = {Proceedings of IEEE International Conference on Data Mining (ICDM) (Demo)}, pages = {1276-1279}, year = {2016} }A. Furmanchuk, A. Agrawal, and A. Choudhary, “Predictive analytics for crystalline materials: Bulk modulus,” RSC Advances, vol. 6, no. 97, pp. 95246–95251, 2016. [url] [bib]
@article{FAC16, author = {Furmanchuk, Al'ona and Agrawal, Ankit and Choudhary, Alok}, title = {Predictive analytics for crystalline materials: Bulk modulus}, journal = {RSC Advances}, year = {2016}, volume = {6}, number = {97}, pages = {95246-95251} }R. Liu, A. Agrawal, W. Liao, M. D. Graef, and A. Choudhary, “Materials Discovery: Understanding Polycrystals from Large-Scale Electron Patterns,” in Proceedings of IEEE BigData Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), 2016, pp. 2261–2269. [url] [bib]
@inproceedings{LAL16, author = {Liu, Ruoqian and Agrawal, Ankit and Liao, W. and Graef, Marc De and Choudhary, Alok}, title = {Materials Discovery: Understanding Polycrystals from Large-Scale Electron Patterns}, booktitle = {Proceedings of IEEE BigData Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH)}, pages = {2261-2269}, year = {2016} }R. Liu, L. Ward, C. Wolverton, A. Agrawal, W. Liao, and A. Choudhary, “Deep Learning for Chemical Compound Stability Prediction,” in Proceedings of ACM SIGKDD Workshop on Large-scale Deep Learning for Data Mining (DL-KDD), 2016, pp. 1–7. [url] [bib]
@inproceedings{LWW16, author = {Liu, Ruoqian and Ward, Logan and Wolverton, Chris and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Deep Learning for Chemical Compound Stability Prediction}, booktitle = {Proceedings of ACM SIGKDD Workshop on Large-scale Deep Learning for Data Mining (DL-KDD)}, pages = {1-7}, year = {2016} }L. Ward, A. Agrawal, A. Choudhary, and C. Wolverton, “A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials,” npj Computational Materials, vol. 2, no. 16028, 2016. [url] [bib]
@article{WAC16, author = {Ward, Logan and Agrawal, Ankit and Choudhary, Alok and Wolverton, Chris}, title = {A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials}, journal = {npj Computational Materials}, year = {2016}, volume = {2}, number = {16028} }R. Liu, A. Agrawal, Z. Chen, W. Liao, and A. Choudhary, “Pruned Search: A Machine Learning Based Meta-Heuristic Approach for Constrained Continuous Optimization,” in Proceedings of 8th IEEE International Conference on Contemporary Computing (IC3), 2015, pp. 13–18. [url] [bib]
@inproceedings{LAC15b, author = {Liu, Ruoqian and Agrawal, Ankit and Chen, Zhengzhang and Liao, W. and Choudhary, Alok}, title = {Pruned Search: A Machine Learning Based Meta-Heuristic Approach for Constrained Continuous Optimization}, booktitle = {Proceedings of 8th IEEE International Conference on Contemporary Computing (IC3)}, pages = {13-18}, year = {2015} }R. Liu, A. Kumar, Z. Chen, A. Agrawal, V. Sundararaghavan, and A. Choudhary, “A Predictive Machine Learning Approach for Microstructure Optimization and Materials Design,” Scientific Reports, vol. 5, no. 11551, 2015. [url] [bib]
@article{LKC15, author = {Liu, Ruoqian and Kumar, Abhishek and Chen, Zhengzhang and Agrawal, Ankit and Sundararaghavan, Veera and Choudhary, Alok}, title = {A Predictive Machine Learning Approach for Microstructure Optimization and Materials Design}, journal = {Scientific Reports}, year = {2015}, volume = {5}, number = {11551} }R. Liu, Y. C. Yabansu, A. Agrawal, S. R. Kalidindi, and A. N. Choudhary, “Machine learning approaches for elastic localization linkages in high-contrast composite materials,” Integrating Materials and Manufacturing Innovation, vol. 4, no. 13, pp. 1–17, 2015. [url] [bib]
@article{LYA15, author = {Liu, Ruoqian and Yabansu, Yuksel C. and Agrawal, Ankit and Kalidindi, Surya R. and Choudhary, Alok N.}, title = {Machine learning approaches for elastic localization linkages in high-contrast composite materials}, journal = {Integrating Materials and Manufacturing Innovation}, year = {2015}, pages = {1-17}, volume = {4}, number = {13} }A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary, and S. R. Kalidindi, “Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters,” Integrating Materials and Manufacturing Innovation, vol. 3, no. 8, pp. 1–19, 2014. [url] [bib]
@article{ADC14, author = {Agrawal, Ankit and Deshpande, Parijat D and Cecen, Ahmet and Basavarsu, Gautham P and Choudhary, Alok N and Kalidindi, Surya R}, title = {Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters}, journal = {Integrating Materials and Manufacturing Innovation}, year = {2014}, pages = {1-19}, volume = {3}, number = {8} }R. Liu, A. Agrawal, W. Liao, and A. Choudhary, “Search Space Preprocessing in Solving Complex Optimization Problems,” in Proceedings of IEEE BigData Workshop on Complexity for Big Data (C4BD), 2014, pp. 1–5. [url] [bib]
@inproceedings{LAL14b, author = {Liu, Ruoqian and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Search Space Preprocessing in Solving Complex Optimization Problems}, booktitle = {Proceedings of IEEE BigData Workshop on Complexity for Big Data (C4BD)}, pages = {1-5}, year = {2014} }B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, “Combinatorial screening for new materials in unconstrained composition space with machine learning,” Physical Review B, vol. 89, no. 094104, pp. 1–7, 2014. BM and AA are co-first authors. [url] [bib]
@article{MAK14, author = {Meredig, Bryce and Agrawal, Ankit and Kirklin, S and Saal, J E and Doak, J W and Thompson, A and Zhang, Kunpeng and Choudhary, Alok and Wolverton, Christopher}, title = {Combinatorial screening for new materials in unconstrained composition space with machine learning}, journal = {Physical Review B}, year = {2014}, number = {094104}, volume = {89}, pages = {1-7}, note = {**BM and AA are co-first authors**} }P. D. Deshpande, B. P. Gautham, A. Cecen, S. Kalidindi, A. Agrawal, and A. Choudhary, “Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels,” in 2nd World Congress on Integrated Computational Materials Engineering, July 7-11, 2013, Salt Lake City, Utah, 2013, pp. 155–160. [url] [bib]
@inproceedings{DGC13, author = {Deshpande, PD and Gautham, BP and Cecen, A and Kalidindi, S and Agrawal, Ankit and Choudhary, A}, title = {Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels}, year = {2013}, pages = {155-160}, booktitle = {2nd World Congress on Integrated Computational Materials Engineering, July 7-11, 2013, Salt Lake City, Utah} }K. Gopalakrishnan, A. Agrawal, H. Ceylan, S. Kim, and A. Choudhary, “Knowledge discovery and data mining in pavement inverse analysis,” Transport, vol. 28, no. 1, pp. 1–10, 2013. [url] [bib]
@article{GAC13, title = {Knowledge discovery and data mining in pavement inverse analysis}, author = {Gopalakrishnan, Kasthurirangan and Agrawal, Ankit and Ceylan, Halil and Kim, Sunghwan and Choudhary, Alok}, journal = {Transport}, volume = {28}, number = {1}, pages = {1--10}, year = {2013}, publisher = {Taylor & Francis Group} }
High Performance Data Mining
C. Lee, V. Hewes, G. Cerati, K. Wang, A. Aurisano, A. Agrawal, A. Choudhary, and W. Liao, “Addressing GPU Memory Limitations for Graph Neural Networks in High-Energy Physics Applications,” Frontiers in High Performance Computing, vol. 2, p. 1458674, 2024. [url] [bib]
@article{LHC24, title = {Addressing GPU Memory Limitations for Graph Neural Networks in High-Energy Physics Applications}, author = {Lee, Claire and Hewes, V and Cerati, Giuseppe and Wang, Kewei and Aurisano, Adam and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, journal = {Frontiers in High Performance Computing}, volume = {2}, pages = {1458674}, year = {2024}, publisher = {Frontiers} }V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data,” in Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA), 2024. To appear. [bib]
@inproceedings{GLC24, author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data}, booktitle = {Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA)}, pages = {}, year = {2024}, note = {To appear} }M. N. T. Kilic, V. Gupta, Y. Mao, K. Wang, A. Peltekian, A. Choudhary, W. Liao, and A. Agrawal, “Enhancing Deep Neural Network Classification Performance through Novel Weight Initialization: t-SNE Supported Walsh Matrix Approach,” in Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA), 2024. To appear. [bib]
@inproceedings{KGM24, author = {Kilic, Muhammed Nur Talha and Gupta, Vishu and Mao, Yuwei and Wang, Kewei and Peltekian, Alec and Choudhary, Alok and Liao, W. and Agrawal, Ankit}, title = {Enhancing Deep Neural Network Classification Performance through Novel Weight Initialization: t-SNE Supported Walsh Matrix Approach}, booktitle = {Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA)}, pages = {}, year = {2024}, note = {To appear} }M. N. T. Kilic, Y. Mao, V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Tackling the Nonlinearity Problem in Inverse Modeling: Mixture Density Network-Backed Quantized AutoEncoder,” in Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA), 2024. To appear. [bib]
@inproceedings{KMG24, author = {Kilic, Muhammed Nur Talha and Mao, Yuwei and Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Tackling the Nonlinearity Problem in Inverse Modeling: Mixture Density Network-Backed Quantized AutoEncoder}, booktitle = {Proceedings of 23rd IEEE International Conference on Machine Learning and Applications (ICMLA)}, pages = {}, year = {2024}, note = {To appear} }Z. Huang, K. Hou, A. Agrawal, A. Choudhary, R. Ross, and W. Liao, “I/O in WRF: A Case Study in Modern Parallel I/O Techniques,” in Proceedings of 35th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC’23), 2023, pp. 1–13. [url] [bib]
@inproceedings{HHA23, author = {Huang, Zanhua and Hou, Kaiyuan and Agrawal, Ankit and Choudhary, Alok and Ross, Rob and Liao, W.}, title = {I/O in WRF: A Case Study in Modern Parallel I/O Techniques}, booktitle = {Proceedings of 35th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC'23)}, pages = {1-13}, year = {2023}, articleno = {94}, publisher = {Association for Computing Machinery} }C. Lee, V. Hewes, G. Cerati, J. Kowalkowski, A. Aurisano, A. Agrawal, A. Choudhary, and W. Liao, “A Case Study of Data Management Challenges Presented in Large-Scale Machine Learning Workflows,” in Proceedings of 23rd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2023, pp. 71–81. [url] [bib]
@inproceedings{LHC23, author = {Lee, Claire and Hewes, V and Cerati, Giuseppe and Kowalkowski, Jim and Aurisano, Adam and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {A Case Study of Data Management Challenges Presented in Large-Scale Machine Learning Workflows}, booktitle = {Proceedings of 23rd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)}, pages = {71-81}, year = {2023} }V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Which Deep Learning Framework Should I Use: A Comparative Study for Deep Regression Modeling,” in Proceedings of 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022, pp. 72–77. [url] [bib]
@inproceedings{GLC22b, author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Which Deep Learning Framework Should I Use: A Comparative Study for Deep Regression Modeling}, booktitle = {Proceedings of 2022 International Conference on Computational Science and Computational Intelligence (CSCI)}, pages = {72-77}, year = {2022} }S. Lee, K.-yuan Hou, K. Wang, S. Sehrish, M. Paterno, J. Kowalkowski, Q. Koziol, R. B. Ross, A. Agrawal, A. Choudhary, and W. Liao, “A case study on parallel HDF5 dataset concatenation for high energy physics data analysis,” Parallel Computing, vol. 110, p. 102877, 2022. [url] [bib]
@article{LHW22, author = {Lee, Sunwoo and Hou, Kai-yuan and Wang, Kewei and Sehrish, Saba and Paterno, Marc and Kowalkowski, James and Koziol, Quincey and Ross, Robert B. and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {A case study on parallel HDF5 dataset concatenation for high energy physics data analysis}, journal = {Parallel Computing}, year = {2022}, volume = {110}, pages = {102877} }S. Lee, Q. Kang, R. Al-Bahrani, A. Agrawal, A. Choudhary, and W. Liao, “Improving scalability of parallel CNN training by adaptively adjusting parameter update frequency,” Journal of Parallel and Distributed Computing, vol. 159, pp. 10–23, 2022. [url] [bib]
@article{LKA22, author = {Lee, Sunwoo and Kang, Qiao and Al-Bahrani, Reda and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Improving scalability of parallel CNN training by adaptively adjusting parameter update frequency}, journal = {Journal of Parallel and Distributed Computing}, year = {2022}, volume = {159}, pages = {10-23} }Y. Mao, V. Gupta, K. Wang, W. Liao, A. Choudhary, and A. Agrawal, “To Shuffle or Not to Shuffle: Mini-Batch Shuffling Strategies for Multi-class Imbalanced Data Classification,” in Proceedings of 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022, pp. 298–301. [url] [bib]
@inproceedings{MGW22, author = {Mao, Yuwei and Gupta, Vishu and Wang, Kewei and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {To Shuffle or Not to Shuffle: Mini-Batch Shuffling Strategies for Multi-class Imbalanced Data Classification}, booktitle = {Proceedings of 2022 International Conference on Computational Science and Computational Intelligence (CSCI)}, pages = {298-301}, year = {2022} }K. Wang, S. Lee, J. Balewski, A. Sim, P. Nugent, A. Agrawal, A. Choudhary, K. Wu, and W. Liao, “Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications,” in Proceedings of 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2022, pp. 404–413. [url] [bib]
@inproceedings{WLB22, author = {Wang, Kewei and Lee, Sunwoo and Balewski, Jan and Sim, Alex and Nugent, Peter and Agrawal, Ankit and Choudhary, Alok and Wu, Kesheng and Liao, W.}, title = {Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications}, booktitle = {Proceedings of 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)}, pages = {404-413}, year = {2022} }R. Al-Bahrani, D. Jha, Q. Kang, S. Lee, Z. Yang, W. Liao, A. Agrawal, and A. Choudhary, “SIGRNN: Synthetic minority Instances Generation in imbalanced datasets using a Recurrent Neural Network,” in Proceedings of International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2021, pp. 349–356. [url] [bib]
@inproceedings{BJK21, author = {Al-Bahrani, Reda and Jha, Dipendra and Kang, Qiao and Lee, Sunwoo and Yang, Zijiang and Liao, W. and Agrawal, Ankit and Choudhary, Alok}, title = {SIGRNN: Synthetic minority Instances Generation in imbalanced datasets using a Recurrent Neural Network}, booktitle = {Proceedings of International Conference on Pattern Recognition Applications and Methods (ICPRAM)}, pages = {349-356}, year = {2021} }K. Hou, Q. Kang, S. Lee, A. Agrawal, A. Choudhary, and W. Liao, “Supporting Data Compression in PnetCDF,” in Proceedings of 2021 IEEE International Conference on Big Data (BigData), 2021, pp. 86–97. [url] [bib]
@inproceedings{HKL21, author = {Hou, Kaiyuan and Kang, Qiao and Lee, Sunwoo and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Supporting Data Compression in PnetCDF}, booktitle = {Proceedings of 2021 IEEE International Conference on Big Data (BigData)}, pages = {86-97}, year = {2021} }D. Jha, V. Gupta, L. Ward, Z. Yang, C. Wolverton, I. Foster, W. Liao, A. N. Choudhary, and A. Agrawal, “Enabling Deeper Learning on Big Data for Materials Informatics Applications,” Scientific Reports, vol. 11, no. 4244, 2021. [url] [bib]
@article{JGW21, author = {Jha, Dipendra and Gupta, Vishu and Ward, Logan and Yang, Zijiang and Wolverton, Christopher and Foster, Ian and Liao, W. and Choudhary, Alok N. and Agrawal, Ankit}, title = {Enabling Deeper Learning on Big Data for Materials Informatics Applications}, journal = {Scientific Reports}, year = {2021}, volume = {11}, number = {4244} }S. Lee, Q. Kang, K. Wang, J. Balewski, A. Sim, A. Agrawal, A. Choudhary, P. Nugent, K. Wu, and W. Liao, “Asynchronous I/O Strategy for Large-Scale Deep Learning Applications,” in Proceedings of 28th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC’21), 2021, pp. 322–331. [url] [bib]
@inproceedings{LKW21, author = {Lee, Sunwoo and Kang, Qiao and Wang, Kewei and Balewski, Jan and Sim, Alex and Agrawal, Ankit and Choudhary, Alok and Nugent, Peter and Wu, Kesheng and Liao, W.}, title = {Asynchronous I/O Strategy for Large-Scale Deep Learning Applications}, booktitle = {Proceedings of 28th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC'21)}, pages = {322-331}, year = {2021} }Q. Kang, S. Lee, K. Hou, R. Ross, A. Agrawal, A. Choudhary, and W. Liao, “Improving MPI Collective I/O for High Volume Non-contiguous Requests With Intra-node Aggregation,” IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 11, pp. 2682–2695, 2020. [url] [bib]
@article{KLH20, author = {Kang, Qiao and Lee, Sunwoo and Hou, Kaiyuan and Ross, Rob and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Improving MPI Collective I/O for High Volume Non-contiguous Requests With Intra-node Aggregation}, journal = {IEEE Transactions on Parallel and Distributed Systems}, year = {2020}, volume = {31}, number = {11}, pages = {2682-2695} }S. Lee, Q. Kang, A. Agrawal, A. Choudhary, and W. Liao, “Communication-Efficient Local Stochastic Gradient Descent for Scalable Deep Learning,” in Proceedings of 2020 IEEE International Conference on Big Data (BigData), 2020, pp. 718–727. [url] [bib]
@inproceedings{LKA20, author = {Lee, Sunwoo and Kang, Qiao and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Communication-Efficient Local Stochastic Gradient Descent for Scalable Deep Learning}, booktitle = {Proceedings of 2020 IEEE International Conference on Big Data (BigData)}, pages = {718-727}, year = {2020} }Q. Kang, R. Ross, R. Latham, S. Lee, A. Agrawal, A. Choudhary, and W. Liao, “Improving all-to-many personalized communication in two-phase I/O,” in Proceedings of 32nd International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC’20), 2020, pp. 118–130. [url] [bib]
@inproceedings{KRL20, author = {Kang, Qiao and Ross, Robert and Latham, Robert and Lee, Sunwoo and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Improving all-to-many personalized communication in two-phase I/O}, booktitle = {Proceedings of 32nd International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC'20)}, pages = {118-130}, year = {2020} }Q. Kang, A. Agrawal, A. Choudhary, A. Sim, K. Wu, R. Kettimuthu, P. Beckman, Z. Liu, and W. Liao, “Spatiotemporal Real-Time Anomaly Detection for Supercomputing Systems,” in Proceedings of IEEE BigData Workshop on Big Data Predictive Maintenance using Artificial Intelligence, 2019, pp. 4381–4389. [url] [bib]
@inproceedings{KAC19, author = {Kang, Qiao and Agrawal, Ankit and Choudhary, Alok and Sim, Alex and Wu, Kesheng and Kettimuthu, Rajkumar and Beckman, Peter and Liu, Zhengchun and Liao, W.}, title = {Spatiotemporal Real-Time Anomaly Detection for Supercomputing Systems}, booktitle = {Proceedings of IEEE BigData Workshop on Big Data Predictive Maintenance using Artificial Intelligence}, pages = {4381-4389}, year = {2019} }Q. Kang, J. L. Träff, R. Al-Bahrani, A. Agrawal, A. Choudhary, and W. Liao, “Scalable Algorithms for MPI Intergroup Allgather and Allgatherv,” Parallel Computing, vol. 85, pp. 220–230, 2019. [url] [bib]
@article{KTA19, author = {Kang, Qiao and Träff, Jesper Larsson and Al-Bahrani, Reda and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Scalable Algorithms for MPI Intergroup Allgather and Allgatherv}, journal = {Parallel Computing}, year = {2019}, volume = {85}, pages = {220-230} }S. Lee, Q. Kang, S. Madireddy, P. Balaprakash, A. Agrawal, A. Choudhary, R. Archibald, and W. Liao, “Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time,” in Proceedings of 2019 IEEE International Conference on Big Data (BigData), 2019, pp. 830–839. [url] [bib]
@inproceedings{LKM19, author = {Lee, Sunwoo and Kang, Qiao and Madireddy, Sandeep and Balaprakash, Prasanna and Agrawal, Ankit and Choudhary, Alok and Archibald, Richard and Liao, W.}, title = {Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time}, booktitle = {Proceedings of 2019 IEEE International Conference on Big Data (BigData)}, pages = {830-839}, year = {2019} }D. Han, A. Agrawal, W. Liao, and A. Choudhary, “A Fast DBSCAN Algorithm with Spark Implementation,” in Big Data in Engineering Applications, S. S. R. et al., Ed. Springer Nature Singapore, 2018, pp. 173–192. [url] [bib]
@inbook{HAL18a, author = {Han, Dianwei and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, editor = {et al., SS Roy}, booktitle = {Big Data in Engineering Applications}, title = {A Fast DBSCAN Algorithm with Spark Implementation}, publisher = {Springer Nature Singapore}, year = {2018}, series = {Studies in Big Data}, pages = {173-192} }D. Han, A. Agrawal, W. Liao, and A. Choudhary, “Parallel DBSCAN Algorithm Using a Data Partitioning Strategy with Spark Implementation,” in Proceedings of 2018 IEEE Conference on Big Data (BigData), 2018, pp. 305–312. [url] [bib]
@inproceedings{HAL18b, author = {Han, Dianwei and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Parallel DBSCAN Algorithm Using a Data Partitioning Strategy with Spark Implementation}, booktitle = {Proceedings of 2018 IEEE Conference on Big Data (BigData)}, pages = {305-312}, year = {2018} }K. Hou, R. Al-Bahrani, E. Rangel, A. Agrawal, R. Latham, R. Ross, A. Choudhary, and W. Liao, “Integration of Burst Buffer in High-level Parallel I/O Library for Exa-scale Computing Era,” in Proceedings of SC workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems (PDSW-DISCS 2018), 2018, pp. 1–12. [url] [bib]
@inproceedings{HAR18, author = {Hou, Kaiyuan and Al-Bahrani, Reda and Rangel, Esteban and Agrawal, Ankit and Latham, Robert and Ross, Robert and Choudhary, Alok and Liao, W.}, title = {Integration of Burst Buffer in High-level Parallel I/O Library for Exa-scale Computing Era}, booktitle = {Proceedings of SC workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems (PDSW-DISCS 2018)}, pages = {1-12}, year = {2018} }Q. Kang, A. Agrawal, A. Choudhary, and W. Liao, “Optimal Algorithms for Half-Duplex Inter-Group All-to-All Broadcast on Fully Connected and Ring Topologies,” in Proceedings of 2018 SC Workshop on Exascale MPI (ExaMPI), 2018, pp. 1–11. [url] [bib]
@inproceedings{KAC18, author = {Kang, Qiao and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Optimal Algorithms for Half-Duplex Inter-Group All-to-All Broadcast on Fully Connected and Ring Topologies}, booktitle = {Proceedings of 2018 SC Workshop on Exascale MPI (ExaMPI)}, pages = {1-11}, year = {2018} }R. Kettimuthu, Z. Liu, I. Foster, P. Beckman, A. Sim, K. Wu, W. Liao, Q. Kang, A. Agrawal, and A. Choudhary, “Towards Autonomic Science Infrastructure: Architecture, Limitations, and Open Issues,” in Proceedings of the 1st International Workshop on Autonomous Infrastructure for Science, 2018. [url] [bib]
@inproceedings{KLF18, author = {Kettimuthu, Rajkumar and Liu, Zhengchun and Foster, Ian and Beckman, Peter and Sim, Alex and Wu, Kesheng and Liao, W. and Kang, Qiao and Agrawal, Ankit and Choudhary, Alok}, title = {Towards Autonomic Science Infrastructure: Architecture, Limitations, and Open Issues}, year = {2018}, booktitle = {Proceedings of the 1st International Workshop on Autonomous Infrastructure for Science}, articleno = {2}, numpages = {9} }Q. Kang, J. L. Träff, R. Al-Bahrani, A. Agrawal, A. Choudhary, and W. Liao, “Full-Duplex Inter-Group All-to-All Broadcast Algorithms with Optimal Bandwidth,” in Proceedings of the 25th European MPI Users’ Group Meeting (EuroMPI 2018), 2018, p. 10. [url] [bib]
@inproceedings{KTA18, author = {Kang, Qiao and Träff, Jesper Larsson and Al-Bahrani, Reda and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Full-Duplex Inter-Group All-to-All Broadcast Algorithms with Optimal Bandwidth}, booktitle = {Proceedings of the 25th European MPI Users' Group Meeting (EuroMPI 2018)}, pages = {10}, year = {2018} }S. Lee, A. Agrawal, P. Balaprakash, A. Choudhary, and W. Liao, “Communication-Efficient Parallelization Strategy for Deep Convolutional Neural Network Training,” in Proceedings of SC workshop on Machine Learning in HPC Environments (MLHPC 2018), 2018, pp. 47–56. [url] [bib]
@inproceedings{LAB18, author = {Lee, Sunwoo and Agrawal, Ankit and Balaprakash, Prasanna and Choudhary, Alok and Liao, W.}, title = {Communication-Efficient Parallelization Strategy for Deep Convolutional Neural Network Training}, booktitle = {Proceedings of SC workshop on Machine Learning in HPC Environments (MLHPC 2018)}, pages = {47-56}, year = {2018} }Q. Kang, W. Liao, A. Agrawal, and A. Choudhary, “A Hybrid Training Algorithm for Recurrent Neural Network Using Particle Swarm Optimization-based Preprocessing and Temporal Error Aggregation,” in IEEE ICDM Workshop on Optimization Based Techniques for Emerging Data Mining Problems (OEDM), 2017, pp. 812–817. [url] [bib]
@inproceedings{KLA17, author = {Kang, Qiao and Liao, W. and Agrawal, Ankit and Choudhary, Alok}, title = {A Hybrid Training Algorithm for Recurrent Neural Network Using Particle Swarm Optimization-based Preprocessing and Temporal Error Aggregation}, booktitle = {IEEE ICDM Workshop on Optimization Based Techniques for Emerging Data Mining Problems (OEDM)}, pages = {812-817}, year = {2017} }S. Lee, D. Jha, A. Agrawal, A. Choudhary, and W. Liao, “Parallel Deep Convolutional Neural Network Training by Exploiting the Overlapping of Computation and Communication,” in Proceedings of 24th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC’17), 2017, pp. 183–192. [url] [bib]
@inproceedings{LJA17, author = {Lee, Sunwoo and Jha, Dipendra and Agrawal, Ankit and Choudhary, Alok and Liao, W.}, title = {Parallel Deep Convolutional Neural Network Training by Exploiting the Overlapping of Computation and Communication}, booktitle = {Proceedings of 24th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC'17)}, pages = {183-192}, year = {2017} }Y. Xie, Z. Chen, D. Palsetia, G. Trajcevski, A. Agrawal, and A. Choudhary, “Silverback+: Scalable Association Mining Via Fast List Intersection For Columnar Social Data,” Knowledge and Information Systems (KAIS), vol. 50, no. 3, pp. 969–997, 2017. [url] [bib]
@article{XCP17, author = {Xie, Yusheng and Chen, Zhengzhang and Palsetia, Diana and Trajcevski, Goce and Agrawal, Ankit and Choudhary, Alok}, title = {Silverback+: Scalable Association Mining Via Fast List Intersection For Columnar Social Data}, journal = {Knowledge and Information Systems (KAIS)}, year = {2017}, volume = {50}, number = {3}, pages = {969–997} }D. Han, A. Agrawal, W. Liao, and A. Choudhary, “A novel scalable DBSCAN algorithm with Spark,” in Proceedings of 5th IEEE IPDPS Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning), 2016, pp. 1393–1402. [url] [bib]
@inproceedings{HAL16, author = {Han, Dianwei and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {A novel scalable DBSCAN algorithm with Spark}, booktitle = {Proceedings of 5th IEEE IPDPS Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning)}, pages = {1393-1402}, year = {2016} }Q. Kang, W. Liao, A. Agrawal, and A. Choudhary, “A Filtering-based Clustering Algorithm for Improving Spatio-temporal Kriging Interpolation Accuracy,” in Proceedings of 25th ACM International Conference on Information and Knowledge Management (CIKM), 2016, pp. 2209–2214. [url] [bib]
@inproceedings{KLA16, author = {Kang, Qiao and Liao, W. and Agrawal, Ankit and Choudhary, Alok}, title = {A Filtering-based Clustering Algorithm for Improving Spatio-temporal Kriging Interpolation Accuracy}, booktitle = {Proceedings of 25th ACM International Conference on Information and Knowledge Management (CIKM)}, pages = {2209-2214}, year = {2016} }S. Lee, W. Liao, A. Agrawal, N. Hardavellas, and A. Choudhary, “Evaluation of K-Means Data Clustering Algorithm on Intel Xeon Phi,” in Proceedings of IEEE BigData Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), 2016, pp. 2251–2260. [url] [bib]
@inproceedings{LLA16, author = {Lee, Sunwoo and Liao, W. and Agrawal, Ankit and Hardavellas, Nikos and Choudhary, Alok}, title = {Evaluation of K-Means Data Clustering Algorithm on Intel Xeon Phi}, booktitle = {Proceedings of IEEE BigData Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH)}, pages = {2251-2260}, year = {2016} }D. Palsetia, W. Hendrix, S. Lee, A. Agrawal, W. Liao, and A. Choudhary, “Parallel Community Detection Algorithm Using a Data Partitioning Strategy with Pairwise Subdomain Duplication,” in High Performance Computing, 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings, 2016, pp. 98–115. [url] [bib]
@inproceedings{PHL16, author = {Palsetia, Diana and Hendrix, William and Lee, Sunwoo and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Parallel Community Detection Algorithm Using a Data Partitioning Strategy with Pairwise Subdomain Duplication}, booktitle = {High Performance Computing, 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings}, pages = {98-115}, year = {2016} }E. Rangel, W. Hendrix, A. Agrawal, W. Liao, and A. Choudhary, “AGORAS: A Fast Algorithm for Estimating Medoids in Large Datasets,” Procedia Computer Science, vol. 80, pp. 1159–1169, 2016. [url] [bib]
@article{RHA16, author = {Rangel, Esteban and Hendrix, William and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {AGORAS: A Fast Algorithm for Estimating Medoids in Large Datasets}, journal = {Procedia Computer Science}, year = {2016}, pages = {1159–1169}, volume = {80} }Z. Yuan, W. Hendrix, S. W. Son, C. Federrath, A. Agrawal, W. Liao, and A. Choudhary, “Parallel Implementation of Lossy Data Compression for Temporal Data Sets,” in Proceedings of 23rd Annual International Conference on High Performance Computing, Data, and Analytics (HiPC’16), 2016, pp. 62–71. [url] [bib]
@inproceedings{YHS16, author = {Yuan, Zheng and Hendrix, William and Son, Seung Woo and Federrath, Christoph and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Parallel Implementation of Lossy Data Compression for Temporal Data Sets}, booktitle = {Proceedings of 23rd Annual International Conference on High Performance Computing, Data, and Analytics (HiPC'16)}, pages = {62-71}, year = {2016} }C. Jin, Z. Chen, W. Hendrix, A. Agrawal, and A. Choudhary, “Incremental, Distributed Single-Linkage Hierarchical Clustering Algorithm Using MapReduce,” in Proceedings of the 23rd High Performance Computing Symposium (HPC), 2015, pp. 83–92. [url] [bib]
@inproceedings{JCH15, author = {Jin, Chen and Chen, Zhengzhang and Hendrix, William and Agrawal, Ankit and Choudhary, Alok}, title = {Incremental, Distributed Single-Linkage Hierarchical Clustering Algorithm Using MapReduce}, booktitle = {Proceedings of the 23rd High Performance Computing Symposium (HPC)}, pages = {83-92}, year = {2015} }C. Jin, Q. Fu, H. Wang, W. Hendrix, Z. Chen, A. Agrawal, A. Banerjee, and A. Choudhary, “Running MAP Inference on Million Node Graphical Models: A High Performance Computing Perspective,” in Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2015, pp. 565–575. [url] [bib]
@inproceedings{JFW15, author = {Jin, Chen and Fu, Qiang and Wang, Huahua and Hendrix, William and Chen, Zhengzhang and Agrawal, Ankit and Banerjee, Arindam and Choudhary, Alok}, title = {Running MAP Inference on Million Node Graphical Models: A High Performance Computing Perspective}, booktitle = {Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)}, pages = {565-575}, year = {2015} }C. Jin, R. Liu, Z. Chen, W. Hendrix, A. Agrawal, and A. Choudhary, “A Scalable Hierarchical Clustering Algorithm Using Spark,” in Proceedings of IEEE International Conference on Big Data Computing Service and Applications (BigDataService), 2015, pp. 418–426. [url] [bib]
@inproceedings{JLC15, author = {Jin, Chen and Liu, Ruoqian and Chen, Zhengzhang and Hendrix, William and Agrawal, Ankit and Choudhary, Alok}, title = {A Scalable Hierarchical Clustering Algorithm Using Spark}, booktitle = {Proceedings of IEEE International Conference on Big Data Computing Service and Applications (BigDataService)}, pages = {418-426}, year = {2015} }R. Liu, A. Agrawal, Z. Chen, W. Liao, and A. Choudhary, “Pruned Search: A Machine Learning Based Meta-Heuristic Approach for Constrained Continuous Optimization,” in Proceedings of 8th IEEE International Conference on Contemporary Computing (IC3), 2015, pp. 13–18. [url] [bib]
@inproceedings{LAC15b, author = {Liu, Ruoqian and Agrawal, Ankit and Chen, Zhengzhang and Liao, W. and Choudhary, Alok}, title = {Pruned Search: A Machine Learning Based Meta-Heuristic Approach for Constrained Continuous Optimization}, booktitle = {Proceedings of 8th IEEE International Conference on Contemporary Computing (IC3)}, pages = {13-18}, year = {2015} }Z. Chen, S. W. Son, W. Hendrix, A. Agrawal, W. Liao, and A. Choudhary, “NUMARCK: Machine Learning Algorithm for Resiliency and Checkpointing,” in Proceedings of 26th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC’14), 2014, pp. 733–744. [url] [bib]
@inproceedings{CSH14, author = {Chen, Zhengzhang and Son, Seung Woo and Hendrix, William and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {NUMARCK: Machine Learning Algorithm for Resiliency and Checkpointing}, booktitle = {Proceedings of 26th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC'14)}, pages = {733-744}, year = {2014} }S. Gupta, D. Palsetia, M. M. A. Patwary, A. Agrawal, and A. Choudhary, “A New Parallel Algorithm for Two-Pass Connected Component Labeling,” in Proceedings of IEEE IPDPS Workshop on Multithreaded Architectures and Applications (MTAAP), 2014, pp. 1355–1362. [url] [bib]
@inproceedings{GPP14, author = {Gupta, Siddharth and Palsetia, Diana and Patwary, Md. Mostofa Ali and Agrawal, Ankit and Choudhary, Alok}, title = {A New Parallel Algorithm for Two-Pass Connected Component Labeling}, booktitle = {Proceedings of IEEE IPDPS Workshop on Multithreaded Architectures and Applications (MTAAP)}, pages = {1355-1362}, year = {2014} }R. Liu, A. Agrawal, W. Liao, and A. Choudhary, “Search Space Preprocessing in Solving Complex Optimization Problems,” in Proceedings of IEEE BigData Workshop on Complexity for Big Data (C4BD), 2014, pp. 1–5. [url] [bib]
@inproceedings{LAL14b, author = {Liu, Ruoqian and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Search Space Preprocessing in Solving Complex Optimization Problems}, booktitle = {Proceedings of IEEE BigData Workshop on Complexity for Big Data (C4BD)}, pages = {1-5}, year = {2014} }S. W. Son, Z. Chen, W. Hendrix, A. Agrawal, W. Liao, and A. Choudhary, “Data Compression for the Exascale Computing Era - Survey,” Supercomputing Frontiers and Innovations, vol. 1, no. 2, pp. 76–88, 2014. [url] [bib]
@article{SCH14, author = {Son, Seung Woo and Chen, Zhengzhang and Hendrix, William and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Data Compression for the Exascale Computing Era - Survey}, journal = {Supercomputing Frontiers and Innovations}, year = {2014}, volume = {1}, number = {2}, pages = {76-88} }Y. Xie, D. Palsetia, G. Trajcevski, A. Agrawal, and A. Choudhary, “Silverback: Scalable Association Mining For Temporal Data in Columnar Probabilistic Databases,” in Proceedings of 30th IEEE International Conference on Data Engineering (ICDE), Industrial and Applications Track, 2014, pp. 1072–1083. [url] [bib]
@inproceedings{XPT14, author = {Xie, Yusheng and Palsetia, Diana and Trajcevski, Goce and Agrawal, Ankit and Choudhary, Alok}, title = {Silverback: Scalable Association Mining For Temporal Data in Columnar Probabilistic Databases}, booktitle = {Proceedings of 30th IEEE International Conference on Data Engineering (ICDE), Industrial and Applications Track}, pages = {1072-1083}, year = {2014} }A. Agrawal, M. Patwary, W. Hendrix, W. Liao, and A. Choudhary, “High performance big data clustering,” in Advances in Parallel Computing, Volume 23: Cloud Computing and Big Data, L. Grandinetti, Ed. IOS Press, 2013, pp. 192–211. [url] [bib]
@inbook{APH13, author = {Agrawal, Ankit and Patwary, Mostofa and Hendrix, William and Liao, W. and Choudhary, Alok}, title = {High performance big data clustering}, editor = {Grandinetti, Lucio}, booktitle = {Advances in Parallel Computing, Volume 23: Cloud Computing and Big Data}, publisher = {IOS Press}, year = {2013}, pages = {192-211} }Y. Cheng, Z. Chen, L. Liu, J. Wang, A. Agrawal, and A. Choudhary, “Feedback-Driven Multiclass Active Learning for Data Streams,” in Proceedings of 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, USA, Oct. 2013, 2013, pp. 1311–1320. [url] [bib]
@inproceedings{CCL13, author = {Cheng, Yu and Chen, Zhengzhang and Liu, Lu and Wang, Jiang and Agrawal, Ankit and Choudhary, Alok}, title = {Feedback-Driven Multiclass Active Learning for Data Streams}, booktitle = {Proceedings of 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, USA, Oct. 2013}, pages = {1311-1320}, year = {2013} }W. Hendrix, D. Palsetia, M. M. A. Patwary, A. Agrawal, W. Liao, and A. Choudhary, “A Scalable Algorithm for Single-Linkage Hierarchical Clustering on Distributed Memory Architectures,” in Proceedings of 3rd IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV 2013), Atlanta GA, USA, Oct. 2013, 2013, pp. 7–13. [url] [bib]
@inproceedings{HPP13, author = {Hendrix, William and Palsetia, Diana and Patwary, Md. Mostofa Ali and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {A Scalable Algorithm for Single-Linkage Hierarchical Clustering on Distributed Memory Architectures}, booktitle = {Proceedings of 3rd IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV 2013), Atlanta GA, USA, Oct. 2013}, pages = {7-13}, year = {2013} }C. Jin, Q. Fu, H. Wang, A. Agrawal, W. Hendrix, W. Liao, M. M. A. Patwary, A. Banerjee, and A. Choudhary, “Solving Combinatorial Optimization Problems using Relaxed Linear Programming: A High Performance Computing Perspective,” in Proceedings of the KDD Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine), 2013, pp. 39–46. Won the best paper award. [url] [bib]
@inproceedings{JFW13, author = {Jin, Chen and Fu, Qiang and Wang, Huahua and Agrawal, Ankit and Hendrix, William and Liao, W. and Patwary, Md. Mostofa Ali and Banerjee, Arindam and Choudhary, Alok}, title = {Solving Combinatorial Optimization Problems using Relaxed Linear Programming: A High Performance Computing Perspective}, booktitle = {Proceedings of the KDD Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine)}, pages = {39-46}, year = {2013}, note = {**Won the best paper award**} }C. Jin, M. M. A. Patwary, A. Agrawal, W. Hendrix, W. Liao, and A. Choudhary, “DiSC: A Distributed Single-Linkage Hierarchical Clustering Algorithm using MapReduce,” in Proceedings of the 4th International SC Workshop on Data Intensive Computing in the Clouds (DataCloud) 2013, 2013, pp. 1–6. [url] [bib]
@inproceedings{JPA13, author = {Jin, Chen and Patwary, Md. Mostofa Ali and Agrawal, Ankit and Hendrix, William and Liao, W. and Choudhary, Alok}, title = {DiSC: A Distributed Single-Linkage Hierarchical Clustering Algorithm using MapReduce}, booktitle = {Proceedings of the 4th International SC Workshop on Data Intensive Computing in the Clouds (DataCloud) 2013}, pages = {1-6}, year = {2013} }M. Patwary, D. Palsetia, A. Agrawal, W. Liao, F. Manne, and A. Choudhary, “Scalable Parallel OPTICS Data Clustering Using Graph Algorithmic Techniques,” in Proceedings of 25th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC’13), 2013, pp. 1–12. Article No. 49. [url] [bib]
@inproceedings{PPA13, author = {Patwary, Mostofa and Palsetia, Diana and Agrawal, Ankit and Liao, W. and Manne, Fredrik and Choudhary, Alok}, title = {Scalable Parallel OPTICS Data Clustering Using Graph Algorithmic Techniques}, booktitle = {Proceedings of 25th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC'13)}, year = {2013}, note = {Article No. 49}, pages = {1-12} }Y. Cheng, K. Zhang, Y. Xie, A. Agrawal, and A. Choudhary, “On active learning in hierarchical classification,” in Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM) (Poster paper), 2012, pp. 2467–2470. [url] [bib]
@inproceedings{CZX12, title = {On active learning in hierarchical classification}, author = {Cheng, Yu and Zhang, Kunpeng and Xie, Yusheng and Agrawal, Ankit and Choudhary, Alok}, booktitle = {Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM) (Poster paper)}, pages = {2467--2470}, year = {2012}, organization = {ACM} }W. Hendrix, M. M. A. Patwary, A. Agrawal, W. Liao, and A. Choudhary, “Parallel Hierarchical Clustering on Shared Memory Platforms,” in Proceedings of the 19th International Conference on High Performance Computing (HiPC), 2012, pp. 1–9. [url] [bib]
@inproceedings{HPA12a, title = {Parallel Hierarchical Clustering on Shared Memory Platforms}, author = {Hendrix, William and Patwary, Md. Mostofa Ali and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, booktitle = {Proceedings of the 19th International Conference on High Performance Computing (HiPC)}, pages = {1-9}, year = {2012} }M. Patwary, D. Palsetia, A. Agrawal, W. Liao, F. Manne, and A. Choudhary, “A new scalable parallel DBSCAN algorithm using the disjoint-set data structure,” in ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2012, pp. 1–11. Article No. 62. [url] [bib]
@inproceedings{PPA12, title = {A new scalable parallel DBSCAN algorithm using the disjoint-set data structure}, author = {Patwary, Mostofa and Palsetia, Diana and Agrawal, Ankit and Liao, W. and Manne, Fredrik and Choudhary, Alok}, booktitle = {ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC)}, pages = {1--11}, year = {2012}, note = {Article No. 62}, organization = {IEEE} }N. Nakka, A. Agrawal, and A. N. Choudhary, “Predicting Node Failure in High Performance Computing Systems from Failure and Usage Logs,” in IEEE International Symposium on Parallel and Distributed Processing Symposium (IPDPS) Workshops, 2011, pp. 1557–1566. [url] [bib]
@inproceedings{NAC11, author = {Nakka, Nithin and Agrawal, Ankit and Choudhary, Alok N.}, booktitle = {IEEE International Symposium on Parallel and Distributed Processing Symposium (IPDPS) Workshops}, pages = {1557-1566}, publisher = {IEEE}, title = {Predicting Node Failure in High Performance Computing Systems from Failure and Usage Logs}, year = {2011} }
Healthcare Informatics
A. Paul, W. Liao, A. Choudhary, and A. Agrawal, “Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards,” Journal of Healthcare Informatics Research, 2021. [url] [bib]
@article{PLC21, author = {Paul, Arindam and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards}, journal = {Journal of Healthcare Informatics Research}, year = {2021} }M. K. Danilovich, J. Tsay, R. Al-Bahrani, A. Choudhary, and A. Agrawal, “#Alzheimer’s and Dementia: Expressions of Memory Loss on Twitter,” Topics in Geriatric Rehabilitation, vol. 34, pp. 48–53, 2018. [url] [bib]
@article{DTA18, author = {Danilovich, Margaret K. and Tsay, Jonathan and Al-Bahrani, Reda and Choudhary, Alok and Agrawal, Ankit}, title = {#Alzheimer's and Dementia: Expressions of Memory Loss on Twitter}, journal = {Topics in Geriatric Rehabilitation}, year = {2018}, volume = {34}, pages = {48-53} }R. Al-Bahrani, A. Agrawal, and A. Choudhary, “Survivability prediction of colon cancer patients using neural networks,” Health Informatics Journal, p. 1460458217720395, 2017. [url] [bib]
@article{BAC17, author = {Al-Bahrani, Reda and Agrawal, Ankit and Choudhary, Alok}, title = {Survivability prediction of colon cancer patients using neural networks}, journal = {Health Informatics Journal}, year = {2017}, pages = {1460458217720395} }R. Al-Bahrani, M. K. Danilovich, W. Liao, A. Choudhary, and A. Agrawal, “Towards Identifying Informal Caregivers of Alzheimer’s and Dementia Patients in Social Media,” in Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI), 2017, pp. 324–324. [url] [bib]
@inproceedings{BDL17a, author = {Al-Bahrani, Reda and Danilovich, Margaret K and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Towards Identifying Informal Caregivers of Alzheimer’s and Dementia Patients in Social Media}, booktitle = {Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI)}, pages = {324-324}, year = {2017} }R. Al-Bahrani, M. K. Danilovich, W. Liao, A. Choudhary, and A. Agrawal, “Analyzing Informal Caregiving Expression in Social Media,” in IEEE ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE), 2017, pp. 342–349. [url] [bib]
@inproceedings{BDL17b, author = {Al-Bahrani, Reda and Danilovich, Margaret K and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Analyzing Informal Caregiving Expression in Social Media}, booktitle = {IEEE ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE)}, pages = {342-349}, year = {2017} }K. Lee, A. Agrawal, and A. Choudhary, “Forecasting Influenza Levels using Real-Time Social Media Streams,” in Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI), 2017, pp. 409–414. [url] [bib]
@inproceedings{LAC17, author = {Lee, Kathy and Agrawal, Ankit and Choudhary, Alok}, title = {Forecasting Influenza Levels using Real-Time Social Media Streams}, booktitle = {Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI)}, pages = {409-414}, year = {2017} }K. Lee, S. A. Hasan, O. Farri, A. Choudhary, and A. Agrawal, “Medical Concept Normalization for Online User-Generated Texts,” in Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI), 2017, pp. 462–469. [url] [bib]
@inproceedings{LHF17, author = {Lee, Kathy and Hasan, Sadid A and Farri, Oladimeji and Choudhary, Alok and Agrawal, Ankit}, title = {Medical Concept Normalization for Online User-Generated Texts}, booktitle = {Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI)}, pages = {462-469}, year = {2017} }A. Agrawal and A. Choudhary, “Health Services Data: Big Data Analytics for Deriving Predictive Healthcare Insights,” in Data and Measures in Health Services Research, B. Sobolev, A. Levy, and S. Goring, Eds. Springer US, 2016, pp. 1–17. [url] [bib]
@inbook{AChsr16, author = {Agrawal, Ankit and Choudhary, Alok}, title = {Health Services Data: Big Data Analytics for Deriving Predictive Healthcare Insights}, editor = {Sobolev, Boris and Levy, Adrian and Goring, Sarah}, booktitle = {Data and Measures in Health Services Research}, publisher = {Springer US}, year = {2016}, pages = {1-17} }A. Agrawal, J. Mathias, D. Baker, and A. Choudhary, “Identifying HotSpots in Five Year Survival Electronic Health Records of Older Adults,” in Proceedings of 6th IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2016, pp. 1–6. [url] [bib]
@inproceedings{AMB16iccabs, author = {Agrawal, Ankit and Mathias, Jason and Baker, David and Choudhary, Alok}, title = {Identifying HotSpots in Five Year Survival Electronic Health Records of Older Adults}, booktitle = {Proceedings of 6th IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)}, pages = {1-6}, year = {2016} }A. Agrawal, J. Mathias, D. Baker, and A. Choudhary, “Five Year Life Expectancy Calculator for Older Adults,” in Proceedings of IEEE International Conference on Data Mining (ICDM) (Demo), 2016, pp. 1280–1283. [url] [bib]
@inproceedings{AMB16icdm, author = {Agrawal, Ankit and Mathias, Jason and Baker, David and Choudhary, Alok}, title = {Five Year Life Expectancy Calculator for Older Adults}, booktitle = {Proceedings of IEEE International Conference on Data Mining (ICDM) (Demo)}, pages = {1280-1283}, year = {2016} }R. Al-Bahrani, M. K. Danilovich, A. Agrawal, and A. Choudhary, “Towards Informal Caregiver Identification in Social Media,” in Proceedings of IEEE International Conference on Healthcare Informatics (ICHI) (Poster), 2016, pp. 414–414. [url] [bib]
@inproceedings{BDA16, author = {Al-Bahrani, Reda and Danilovich, Margaret K and Agrawal, Ankit and Choudhary, Alok}, title = {Towards Informal Caregiver Identification in Social Media}, booktitle = {Proceedings of IEEE International Conference on Healthcare Informatics (ICHI) (Poster)}, year = {2016}, pages = {414-414} }K. Lee, A. Agrawal, and A. Choudhary, “Mining Social Media Streams to Improve Public Health Allergy Surveillance,” in Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM), 2015, pp. 815–822. [url] [bib]
@inproceedings{LAC15a, author = {Lee, Kathy and Agrawal, Ankit and Choudhary, Alok}, title = {Mining Social Media Streams to Improve Public Health Allergy Surveillance}, booktitle = {Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM)}, pages = {815-822}, year = {2015} }A. Agrawal, R. Al-Bahrani, R. Merkow, K. Bilimoria, and A. Choudhary, “Colon Surgery Outcome Prediction Using ACS NSQIP Data,” in Proceedings of the KDD Workshop on Data Mining for Healthcare (DMH), 2013, pp. 1–6. [url] [bib]
@inproceedings{ABM13, author = {Agrawal, Ankit and Al-Bahrani, Reda and Merkow, Ryan and Bilimoria, Karl and Choudhary, Alok}, title = {Colon Surgery Outcome Prediction Using ACS NSQIP Data}, booktitle = {Proceedings of the KDD Workshop on Data Mining for Healthcare (DMH)}, pages = {1-6}, year = {2013} }A. Agrawal, R. Al-Bahrani, J. Raman, M. J. Russo, and A. Choudhary, “Lung Transplant Outcome Prediction using UNOS Data,” in Proceedings of the IEEE Big Data Workshop on Bioinformatics and Health Informatics (BHI), 2013, pp. 1–8. [url] [bib]
@inproceedings{ABR13, author = {Agrawal, Ankit and Al-Bahrani, Reda and Raman, Jaishankar and Russo, Mark J. and Choudhary, Alok}, title = {Lung Transplant Outcome Prediction using UNOS Data}, booktitle = {Proceedings of the IEEE Big Data Workshop on Bioinformatics and Health Informatics (BHI)}, pages = {1-8}, year = {2013} }A. Agrawal and A. Choudhary, “An Analysis of Variation in Hospital Billing Using Medicare Data,” in Proceedings of the KDD Workshop on Data Mining for Healthcare (DMH), 2013, pp. 1–6. [url] [bib]
@inproceedings{AC13, author = {Agrawal, Ankit and Choudhary, Alok}, title = {An Analysis of Variation in Hospital Billing Using Medicare Data}, booktitle = {Proceedings of the KDD Workshop on Data Mining for Healthcare (DMH)}, pages = {1-6}, year = {2013} }A. Agrawal, J. Raman, M. J. Russo, and A. Choudhary, “Heart Transplant Outcome Prediction using UNOS Data,” in Proceedings of the KDD Workshop on Data Mining for Healthcare (DMH), 2013, pp. 1–6. [url] [bib]
@inproceedings{ARR13, author = {Agrawal, Ankit and Raman, Jaishankar and Russo, Mark J. and Choudhary, Alok}, title = {Heart Transplant Outcome Prediction using UNOS Data}, booktitle = {Proceedings of the KDD Workshop on Data Mining for Healthcare (DMH)}, pages = {1-6}, year = {2013} }R. Al-Bahrani, A. Agrawal, and A. Choudhary, “Colon cancer survival prediction using ensemble data mining on SEER data,” in Proceedings of the IEEE Big Data Workshop on Bioinformatics and Health Informatics (BHI), 2013, pp. 9–16. [url] [bib]
@inproceedings{BAC13, author = {Al-Bahrani, Reda and Agrawal, Ankit and Choudhary, Alok}, title = {Colon cancer survival prediction using ensemble data mining on SEER data}, booktitle = {Proceedings of the IEEE Big Data Workshop on Bioinformatics and Health Informatics (BHI)}, pages = {9-16}, year = {2013} }K. Lee, A. Agrawal, and A. Choudhary, “Real-Time Digital Flu Surveillance using Twitter Data,” in Proceedings of the SDM Workshop on Data Mining for Medicine and Healthcare (DMMH), 2013, pp. 19–27. [url] [bib]
@inproceedings{LAC13a, title = {Real-Time Digital Flu Surveillance using Twitter Data}, author = {Lee, Kathy and Agrawal, Ankit and Choudhary, Alok}, booktitle = {Proceedings of the SDM Workshop on Data Mining for Medicine and Healthcare (DMMH)}, pages = {19-27}, year = {2013} }K. Lee, A. Agrawal, and A. Choudhary, “Real-Time Disease Surveillance using Twitter Data: Demonstration on Flu and Cancer,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) (Demo paper), 2013, pp. 1474–1477. [url] [bib]
@inproceedings{LAC13b, author = {Lee, Kathy and Agrawal, Ankit and Choudhary, Alok}, title = {Real-Time Disease Surveillance using Twitter Data: Demonstration on Flu and Cancer}, booktitle = {Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) (Demo paper)}, pages = {1474-1477}, year = {2013} }L. Liu, J. Tang, Y. Cheng, A. Agrawal, W. Liao, and A. Choudhary, “Mining Diabetes Complication and Treatment Patterns for Clinical Decision Support,” in Proceedings of 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, USA, Oct. 2013, 2013, pp. 279–288. [url] [bib]
@inproceedings{LTC13, author = {Liu, Lu and Tang, Jie and Cheng, Yu and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Mining Diabetes Complication and Treatment Patterns for Clinical Decision Support}, booktitle = {Proceedings of 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, USA, Oct. 2013}, pages = {279-288}, year = {2013} }J. S. Mathias, A. Agrawal, J. Feinglass, A. J. Cooper, D. W. Baker, and A. Choudhary, “Development of a 5 year life expectancy index in older adults using predictive mining of electronic health record data,” Journal of the American Medical Informatics Association, vol. 20, pp. e118–e124, 2013. JSM and AA are co-first authors. [url] [bib]
@article{MAF13, title = {Development of a 5 year life expectancy index in older adults using predictive mining of electronic health record data}, author = {Mathias, Jason Scott and Agrawal, Ankit and Feinglass, Joe and Cooper, Andrew J and Baker, David William and Choudhary, Alok}, journal = {Journal of the American Medical Informatics Association}, year = {2013}, volume = {20}, pages = {e118-e124}, publisher = {BMJ Publishing Group Ltd}, note = {**JSM and AA are co-first authors**} }Y. Xie, Z. Chen, Y. Cheng, K. Zhang, A. Agrawal, W. Liao, and A. Choudhary, “Detecting and Tracking Disease Outbreaks by Mining Social Media Data,” in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013, pp. 2958–2960. [url] [bib]
@inproceedings{XCC13, title = {Detecting and Tracking Disease Outbreaks by Mining Social Media Data}, author = {Xie, Yusheng and Chen, Zhengzhang and Cheng, Yu and Zhang, Kunpeng and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI)}, pages = {2958-2960}, year = {2013} }A. Agrawal, S. Misra, R. Narayanan, L. Polepeddi, and A. Choudhary, “Lung cancer survival prediction using ensemble data mining on SEER data,” Scientific Programming, vol. 20, no. 1, pp. 29–42, 2012. [url] [bib]
@article{AMN12, title = {Lung cancer survival prediction using ensemble data mining on SEER data}, author = {Agrawal, Ankit and Misra, Sanchit and Narayanan, Ramanathan and Polepeddi, Lalith and Choudhary, Alok}, journal = {Scientific Programming}, volume = {20}, number = {1}, pages = {29--42}, year = {2012}, publisher = {IOS Press} }A. Agrawal and A. Choudhary, “Identifying HotSpots in Lung Cancer Data Using Association Rule Mining,” in 2nd IEEE ICDM Workshop on Biological Data Mining and its Applications in Healthcare (BioDM), 2011, pp. 995–1002. [url] [bib]
@inproceedings{AC11a, author = {Agrawal, Ankit and Choudhary, Alok}, title = {Identifying HotSpots in Lung Cancer Data Using Association Rule Mining}, year = {2011}, booktitle = {2nd IEEE ICDM Workshop on Biological Data Mining and its Applications in Healthcare (BioDM)}, pages = {995--1002} }A. Agrawal and A. Choudhary, “Association Rule Mining Based HotSpot Analysis on SEER Lung Cancer Data,” International Journal of Knowledge Discovery in Bioinformatics (IJKDB), vol. 2, no. 2, pp. 34–54, 2011. [url] [bib]
@article{AC11b, title = {Association Rule Mining Based HotSpot Analysis on SEER Lung Cancer Data}, author = {Agrawal, Ankit and Choudhary, Alok}, journal = {International Journal of Knowledge Discovery in Bioinformatics (IJKDB)}, volume = {2}, number = {2}, pages = {34--54}, year = {2011}, publisher = {IGI Global} }A. Agrawal, S. Misra, A. Choudhary, and K. Bilimoria, “Risk prediction for post-operative adverse outcomes in colorectal cancer surgery,” in Proc. of IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (Poster paper), 2011, pp. 232–232. [url] [bib]
@inproceedings{AMC11, title = {Risk prediction for post-operative adverse outcomes in colorectal cancer surgery}, author = {Agrawal, Ankit and Misra, Sanchit and Choudhary, Alok and Bilimoria, Karl}, booktitle = {Proc. of IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (Poster paper)}, pages = {232--232}, year = {2011}, organization = {IEEE} }A. Agrawal, S. Misra, R. Narayanan, L. Polepeddi, and A. Choudhary, “A lung cancer mortality risk calculator based on SEER data,” in Proc. of IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (Poster paper), 2011, pp. 233–233. [url] [bib]
@inproceedings{AMN11a, author = {Agrawal, Ankit and Misra, Sanchit and Narayanan, Ramanathan and Polepeddi, Lalith and Choudhary, Alok}, title = {A lung cancer mortality risk calculator based on SEER data}, booktitle = {Proc. of IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (Poster paper)}, pages = {233-233}, year = {2011} }A. Agrawal, S. Misra, R. Narayanan, L. Polepeddi, and A. Choudhary, “A lung cancer outcome calculator using ensemble data mining on SEER data,” in Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics (BIOKDD), New York, NY, USA, 2011, pp. 1–9. [url] [bib]
@inproceedings{AMN11b, author = {Agrawal, Ankit and Misra, Sanchit and Narayanan, Ramanathan and Polepeddi, Lalith and Choudhary, Alok}, title = {A lung cancer outcome calculator using ensemble data mining on SEER data}, booktitle = {Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics (BIOKDD)}, year = {2011}, isbn = {978-1-4503-0839-7}, location = {San Diego, California}, pages = {1--9}, articleno = {5}, numpages = {9}, publisher = {ACM}, address = {New York, NY, USA} }
Social Media Analytics
A. Paul, W. Liao, A. Choudhary, and A. Agrawal, “Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards,” Journal of Healthcare Informatics Research, 2021. [url] [bib]
@article{PLC21, author = {Paul, Arindam and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards}, journal = {Journal of Healthcare Informatics Research}, year = {2021} }M. K. Danilovich, J. Tsay, R. Al-Bahrani, A. Choudhary, and A. Agrawal, “#Alzheimer’s and Dementia: Expressions of Memory Loss on Twitter,” Topics in Geriatric Rehabilitation, vol. 34, pp. 48–53, 2018. [url] [bib]
@article{DTA18, author = {Danilovich, Margaret K. and Tsay, Jonathan and Al-Bahrani, Reda and Choudhary, Alok and Agrawal, Ankit}, title = {#Alzheimer's and Dementia: Expressions of Memory Loss on Twitter}, journal = {Topics in Geriatric Rehabilitation}, year = {2018}, volume = {34}, pages = {48-53} }R. Al-Bahrani, M. K. Danilovich, W. Liao, A. Choudhary, and A. Agrawal, “Towards Identifying Informal Caregivers of Alzheimer’s and Dementia Patients in Social Media,” in Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI), 2017, pp. 324–324. [url] [bib]
@inproceedings{BDL17a, author = {Al-Bahrani, Reda and Danilovich, Margaret K and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Towards Identifying Informal Caregivers of Alzheimer’s and Dementia Patients in Social Media}, booktitle = {Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI)}, pages = {324-324}, year = {2017} }R. Al-Bahrani, M. K. Danilovich, W. Liao, A. Choudhary, and A. Agrawal, “Analyzing Informal Caregiving Expression in Social Media,” in IEEE ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE), 2017, pp. 342–349. [url] [bib]
@inproceedings{BDL17b, author = {Al-Bahrani, Reda and Danilovich, Margaret K and Liao, W. and Choudhary, Alok and Agrawal, Ankit}, title = {Analyzing Informal Caregiving Expression in Social Media}, booktitle = {IEEE ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE)}, pages = {342-349}, year = {2017} }Y. Cheng, A. Agrawal, H. Liu, and A. Choudhary, “Legislative Prediction with Dual Uncertainty Minimization from Heterogeneous Information,” Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 10, no. 2, pp. 110–120, 2017. [url] [bib]
@article{CAL17, author = {Cheng, Yu and Agrawal, Ankit and Liu, Huan and Choudhary, Alok}, title = {Legislative Prediction with Dual Uncertainty Minimization from Heterogeneous Information}, journal = {Statistical Analysis and Data Mining: The ASA Data Science Journal}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, volume = {10}, number = {2}, pages = {110-120}, year = {2017} }K. Gopalakrishnan, A. Choudhary, and A. Agrawal, “Big Data in Building Information Modeling Research: Survey and Exploratory Text Mining,” MOJ Civil Eng, vol. 3, no. 6, p. 00087, 2017. [url] [bib]
@article{GAC17, author = {Gopalakrishnan, Kasthurirangan and Choudhary, Alok and Agrawal, Ankit}, title = {Big Data in Building Information Modeling Research: Survey and Exploratory Text Mining}, journal = {MOJ Civil Eng}, year = {2017}, volume = {3}, number = {6}, pages = {00087} }K. Lee, A. Agrawal, and A. Choudhary, “Forecasting Influenza Levels using Real-Time Social Media Streams,” in Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI), 2017, pp. 409–414. [url] [bib]
@inproceedings{LAC17, author = {Lee, Kathy and Agrawal, Ankit and Choudhary, Alok}, title = {Forecasting Influenza Levels using Real-Time Social Media Streams}, booktitle = {Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI)}, pages = {409-414}, year = {2017} }K. Lee, S. A. Hasan, O. Farri, A. Choudhary, and A. Agrawal, “Medical Concept Normalization for Online User-Generated Texts,” in Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI), 2017, pp. 462–469. [url] [bib]
@inproceedings{LHF17, author = {Lee, Kathy and Hasan, Sadid A and Farri, Oladimeji and Choudhary, Alok and Agrawal, Ankit}, title = {Medical Concept Normalization for Online User-Generated Texts}, booktitle = {Proceedings of the Fifth International Conference on Healthcare Informatics (ICHI)}, pages = {462-469}, year = {2017} }Y. Xie, Z. Chen, A. Agrawal, and A. Choudhary, “Distinguish polarity in bag-of-words visualization,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 2017, pp. 3344–3350. [url] [bib]
@inproceedings{XCA17, author = {Xie, Yusheng and Chen, Zhengzhang and Agrawal, Ankit and Choudhary, Alok}, title = {Distinguish polarity in bag-of-words visualization}, booktitle = {Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)}, pages = {3344-3350}, year = {2017} }R. Al-Bahrani, M. K. Danilovich, A. Agrawal, and A. Choudhary, “Towards Informal Caregiver Identification in Social Media,” in Proceedings of IEEE International Conference on Healthcare Informatics (ICHI) (Poster), 2016, pp. 414–414. [url] [bib]
@inproceedings{BDA16, author = {Al-Bahrani, Reda and Danilovich, Margaret K and Agrawal, Ankit and Choudhary, Alok}, title = {Towards Informal Caregiver Identification in Social Media}, booktitle = {Proceedings of IEEE International Conference on Healthcare Informatics (ICHI) (Poster)}, year = {2016}, pages = {414-414} }A. Krishna, A. Agrawal, and A. Choudhary, “Predicting the Outcome of Startups: Less Failure, More Success,” in Proceedings of IEEE ICDM Workshop on Data Market for Co-evolution of Sciences and Business (MoDAT), 2016, pp. 798–805. [url] [bib]
@inproceedings{KAC16, author = {Krishna, Amar and Agrawal, Ankit and Choudhary, Alok}, title = {Predicting the Outcome of Startups: Less Failure, More Success}, booktitle = {Proceedings of IEEE ICDM Workshop on Data Market for Co-evolution of Sciences and Business (MoDAT)}, pages = {798-805}, year = {2016} }R. Liu, D. Palsetia, A. Paul, R. Al-Bahrani, D. Jha, W. Liao, A. Agrawal, and A. Choudhary, “PinterNet: A Thematic Label Curation Tool for Large Image Datasets,” in Proceedings of IEEE BigData Workshop on Open Science in Big Data (OSBD), 2016, pp. 2353–2362. [url] [bib]
@inproceedings{LPP16, author = {Liu, Ruoqian and Palsetia, Diana and Paul, Arindam and Al-Bahrani, Reda and Jha, Dipendra and Liao, W. and Agrawal, Ankit and Choudhary, Alok}, title = {PinterNet: A Thematic Label Curation Tool for Large Image Datasets}, booktitle = {Proceedings of IEEE BigData Workshop on Open Science in Big Data (OSBD)}, pages = {2353-2362}, year = {2016} }A. Paul, A. Agrawal, W. Liao, and A. Choudhary, “AnonyMine: Mining anonymous social media posts using psycho-lingual and crowd-sourced dictionaries,” in Proceedings of ACM SIGKDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM), 2016. [url] [bib]
@inproceedings{PAL16, author = {Paul, Arindam and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {AnonyMine: Mining anonymous social media posts using psycho-lingual and crowd-sourced dictionaries}, booktitle = {Proceedings of ACM SIGKDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM)}, pages = {}, year = {2016} }Y. Cheng, A. Agrawal, H. Liu, and A. Choudhary, “Legislative prediction with dual uncertainty minimization from heterogeneous information,” in Proceedings of the 15th SIAM International Conference on Data Mining (SDM), 2015, pp. 361–369. [url] [bib]
@inproceedings{CAL15, author = {Cheng, Yu and Agrawal, Ankit and Liu, Huan and Choudhary, Alok}, title = {Legislative prediction with dual uncertainty minimization from heterogeneous information}, booktitle = {Proceedings of the 15th SIAM International Conference on Data Mining (SDM)}, pages = {361-369}, year = {2015} }K. Lee, A. Agrawal, and A. Choudhary, “Mining Social Media Streams to Improve Public Health Allergy Surveillance,” in Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM), 2015, pp. 815–822. [url] [bib]
@inproceedings{LAC15a, author = {Lee, Kathy and Agrawal, Ankit and Choudhary, Alok}, title = {Mining Social Media Streams to Improve Public Health Allergy Surveillance}, booktitle = {Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM)}, pages = {815-822}, year = {2015} }V. Rastogi and A. Agrawal, “All Your Google and Facebook Logins are Belong to Us: A Case for Single Sign-off,” in Proceedings of 8th IEEE International Conference on Contemporary Computing (IC3), 2015, pp. 416–421. [url] [bib]
@inproceedings{RA15, author = {Rastogi, Vaibhav and Agrawal, Ankit}, title = {All Your Google and Facebook Logins are Belong to Us: A Case for Single Sign-off}, booktitle = {Proceedings of 8th IEEE International Conference on Contemporary Computing (IC3)}, pages = {416-421}, year = {2015} }Y. Xie, P. Daga, Y. Cheng, K. Zhang, A. Agrawal, and A. Choudhary, “Reducing infrequent-token perplexity via variational corpora,” in Proceedings of the 53rd Annual Meeting of the Association of Computational Linguistics (ACL) and the 7th International Joint Conference on Natural Language Processing, 2015, pp. 609–615. [url] [bib]
@inproceedings{XDC15, author = {Xie, Yusheng and Daga, Pranjal and Cheng, Yu and Zhang, Kunpeng and Agrawal, Ankit and Choudhary, Alok}, title = {Reducing infrequent-token perplexity via variational corpora}, booktitle = {Proceedings of the 53rd Annual Meeting of the Association of Computational Linguistics (ACL) and the 7th International Joint Conference on Natural Language Processing}, pages = {609-615}, year = {2015} }Y. Cheng, A. Agrawal, H. Liu, and A. Choudhary, “Social Role Identification via Dual Uncertainty Minimization Regularization,” in Proceedings of International Conference on Data Mining (ICDM), 2014, pp. 767–772. [url] [bib]
@inproceedings{CAL14, author = {Cheng, Yu and Agrawal, Ankit and Liu, Huan and Choudhary, Alok}, title = {Social Role Identification via Dual Uncertainty Minimization Regularization}, booktitle = {Proceedings of International Conference on Data Mining (ICDM)}, pages = {767-772}, year = {2014} }R. Liu, A. Agrawal, W. Liao, and A. Choudhary, “Enhancing Financial Decision-Making Using Social Behavior Modeling,” in Proceedings of 8th KDD Workshop on Social Network Mining and Analysis for Business, Consumer and Social Insights (SNAKDD), 2014, pp. 13:1–13:5. Article No. 13. [url] [bib]
@inproceedings{LAL14a, author = {Liu, Ruoqian and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Enhancing Financial Decision-Making Using Social Behavior Modeling}, booktitle = {Proceedings of 8th KDD Workshop on Social Network Mining and Analysis for Business, Consumer and Social Insights (SNAKDD)}, pages = {13:1--13:5}, year = {2014}, note = {Article No. 13} }D. Palsetia, M. M. A. Patwary, A. Agrawal, and A. Choudhary, “Excavating Social Circles via User-Interests,” Social Network Analysis and Mining, vol. 4, no. 1, pp. 1–12, 2014. [url] [bib]
@article{PPA14, author = {Palsetia, Diana and Patwary, Md. Mostofa Ali and Agrawal, Ankit and Choudhary, Alok}, title = {Excavating Social Circles via User-Interests}, journal = {Social Network Analysis and Mining}, year = {2014}, volume = {4}, number = {1}, pages = {1-12} }D. Palsetia, M. Patwary, W. Hendrix, A. Agrawal, and A. Choudhary, “Clique Guided Community Detection,” in Proceedings of 2014 IEEE International Conference on Big Data (BigData), 2014, pp. 500–509. [url] [bib]
@inproceedings{PPH14, author = {Palsetia, Diana and Patwary, Mostofa and Hendrix, William and Agrawal, Ankit and Choudhary, Alok}, title = {Clique Guided Community Detection}, booktitle = {Proceedings of 2014 IEEE International Conference on Big Data (BigData)}, pages = {500-509}, year = {2014} }Y. Xie, Y. Cheng, A. Agrawal, and A. Choudhary, “Estimating online user location distribution without GPS location,” in Proceedings of ICDM Workshop on Connecting Online and Offline Social Network Analysis (COOL-SNA), 2014, pp. 936–943. [url] [bib]
@inproceedings{XCA14, author = {Xie, Yusheng and Cheng, Yu and Agrawal, Ankit and Choudhary, Alok}, title = {Estimating online user location distribution without GPS location}, booktitle = {Proceedings of ICDM Workshop on Connecting Online and Offline Social Network Analysis (COOL-SNA)}, pages = {936-943}, year = {2014} }Y. Xie, Z. Chen, D. Palsetia, A. Agrawal, and A. Choudhary, “Indexing Bipartite Memberships in Web Graphs,” in Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM) 2014, 2014, pp. 166–173. [url] [bib]
@inproceedings{XCP14, author = {Xie, Yusheng and Chen, Zhengzhang and Palsetia, Diana and Agrawal, Ankit and Choudhary, Alok}, title = {Indexing Bipartite Memberships in Web Graphs}, booktitle = {Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM) 2014}, pages = {166-173}, year = {2014} }Y. Cheng, Z. Chen, J. Wang, A. Agrawal, and A. Choudhary, “Bootstrapping Active Name Disambiguation with Crowdsourcing,” in Proceedings of 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, USA, Oct. 2013 (Poster paper), 2013, pp. 1213–1216. [url] [bib]
@inproceedings{CCW13, author = {Cheng, Yu and Chen, Zhengzhang and Wang, Jiang and Agrawal, Ankit and Choudhary, Alok}, title = {Bootstrapping Active Name Disambiguation with Crowdsourcing}, booktitle = {Proceedings of 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, USA, Oct. 2013 (Poster paper)}, pages = {1213-1216}, year = {2013} }Y. Cheng, Y. Xie, Z. Chen, A. Agrawal, A. Choudhary, and S. Guo, “JobMiner: A Real-time System for Mining Job-related Patterns from Social Media,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) (Demo paper), 2013, pp. 1450–1453. [url] [bib]
@inproceedings{CXC13b, author = {Cheng, Yu and Xie, Yusheng and Chen, Zhengzhang and Agrawal, Ankit and Choudhary, Alok and Guo, Songtao}, title = {JobMiner: A Real-time System for Mining Job-related Patterns from Social Media}, booktitle = {Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) (Demo paper)}, pages = {1450-1453}, year = {2013} }K. Lee, A. Agrawal, and A. Choudhary, “Real-Time Digital Flu Surveillance using Twitter Data,” in Proceedings of the SDM Workshop on Data Mining for Medicine and Healthcare (DMMH), 2013, pp. 19–27. [url] [bib]
@inproceedings{LAC13a, title = {Real-Time Digital Flu Surveillance using Twitter Data}, author = {Lee, Kathy and Agrawal, Ankit and Choudhary, Alok}, booktitle = {Proceedings of the SDM Workshop on Data Mining for Medicine and Healthcare (DMMH)}, pages = {19-27}, year = {2013} }K. Lee, A. Agrawal, and A. Choudhary, “Real-Time Disease Surveillance using Twitter Data: Demonstration on Flu and Cancer,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) (Demo paper), 2013, pp. 1474–1477. [url] [bib]
@inproceedings{LAC13b, author = {Lee, Kathy and Agrawal, Ankit and Choudhary, Alok}, title = {Real-Time Disease Surveillance using Twitter Data: Demonstration on Flu and Cancer}, booktitle = {Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) (Demo paper)}, pages = {1474-1477}, year = {2013} }Y. Xie, Z. Chen, A. Agrawal, A. Choudhary, and L. Liu, “Random Walk-based Graphical Sampling in Unbalanced Heterogeneous Bipartite Social Graphs,” in Proceedings of 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, USA, Oct. 2013 (Poster paper), 2013, pp. 1473–1476. [url] [bib]
@inproceedings{XCA13a, author = {Xie, Yusheng and Chen, Zhengzhang and Agrawal, Ankit and Choudhary, Alok and Liu, Lu}, title = {Random Walk-based Graphical Sampling in Unbalanced Heterogeneous Bipartite Social Graphs}, booktitle = {Proceedings of 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, USA, Oct. 2013 (Poster paper)}, pages = {1473-1476}, year = {2013} }Y. Xie, Z. Chen, A. Agrawal, W. Liao, and A. Choudhary, “Caranx: Scalable Social Image Index Using Phylogenetic Tree of Hashtags,” in Proceedings of 25th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC’13) (Poster paper), 2013, pp. 1–2. [url] [bib]
@inproceedings{XCA13b, author = {Xie, Yusheng and Chen, Zhuoyuan and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Caranx: Scalable Social Image Index Using Phylogenetic Tree of Hashtags}, booktitle = {Proceedings of 25th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC'13) (Poster paper)}, pages = {1-2}, year = {2013} }Y. Xie, Z. Chen, Y. Cheng, K. Zhang, A. Agrawal, W. Liao, and A. Choudhary, “Detecting and Tracking Disease Outbreaks by Mining Social Media Data,” in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013, pp. 2958–2960. [url] [bib]
@inproceedings{XCC13, title = {Detecting and Tracking Disease Outbreaks by Mining Social Media Data}, author = {Xie, Yusheng and Chen, Zhengzhang and Cheng, Yu and Zhang, Kunpeng and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI)}, pages = {2958-2960}, year = {2013} }Y. Xie, Z. Chen, K. Zhang, M. M. A. Patwary, Y. Cheng, H. Liu, A. Agrawal, and A. Choudhary, “Graphical Modeling of Macro Behavioral Targeting in Social Networks,” in Proceedings of the 13th SIAM International Conference on Data Mining (SDM), 2013, pp. 740–748. [url] [bib]
@inproceedings{XCZ13a, title = {Graphical Modeling of Macro Behavioral Targeting in Social Networks}, author = {Xie, Yusheng and Chen, Zhengzhang and Zhang, Kunpeng and Patwary, Md. Mostofa Ali and Cheng, Yu and Liu, Haotian and Agrawal, Ankit and Choudhary, Alok}, booktitle = {Proceedings of the 13th SIAM International Conference on Data Mining (SDM)}, pages = {740-748}, year = {2013} }Y. Xie, Z. Chen, K. Zhang, Y. Cheng, D. K. Honbo, A. Agrawal, and A. Choudhary, “MuSES: a Multilingual Sentiment Elicitation System for Social Media Data,” IEEE Intelligent Systems, vol. 99, pp. 1541–1672, 2013. [url] [bib]
@article{XCZ13b, author = {Xie, Yusheng and Chen, Zhengzhang and Zhang, Kunpeng and Cheng, Yu and Honbo, Daniel K. and Agrawal, Ankit and Choudhary, Alok}, title = {MuSES: a Multilingual Sentiment Elicitation System for Social Media Data}, journal = {IEEE Intelligent Systems}, year = {2013}, pages = {1541-1672}, volume = {99} }Y. Xie, Z. Chen, K. Zhang, Y. Cheng, C. Jin, A. Agrawal, and A. Choudhary, “Elver: Recommending Facebook Pages in Cold Start Situation Without Content Features,” in Proceedings of IEEE International Conference on Big Data (BigData), 2013, pp. 475–479. [url] [bib]
@inproceedings{XCZ13c, author = {Xie, Yusheng and Chen, Zhengzhang and Zhang, Kunpeng and Cheng, Yu and Jin, Chen and Agrawal, Ankit and Choudhary, Alok}, title = {Elver: Recommending Facebook Pages in Cold Start Situation Without Content Features}, booktitle = {Proceedings of IEEE International Conference on Big Data (BigData)}, pages = {475-479}, year = {2013} }K. Zhang, D. Downey, Z. Chen, Y. Xie, Y. Cheng, A. Agrawal, W. Liao, and A. Choudhary, “A Probabilistic Graphical Model for Brand Reputation Assessment in Social Networks,” in Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM), Niagara Falls, Canada, Aug. 2013, 2013, pp. 223–230. [url] [bib]
@inproceedings{ZDC13, author = {Zhang, Kunpeng and Downey, Doug and Chen, Zhengzhang and Xie, Yusheng and Cheng, Yu and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {A Probabilistic Graphical Model for Brand Reputation Assessment in Social Networks}, booktitle = {Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM), Niagara Falls, Canada, Aug. 2013}, pages = {223-230}, year = {2013} }Y. Cheng, Y. Xie, K. Zhang, A. Agrawal, and A. Choudhary, “CluChunk: clustering large scale user-generated content incorporating chunklet information,” in Proceedings of the KDD Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine), 2012, pp. 12–19. [url] [bib]
@inproceedings{CXZ12a, title = {CluChunk: clustering large scale user-generated content incorporating chunklet information}, author = {Cheng, Yu and Xie, Yusheng and Zhang, Kunpeng and Agrawal, Ankit and Choudhary, Alok}, booktitle = {Proceedings of the KDD Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine)}, pages = {12--19}, year = {2012}, organization = {ACM} }Y. Cheng, Y. Xie, K. Zhang, A. Agrawal, and A. Choudhary, “How Online Content is Received by Users in Social Media: A Case Study on Facebook. com Posts,” in Proceedings of the KDD Workshop on Social Media Analytics (SOMA), 2012, pp. 1–8. [url] [bib]
@inproceedings{CXZ12b, title = {How Online Content is Received by Users in Social Media: A Case Study on Facebook. com Posts}, author = {Cheng, Yu and Xie, Yusheng and Zhang, Kunpeng and Agrawal, Ankit and Choudhary, Alok}, booktitle = {Proceedings of the KDD Workshop on Social Media Analytics (SOMA)}, pages = {1--8}, year = {2012} }D. Palsetia, M. M. A. Patwary, K. Zhang, K. Lee, C. Moran, Y. Xie, D. Honbo, A. Agrawal, W. Liao, and A. Choudhary, “User-Interest based Community Extraction in Social Networks,” in Proceedings of the KDD Workshop on Social Network Mining and Analysis (SNAKDD), 2012, pp. 1–4. [url] [bib]
@inproceedings{PPZ12, title = {User-Interest based Community Extraction in Social Networks}, author = {Palsetia, Diana and Patwary, Md Mostofa Ali and Zhang, Kunpeng and Lee, Kathy and Moran, Christopher and Xie, Yves and Honbo, Daniel and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, booktitle = {Proceedings of the KDD Workshop on Social Network Mining and Analysis (SNAKDD)}, pages = {1--4}, year = {2012} }Y. Xie, Y. Cheng, D. Honbo, K. Zhang, A. Agrawal, A. Choudhary, Y. Gao, and J. Gou, “Probabilistic macro behavioral targeting,” in Proceedings of the CIKM workshop on Data-driven user behavioral modelling and mining from social media (DUBMMSM), 2012, pp. 7–10. [url] [bib]
@inproceedings{XCH12a, title = {Probabilistic macro behavioral targeting}, author = {Xie, Yusheng and Cheng, Yu and Honbo, Daniel and Zhang, Kunpeng and Agrawal, Ankit and Choudhary, Alok and Gao, Yi and Gou, Jiangtao}, booktitle = {Proceedings of the CIKM workshop on Data-driven user behavioral modelling and mining from social media (DUBMMSM)}, pages = {7--10}, year = {2012}, organization = {ACM} }Y. Xie, Y. Cheng, D. Honbo, K. Zhang, A. Agrawal, and A. Choudhary, “Crowdsourcing recommendations from social sentiment,” in Proceedings of KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM), 2012, pp. 9:1–9:8. [url] [bib]
@inproceedings{XCH12b, title = {Crowdsourcing recommendations from social sentiment}, author = {Xie, Yusheng and Cheng, Yu and Honbo, Daniel and Zhang, Kunpeng and Agrawal, Ankit and Choudhary, Alok}, booktitle = {Proceedings of KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM)}, pages = {9:1-9:8}, year = {2012}, organization = {ACM} }Y. Xie, D. Honbo, A. Choudhary, K. Zhang, Y. Cheng, and A. Agrawal, “VOXSUP: a social engagement framework,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) (Demo paper), 2012, pp. 1556–1559. [url] [bib]
@inproceedings{XHC12, title = {VOXSUP: a social engagement framework}, author = {Xie, Yusheng and Honbo, Daniel and Choudhary, Alok and Zhang, Kunpeng and Cheng, Yu and Agrawal, Ankit}, booktitle = {Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) (Demo paper)}, pages = {1556--1559}, year = {2012}, organization = {ACM} }K. Zhang, Y. Xie, Y. Cheng, D. Honbo, D. Downey, A. Agrawal, W. Liao, and A. Choudhary, “Sentiment identification by incorporating syntax, semantics and context information,” in Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (Poster paper), 2012, pp. 1143–1144. [url] [bib]
@inproceedings{ZXC12, title = {Sentiment identification by incorporating syntax, semantics and context information}, author = {Zhang, Kunpeng and Xie, Yusheng and Cheng, Yu and Honbo, Daniel and Downey, Doug and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, booktitle = {Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (Poster paper)}, pages = {1143--1144}, year = {2012}, organization = {ACM} }Y. Cheng, K. Zhang, Y. Xie, A. Agrawal, W. Liao, and A. Choudhary, “Learning to Group Web Text Incorporating Prior Information,” in 6th IEEE ICDM Workshop on Optimization Based Techniques for Emerging Data Mining Problems, (OEDM), 2011, pp. 212–219. [url] [bib]
@inproceedings{CZX11, author = {Cheng, Yu and Zhang, Kunpeng and Xie, Yusheng and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Learning to Group Web Text Incorporating Prior Information}, year = {2011}, booktitle = {6th IEEE ICDM Workshop on Optimization Based Techniques for Emerging Data Mining Problems, (OEDM)}, pages = {212--219} }K. Lee, D. Palsetia, R. Narayanan, M. Patwary, A. Agrawal, and A. Choudhary, “Twitter Trending Topic Classification,” in 6th IEEE ICDM Workshop on Optimization Based Techniques for Emerging Data Mining Problems, (OEDM), 2011, pp. 251–258. [url] [bib]
@inproceedings{LPN11, author = {Lee, Kathy and Palsetia, Diana and Narayanan, Ramanathan and Patwary, Mostofa and Agrawal, Ankit and Choudhary, Alok}, title = {Twitter Trending Topic Classification}, year = {2011}, booktitle = {6th IEEE ICDM Workshop on Optimization Based Techniques for Emerging Data Mining Problems, (OEDM)}, pages = {251--258} }L. Polepeddi, A. Agrawal, and A. Choudhary, “Poll: A Citation-Text-Based System for Identifying High-Impact Contributions of an Article,” in IEEE ICDM Workshop on Data Mining in Networks, (DaMNet), 2011, pp. 965–968. [url] [bib]
@inproceedings{PAC11, author = {Polepeddi, Lalith and Agrawal, Ankit and Choudhary, Alok}, title = {Poll: A Citation-Text-Based System for Identifying High-Impact Contributions of an Article}, year = {2011}, booktitle = {IEEE ICDM Workshop on Data Mining in Networks, (DaMNet)}, pages = {965--968} }K. Zhang, Y. Cheng, Y. Xie, A. Agrawal, D. Palsetia, K. Lee, and A. Choudhary, “SES: Sentiment Elicitation System for Social Media Data,” in IEEE ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, (SENTIRE), 2011, pp. 129–136. [url] [bib]
@inproceedings{ZCX11, author = {Zhang, Kunpeng and Cheng, Yu and Xie, Yusheng and Agrawal, Ankit and Palsetia, Diana and Lee, Kathy and Choudhary, Alok}, title = {SES: Sentiment Elicitation System for Social Media Data}, year = {2011}, booktitle = {IEEE ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, (SENTIRE)}, pages = {129--136} }
Astroinformatics
Q. Kang, A. Sim, P. Nugent, S. Lee, W. Liao, A. Agrawal, A. Choudhary, and K. Wu, “Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow,” in Proceedings of 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2020, pp. 619–628. [url] [bib]
@inproceedings{KSN20, author = {Kang, Qiao and Sim, Alex and Nugent, Peter and Lee, Sunwoo and Liao, W. and Agrawal, Ankit and Choudhary, Alok and Wu, Kesheng}, title = {Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow}, booktitle = {Proceedings of 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)}, pages = {619-628}, year = {2020} }E. Rangel, N. Frontiere, S. Habib, K. Heitmann, W. Liao, A. Agrawal, and A. Choudhary, “Building Halo Merger Trees from the Q Continuum Simulation,” in Proceedings of 24th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC’17), 2017, pp. 398–407. [url] [bib]
@inproceedings{RFH17, author = {Rangel, Esteban and Frontiere, Nicholas and Habib, Salman and Heitmann, Katrin and Liao, W. and Agrawal, Ankit and Choudhary, Alok}, title = {Building Halo Merger Trees from the Q Continuum Simulation}, booktitle = {Proceedings of 24th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC'17)}, pages = {398-407}, year = {2017} }E. Rangel, N. Li, S. Habib, T. Peterka, A. Agrawal, W. Liao, and A. Choudhary, “Parallel DTFE Surface Density Field Reconstruction,” in Proceedings of IEEE Cluster, 2016, pp. 30–39. Won the best paper award. [url] [bib]
@inproceedings{RLH16, author = {Rangel, Esteban and Li, Nan and Habib, Salman and Peterka, Tom and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, title = {Parallel DTFE Surface Density Field Reconstruction}, booktitle = {Proceedings of IEEE Cluster}, pages = {30-39}, note = {Won the best paper award}, year = {2016} }
Climate Informatics
A. R. Ganguly, E. Kodra, A. Agrawal, A. Banerjee, S. Boriah, S. Chatterjee, S. Chatterjee, A. Choudhary, D. Das, J. Faghmous, P. Ganguli, S. Ghosh, K. Hayhoe, C. Hays, W. Hendrix, Q. Fu, J. Kawale, D. Kumar, V. Kumar, W. Liao, S. Liess, R. Mawalagedara, V. Mithal, R. Oglesby, K. Salvi, P. K. Snyder, K. Steinhaeuser, D. Wang, and D. Wuebbles, “Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques,” Nonlinear Processes in Geophysics, vol. 21, pp. 777–795, 2014. [url] [bib]
@article{GKA14, author = {Ganguly, Auroop R. and Kodra, Evan and Agrawal, Ankit and Banerjee, Arindam and Boriah, Shyam and Chatterjee, Snigdhansu and Chatterjee, Soumyadeep and Choudhary, Alok and Das, Debasish and Faghmous, J and Ganguli, Poulomi and Ghosh, Subimal and Hayhoe, Katharine and Hays, Cindy and Hendrix, William and Fu, Qiang and Kawale, Jaya and Kumar, Devashish and Kumar, Vipin and Liao, W. and Liess, Stefan and Mawalagedara, Rachindra and Mithal, Varun and Oglesby, Robert and Salvi, Kaustubh and Snyder, Peter K. and Steinhaeuser, Karsten and Wang, Daiwei and Wuebbles, Don}, title = {Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques}, journal = {Nonlinear Processes in Geophysics}, year = {2014}, volume = {21}, number = {}, pages = {777-795} }Z. Chen, Y. Xie, Y. Cheng, K. Zhang, A. Agrawal, W. Liao, N. Samatova, and A. Choudhary, “Forecast Oriented Classification of Spatio-Temporal Extreme Events,” in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013, pp. 2952–2954. [url] [bib]
@inproceedings{CXC13a, title = {Forecast Oriented Classification of Spatio-Temporal Extreme Events}, author = {Chen, Zhengzhang and Xie, Yusheng and Cheng, Yu and Zhang, Kunpeng and Agrawal, Ankit and Liao, W. and Samatova, Nagiza and Choudhary, Alok}, booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI)}, pages = {2952-2954}, year = {2013} }W. Hendrix, I. Tetteh, A. Agrawal, F. Semazzi, W. Liao, and A. Choudhary, “Community Dynamics and Analysis of Decadal Trends in Climate Data,” in 3rd IEEE ICDM Workshop on Knowledge Discovery from Climate Data, (ClimKD), 2011, pp. 9–14. [url] [bib]
@inproceedings{HTA11, author = {Hendrix, William and Tetteh, Isaac and Agrawal, Ankit and Semazzi, Fredrick and Liao, W. and Choudhary, Alok}, title = {Community Dynamics and Analysis of Decadal Trends in Climate Data}, year = {2011}, booktitle = {3rd IEEE ICDM Workshop on Knowledge Discovery from Climate Data, (ClimKD)}, pages = {9--14} }
Bioinformatics
Y. Zhang, S. Misra, A. Agrawal, M. M. A. Patwary, W. Liao, Z. Qin, and A. Choudhary, “Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU’s power,” BMC Bioinformatics, vol. 13, no. Suppl 5, p. S3, 2012. [url] [bib]
@article{ZMA12, title = {Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power}, author = {Zhang, Yuhong and Misra, Sanchit and Agrawal, Ankit and Patwary, Md Mostofa A and Liao, W. and Qin, Zhiguang and Choudhary, Alok}, journal = {BMC Bioinformatics}, volume = {13}, number = {Suppl 5}, pages = {S3}, year = {2012}, publisher = {BioMed Central Ltd} }Y. Zhang, M. M. A. Patwary, S. Misra, A. Agrawal, W. Liao, Z. Qin, and A. Choudhary, “Par-psse: Software for pairwise statistical significance estimation in parallel for local sequence alignment,” International Journal of Digital Content Technology and its Applications (JDCTA), vol. 6, no. 5, pp. 200–208, 2012. [url] [bib]
@article{ZPM12, title = {Par-psse: Software for pairwise statistical significance estimation in parallel for local sequence alignment}, author = {Zhang, Yuhong and Patwary, Md Mostofa Ali and Misra, Sanchit and Agrawal, Ankit and Liao, W. and Qin, Zhiguang and Choudhary, Alok}, journal = {International Journal of Digital Content Technology and its Applications (JDCTA)}, volume = {6}, number = {5}, pages = {200--208}, year = {2012}, publisher = {Advanced Institute of Convergence IT} }Y. Zhang, F. Zhou, J. Gou, H. Xiao, Z. Qin, and A. Agrawal, “Accelerating Pairwise Statistical Significance Estimation Using NUMA Machine,” Journal of Computational Information Systems (JCIS), vol. 8, no. 9, pp. 3887–3894, 2012. [url] [bib]
@article{ZZG12, title = {Accelerating Pairwise Statistical Significance Estimation Using NUMA Machine}, author = {Zhang, Yuhong and Zhou, Fan and Gou, Jianping and Xiao, Hua and Qin, Zhiguang and Agrawal, Ankit}, journal = {Journal of Computational Information Systems (JCIS)}, volume = {8}, number = {9}, pages = {3887--3894}, year = {2012} }A. Agrawal, A. Choudhary, and X. Huang, “Sequence-Specific Sequence Comparison Using Pairwise Statistical Significance,” in Software Tools and Algorithms for Biological Systems, vol. 696, H. R. Arabnia, Ed. Springer, 2011, pp. 297–306. [url] [bib]
@inbook{ACH11, author = {Agrawal, Ankit and Choudhary, Alok and Huang, Xiaoqiu}, editor = {Arabnia, H. R.}, booktitle = {Software Tools and Algorithms for Biological Systems}, title = {Sequence-Specific Sequence Comparison Using Pairwise Statistical Significance}, volume = {696}, publisher = {Springer}, year = {2011}, series = {Advances in Experimental Medicine and Biology, AEMB}, pages = {297-306} }A. Agrawal and X. Huang, “Pairwise Statistical Significance of Local Sequence Alignment Using Sequence-Specific and Position-Specific Substitution Matrices,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 1, pp. 194–205, 2011. [url] [bib]
@article{AH11, author = {Agrawal, Ankit and Huang, Xiaoqiu}, title = {Pairwise Statistical Significance of Local Sequence Alignment Using Sequence-Specific and Position-Specific Substitution Matrices}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, publisher = {IEEE}, volume = {8}, number = {1}, pages = {194--205}, year = {2011} }A. Agrawal, S. Misra, D. Honbo, and A. Choudhary, “Parallel pairwise statistical significance estimation of local sequence alignment using Message Passing Interface library,” Concurrency and Computation: Practice and Experience, vol. 23, no. 17, pp. 2269–2279, 2011. [url] [bib]
@article{AMH11, title = {Parallel pairwise statistical significance estimation of local sequence alignment using Message Passing Interface library}, author = {Agrawal, Ankit and Misra, Sanchit and Honbo, Daniel and Choudhary, Alok}, journal = {Concurrency and Computation: Practice and Experience}, year = {2011}, volume = {23}, number = {17}, pages = {2269-2279}, publisher = {Wiley-Blackwell} }S. Misra, A. Agrawal, W. Liao, and A. Choudhary, “Anatomy of a hash-based long read sequence mapping algorithm for next generation DNA sequencing,” Bioinformatics, vol. 27, no. 2, pp. 189–195, 2011. [url] [bib]
@article{MAL11, title = {Anatomy of a hash-based long read sequence mapping algorithm for next generation DNA sequencing}, author = {Misra, Sanchit and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, journal = {Bioinformatics}, volume = {27}, number = {2}, pages = {189--195}, year = {2011}, publisher = {Oxford Univ Press} }Y. Zhang, S. Misra, D. Honbo, A. Agrawal, W. Liao, and A. Choudhary, “Efficient pairwise statistical significance estimation for local sequence alignment using GPU,” in IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2011, pp. 226–231. [url] [bib]
@inproceedings{ZMH11, title = {Efficient pairwise statistical significance estimation for local sequence alignment using GPU}, author = {Zhang, Yuhong and Misra, Sanchit and Honbo, Daniel and Agrawal, Ankit and Liao, W. and Choudhary, Alok}, booktitle = {IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)}, pages = {226--231}, year = {2011}, organization = {IEEE} }Y. Zhang, M. Patwary, S. Misra, A. Agrawal, W. Liao, and A. N. Choudhary, “Enhancing parallelism of pairwise statistical significance estimation for local sequence alignment,” in HiPC Workshop on Hybrid Multi-core Computing (WHMC), 2011, pp. 1–8. [bib]
@inproceedings{ZPM11, title = {Enhancing parallelism of pairwise statistical significance estimation for local sequence alignment}, author = {Zhang, Yuhong and Patwary, M and Misra, Sanchit and Agrawal, Ankit and Liao, W. and Choudhary, Alok N}, booktitle = {HiPC Workshop on Hybrid Multi-core Computing (WHMC)}, pages = {1--8}, year = {2011} }A. Agrawal, A. Choudhary, and X. Huang, “Derived Distribution Points Heuristic for Fast Pairwise Statistical Significance Estimation,” in Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology (BCB), 2010, pp. 312–321. [url] [bib]
@inproceedings{ACH10a, author = {Agrawal, Ankit and Choudhary, Alok and Huang, Xiaoqiu}, title = {Derived Distribution Points Heuristic for Fast Pairwise Statistical Significance Estimation}, year = {2010}, booktitle = {Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology (BCB)}, pages = {312-321} }A. Agrawal, A. Choudhary, and X. Huang, “Non-conservative Pairwise Statistical Significance of Local Sequence Alignment Using Position-Specific Substitution Matrices,” in Proceedings of BIOCOMP, 2010, pp. 262–268. [bib]
@inproceedings{ACH10b, author = {Agrawal, Ankit and Choudhary, Alok and Huang, Xiaoqiu}, title = {Non-conservative Pairwise Statistical Significance of Local Sequence Alignment Using Position-Specific Substitution Matrices}, year = {2010}, booktitle = {Proceedings of BIOCOMP}, pages = {262--268} }A. Agrawal, S. Misra, D. Honbo, and A. Choudhary, “MPIPairwiseStatSig: Parallel Pairwise Statistical Significance Estimation of Local Sequence Alignment,” in Proceedings of the HPDC Workshop on Emerging Computational Methods for the Life Sciences (ECMLS), 2010, pp. 470–476. [url] [bib]
@inproceedings{AMH10, author = {Agrawal, Ankit and Misra, Sanchit and Honbo, Daniel and Choudhary, Alok}, title = {MPIPairwiseStatSig: Parallel Pairwise Statistical Significance Estimation of Local Sequence Alignment}, year = {2010}, booktitle = {Proceedings of the HPDC Workshop on Emerging Computational Methods for the Life Sciences (ECMLS)}, pages = {470-476}, organization = {ACM} }A. Agrawal, A. Mittal, R. Jain, and R. Takkar, “Fuzzy-adaptive-thresholding-based exon prediction,” International Journal of Computational Biology and Drug Design, vol. 3, no. 4, pp. 311–333, 2010. [url] [bib]
@article{AMJ10, title = {Fuzzy-adaptive-thresholding-based exon prediction}, author = {Agrawal, Ankit and Mittal, Ankush and Jain, Rahul and Takkar, Raghav}, journal = {International Journal of Computational Biology and Drug Design}, volume = {3}, number = {4}, pages = {311--333}, year = {2010}, publisher = {Inderscience} }D. Honbo, A. Agrawal, and A. Choudhary, “Efficient pairwise statistical significance estimation using FPGAs,” in Proceedings of BIOCOMP, 2010, pp. 571–577. [bib]
@inproceedings{HAC10, author = {Honbo, Daniel and Agrawal, Ankit and Choudhary, Alok}, title = {Efficient pairwise statistical significance estimation using FPGAs}, year = {2010}, booktitle = {Proceedings of BIOCOMP}, pages = {571-577} }A. Agrawal and X. Huang, “Pairwise Statistical Significance of Local Sequence Alignment Using Multiple Parameter Sets and Empirical Justification of Parameter Set Change Penalty,” BMC Bioinformatics, vol. 10, no. Suppl 3, p. S1, 2009. [url] [bib]
@article{AH09a, author = {Agrawal, Ankit and Huang, Xiaoqiu}, title = {Pairwise Statistical Significance of Local Sequence Alignment Using Multiple Parameter Sets and Empirical Justification of Parameter Set Change Penalty}, year = {2009}, journal = {BMC Bioinformatics}, volume = {10}, number = {Suppl 3}, pages = {S1}, publisher = {BioMed Central Ltd} }A. Agrawal and X. Huang, “PSIBLAST_PairwiseStatSig: Reordering PSI-BLAST hits using pairwise statistical significance,” Bioinformatics, vol. 25, no. 8, pp. 1082–1083, 2009. [url] [bib]
@article{AH09b, author = {Agrawal, Ankit and Huang, Xiaoqiu}, title = {PSIBLAST_PairwiseStatSig: Reordering PSI-BLAST hits using pairwise statistical significance}, journal = {Bioinformatics}, volume = {25}, number = {8}, pages = {1082-1083}, year = {2009}, publisher = {Oxford Univ Press} }A. Agrawal, V. Brendel, and X. Huang, “Pairwise Statistical Significance Versus Database Statistical Significance for Local Alignment of Protein Sequences,” in Bioinformatics Research and Applications, 2008, vol. 4983, pp. 50–61. [url] [bib]
@inproceedings{ABH08a, author = {Agrawal, Ankit and Brendel, Volker and Huang, Xiaoqiu}, title = {Pairwise Statistical Significance Versus Database Statistical Significance for Local Alignment of Protein Sequences}, booktitle = {Bioinformatics Research and Applications}, year = {2008}, pages = {50-61}, volume = {4983}, series = {LNCS(LNBI)}, publisher = {Springer Berlin/Heidelberg}, editor = {Mandoiu, Ion and Sunderraman, Raj and Zelikovsky, Alexander} }A. Agrawal, V. P. Brendel, and X. Huang, “Pairwise Statistical Significance and Empirical Determination of Effective Gap Opening Penalties for Protein Local Sequence Alignment,” International Journal of Computational Biology and Drug Design, vol. 1, no. 4, pp. 347–367, 2008. [url] [bib]
@article{ABH08b, author = {Agrawal, Ankit and Brendel, Volker P. and Huang, Xiaoqiu}, title = {Pairwise Statistical Significance and Empirical Determination of Effective Gap Opening Penalties for Protein Local Sequence Alignment}, year = {2008}, journal = {International Journal of Computational Biology and Drug Design}, volume = {1}, number = {4}, pages = {347-367}, publisher = {Inderscience} }A. Agrawal, A. Ghosh, and X. Huang, “Estimating Pairwise Statistical Significance of Protein Local Alignments Using a Clustering-Classification Approach Based on Amino Acid Composition,” in Bioinformatics Research and Applications, 2008, vol. 4983, pp. 62–73. [url] [bib]
@inproceedings{AGH08, author = {Agrawal, Ankit and Ghosh, Arka and Huang, Xiaoqiu}, title = {Estimating Pairwise Statistical Significance of Protein Local Alignments Using a Clustering-Classification Approach Based on Amino Acid Composition}, booktitle = {Bioinformatics Research and Applications}, year = {2008}, pages = {62-73}, editor = {Mandoiu, Ion and Sunderraman, Raj and Zelikovsky, Alexander}, volume = {4983}, series = {LNCS(LNBI)}, publisher = {Springer Berlin / Heidelberg} }A. Agrawal and X. Huang, “Pairwise Statistical Significance of Local Sequence Alignment Using Multiple Parameter Sets,” in Proc. of ACM 2nd International Workshop on Data and Text Mining in Bioinformatics (DTMBIO), 2008, pp. 53–60. [url] [bib]
@inproceedings{AH08a, author = {Agrawal, Ankit and Huang, Xiaoqiu}, title = {Pairwise Statistical Significance of Local Sequence Alignment Using Multiple Parameter Sets}, year = {2008}, booktitle = {Proc. of ACM 2nd International Workshop on Data and Text Mining in Bioinformatics (DTMBIO)}, pages = {53-60} }A. Agrawal and X. Huang, “Pairwise Statistical Significance of Local Sequence Alignment Using Substitution Matrices with Sequence-Pair-Specific Distance,” in Proc. of International Conference on Information Technology, ICIT, 2008, pp. 94–99. Won the best student paper award. [url] [bib]
@inproceedings{AH08b, title = {Pairwise Statistical Significance of Local Sequence Alignment Using Substitution Matrices with Sequence-Pair-Specific Distance}, author = {Agrawal, Ankit and Huang, Xiaoqiu}, booktitle = {Proc. of International Conference on Information Technology, ICIT}, year = {2008}, note = {**Won the best student paper award**}, pages = {94-99} }A. Agrawal and X. Huang, “Pairwise DNA Alignment with Sequence Specific Transition-Transversion Ratio Using Multiple Parameter Sets,” in Proc. of International Conference on Information Technology, ICIT, 2008, pp. 89–93. [url] [bib]
@inproceedings{AH08c, title = {Pairwise DNA Alignment with Sequence Specific Transition-Transversion Ratio Using Multiple Parameter Sets}, author = {Agrawal, Ankit and Huang, Xiaoqiu}, booktitle = {Proc. of International Conference on Information Technology, ICIT}, pages = {89--93}, year = {2008}, organization = {IEEE} }A. Agrawal and X. Huang, “Conservative, Non-conservative and Average Pairwise Statistical Significance of Local Sequence Alignment,” in Proc. of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2008, pp. 433–436. [url] [bib]
@inproceedings{AH08d, title = {Conservative, Non-conservative and Average Pairwise Statistical Significance of Local Sequence Alignment}, author = {Agrawal, Ankit and Huang, Xiaoqiu}, booktitle = {Proc. of IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, year = {2008}, pages = {433-436} }A. Agrawal and X. Huang, “DNAlignTT: Pairwise DNA Alignment with Sequence Specific Transition-Transversion Ratio,” in Proc. of IEEE International Conference on Electro/Information Technology (EIT), 2008, pp. 453–455. [url] [bib]
@inproceedings{AH08e, author = {Agrawal, Ankit and Huang, Xiaoqiu}, title = {DNAlignTT: Pairwise DNA Alignment with Sequence Specific Transition-Transversion Ratio}, booktitle = {Proc. of IEEE International Conference on Electro/Information Technology (EIT)}, year = {2008}, pages = {453--455} }A. Agrawal and S. K. Khaitan, “A new heuristic for multiple sequence alignment,” in Proc. of IEEE International Conference on Electro/Information Technology (EIT) (Poster paper), 2008, pp. 215–217. [url] [bib]
@inproceedings{AK08, title = {A new heuristic for multiple sequence alignment}, author = {Agrawal, Ankit and Khaitan, Siddhartha K}, booktitle = {Proc. of IEEE International Conference on Electro/Information Technology (EIT) (Poster paper)}, pages = {215--217}, year = {2008}, organization = {IEEE} }A. Agrawal, A. Mittal, R. Jain, and R. Takkar, “An adaptive fuzzy thresholding algorithm for exon prediction,” in Proc. of IEEE International Conference on Electro/Information Technology (EIT) (Poster paper), 2008, pp. 211–214. [url] [bib]
@inproceedings{AMJ08, title = {An adaptive fuzzy thresholding algorithm for exon prediction}, author = {Agrawal, Ankit and Mittal, Ankush and Jain, Rahul and Takkar, Raghav}, booktitle = {Proc. of IEEE International Conference on Electro/Information Technology (EIT) (Poster paper)}, pages = {211--214}, year = {2008}, organization = {IEEE} }A. Agrawal and A. Mittal, “A dynamic time-lagged correlation based method to learn multi-time delay gene networks,” World Academy of Science, Engineering and Technology, vol. 9, pp. 723–730, 2007. [bib]
@article{AM07, title = {A dynamic time-lagged correlation based method to learn multi-time delay gene networks}, author = {Agrawal, Ankit and Mittal, Ankush}, journal = {World Academy of Science, Engineering and Technology}, volume = {9}, pages = {723--730}, year = {2007} }A. Agrawal, A. Mittal, and S. Gupta, “A Restrictive Mining Algorithm to Learn Multi Time Delay Gene Networks,” Bioinformatics Trends - A Journal of Bioinformatics and Its Applications, vol. 2, no. 3, 4, pp. 125–140, 2007. [bib]
@article{AMG07, author = {Agrawal, Ankit and Mittal, Ankush and Gupta, Sumit}, title = {A Restrictive Mining Algorithm to Learn Multi Time Delay Gene Networks}, year = {2007}, journal = {Bioinformatics Trends - A Journal of Bioinformatics and Its Applications}, volume = {2}, number = {3,4}, pages = {125-140} }A. Agrawal and A. Mittal, “Identifying temporal gene networks using signal processing metrics on time-series gene expression data,” in Proc. of 3rd International Conference on Intelligent Sensing and Information Processing (ICISIP), 2005, pp. 86–92. [url] [bib]
@inproceedings{AM05, title = {Identifying temporal gene networks using signal processing metrics on time-series gene expression data}, author = {Agrawal, A and Mittal, A}, booktitle = {Proc. of 3rd International Conference on Intelligent Sensing and Information Processing (ICISIP)}, pages = {86--92}, year = {2005}, organization = {IEEE} }A. Agrawal, A. Mittal, and S. Gupta, “Identifying Temporal Gene Networks by Mining Gene Expression Data,” in Proc. of 12th International Conference on Advanced Computing and Communications (ADCOM), 2004, pp. 194–200. [bib]
@inproceedings{AMG04, title = {Identifying Temporal Gene Networks by Mining Gene Expression Data}, author = {Agrawal, Ankit and Mittal, Ankush and Gupta, Sumit}, booktitle = {Proc. of 12th International Conference on Advanced Computing and Communications (ADCOM)}, pages = {194--200}, year = {2004} }
Miscellaneous
A. Mittal, S. Gupta, A. Agrawal, and L. F. Cheong, “Camera Motion Characterization of Establishment Shots and Video Signatures,” in Proc. of 12th International Conference on Advanced Computing and Communications (ADCOM), 2004, pp. 663–668. [bib]
@inproceedings{MGA04, title = {Camera Motion Characterization of Establishment Shots and Video Signatures}, author = {Mittal, Ankush and Gupta, Sumit and Agrawal, Ankit and Cheong, LF}, booktitle = {Proc. of 12th International Conference on Advanced Computing and Communications (ADCOM)}, pages = {663--668}, year = {2004} }