Journal Articles | Vipin Kumar

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2025

An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw Cycle. D Kumawat, A Ebtehaj, X Xu, A Colliander, V Kumar. IEEE Transactions on Geoscience and Remote Sensing.

Time series predictions in unmonitored sites: A survey of machine learning techniques in water resources. JD Willard, C Varadharajan, X Jia, V Kumar. Environmental Data Science 4, e7.

2024

Machine learning applications in vadose zone hydrology: A review. X Li, JL Nieber, V Kumar. Vadose Zone Journal 23 (4), e20361.

Knowledge-Guided Machine Learning for Real-Time Flood Forecasting and Data Assimilation. ZP McEachran, R Ghosh, A Renganathan, S Sharma, B Connelly, C Duffy, JL Nieber, M Steinbach, V Kumar. Water Science Conference (WaterSciCon24, 307-02).

Global precipitation nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net convolutional LSTM architecture. R Rahimi, P Ravirathinam, A Ebtehaj, A Behrangi, J Tan, V Kumar. Journal of Hydrometeorology 25 (6), 947-963.

Knowledge-guided machine learning: Current trends and future prospects. A Karpatne, X Jia, V Kumar. arXiv preprint arXiv:2403.15989.

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems. L Liu, W Zhou, K Guan, B Peng, S Xu, J Tang, Q Zhu, J Till, X Jia, C Jiang, S Wang, Z Qin, H Kong, R Grant, S Mezbahuddin, V Kumar, Z Jin. Nature communications Vol 15 (1), p. 357.

Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling. S Xu, A Khandelwal, A Renganathan, V Kumar. Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) (307-315).

Combining satellite and weather data for crop type mapping: An inverse modelling approach. P Ravirathinam, R Ghosh, A Khandelwal, X Jia, D Mulla, V Kumar. Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) (445-453).

Towards Entity-Aware Conditional Variational Inference for Heterogeneous Time-Series Prediction: An application to Hydrology. R Ghosh, A Renganathan, W McAliley, M Steinbach, C Duffy, V Kumar. Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) (334-342).

Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management. SS Chatterjee, K Lindsay, N Chatterjee, R Patil, I Altintas De Callafon, M Steinbach, D Giron, MH Nguyen, V Kumar. Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) (589-597).

An approach towards including watershed traits in machine learning models for predictions in unmonitored basins. C Varadharajan, JD Willard, F Ciulla, H Weierbach, AR Lima, N Bouskill, E Brodie, V Kumar. 104th Annual AMS Meeting 2024 Vol. 104, p. 436921.

2023

A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest. Q Yang, L Liu, J Zhou, R Ghosh, B Peng, K Guan, J Tang, W Zhou, V Kumar, Z Jin. Remote sensing of environment, Vol. 299.

Knowledge-Guided Machine Learning: A New Framework for Accelerating Scientific Discovery and Addressing Global Environmental Challenges. Vipin Kumar. 2023 IEEE International Conference on Big Data (BigData).

Koopman invertible autoencoder: Leveraging forward and backward dynamics for temporal modeling. K Tayal, A Renganathan, R Ghosh, X Jia, V Kumar. 2023 IEEE International Conference on Data Mining (ICDM), pp 588-597.

A deep transfer learning framework for mapping high spatiotemporal resolution LAI. J Zhou, Q Yang, L Liu, Y Kang, X Jia, M Chen, R Ghosh, Sh Xu, Ch Jiang, K Guan, V Kumar, Z Jin. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 206, pp 30-48.

On computing paradigms-where will large language models be going. X Wu, X Zhu, E Baralis, R Lu, V Kumar, L Rutkowski, J Tang. 2023 IEEE International Conference on Data Mining (ICDM), pp 1577-1582.

Uncertainty Quantification in Inverse Models in Hydrology. S Sharma Chatterjee, R Ghosh, A Renganathan, X Li, SChatterjee, J Nieber, C Duffy, V Kumar. arXiv e-prints (arXiv: 2310.02193).

Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management. S Sharma Chatterjee, K Lindsay, N Chatterjee, R Patil, I Altintas De Callafon, M Steinbach, D Giron, MH Nguyen, V Kumar. arXiv e-prints (arXiv: 2310.01593).

Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin. L Yin, R Ghosh, C Lin, D Hale, C Weigl, J Obarowski, J Zhou, J Till, X Jia, N You, T Mao, V Kumar, Z Jin. Remote Sensing of Environment, Vol 295, pp. 113695.

A scalable framework for quantifying field-level agricultural carbon outcomes. K Guan, Z Jin, B Peng, J Tang, EH DeLucia, PC West, C Jiang, S Wang, T Kim, W Zhou, T Griffis, L Liu, W H Yang, Z Qin, Q Yang, A Margenot, ER Stuchiner, VKumar, C Bernacchi, J Coppess, KA Novick, J Gerber, M Jahn, Khanna, D Lee, Z Chen, S-J Yang. Earth-Science Reviews, Vol 243, pp. 104462.

The future of fundamental science led by generative closed-loop artificial intelligence. H Zenil, J Tegnér, FS Abrahão, A Lavin, V Kumar, J G Frey, A Weller, L Soldatova, AR Bundy, NR Jennings, KTakahashi, LHunter, SDzeroski, ABriggs, FD Gregory, CP Gomes, J Rowe, J Evans, H Kitano, R King. arXiv preprint arXiv:2307.07522.

Knowledge-based Artificial Intelligence for Agroecosystem Carbon Budget and Crop Yield Estimation. L Liu, W Zhou, K Guan, B Peng, C Jiang, J Tang, S Wang, R Grant, S Mezbahuddin, X Jia, S Xu, V Kumar, Z Jin. ESS Open Archive eprints, Vol 105, essoar. 10509206.

Cyberinfrastructure for sustainability sciences. CX Song, V Merwade, S Wang, M Witt, V Kumar, E Irwin, L Zhao, A Walton. Environmental Research Letters, Vol 18 (7), p. 075002.

Causal Structure Learning from Imperfect Longitudinal Data in Healthcare. H Yang, R Tourani, J Li, P Caraballo, M Steinbach, V Kumar, G Simon. 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), pp 1-11.

Bayesian federated learning: A survey. L Cao, H Chen, X Fan, J Gama, YS Ong, V Kumar. arXiv preprint arXiv:2304.13267.

Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions. JA Zwart, SK Oliver, WD Watkins, JM Sadler, AP Appling, HR Corson‐Dosch, X Jia, V Kumar, JS Read. JAWRA Journal of the American Water Resources Association, Vol 59 (2), pp 317-337.

Entity aware modelling: A survey. R Ghosh, H Yang, A Khandelwal, E He, A Renganathan, S Sharma, X Jia, V Kumar. arXiv preprint arXiv:2302.08406.

Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Heterogeneous Systems. A Renganathan, R Ghosh, A Khandelwal, V Kumar. CoRR Computer Research Repository.

Probabilistic inverse modeling: An application in hydrology. S Sharma, R Ghosh, A Renganathan, X Li, S Chatterjee, J Nieber, C Duffy, V Kumar. Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), pp 847-855.

Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets. P Ravirathinam, R Ghosh, K Wang, K Xuan, A Khandelwal, H Dugan, P Hanson, V Kumar. Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), pp 487-495.

Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications. S Xu, A Khandelwal, X Li, X Jia, L Liu, J Willard, R Ghosh, K Cutler, M Steinbach, C Duffy, J Nieber, V Kumar. Proceedings of the 2023 SIAM international conference on data mining (SDM), pp 649-657.

2022

A framework for scalably quantifying field-level agricultural carbon outcomes. K Guan, Z Jin, EH DeLucia, W Paul, B Peng, J Tang, C Jiang, S Wang, T Kim, W Zhou, T Griffis, L Liu, Z Qin, AJ Margenot, V Kumar, CJ Bernacchi, WH Yang, D Lee, JW Coppess, JG Gerber, M Jahn, M Khanna, S-Jen Yang. Earth ArXiv, 2022/12/16.

2021

Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, Vipin Kumar. ACM Transactions on Data Science (Accepted for publication) 2021 https://arxiv.org/pdf/2001.11086.pdf

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. Yolanda Gil, Daniel Garijo, Deborah Khider, Craig Knoblock, Varun Ratnakar, Maximiliano Osorio, Hernan Vargas, Minh Pham, Jay Pujara, Basel Shbita, Binh Vu, Yao-Yi Chiang, Dan Feldman, Yijun Lin, Hayley Song, Vipin Kumar, Ankush Khandelwal, Michael Steinbach, Kshitij Tayal, Shaoming Xu, Suzanne Pierce, Lissa Pearson, Daniel Hardesty-Lewis, Ewa Deelman, Rafael Ferreira da Silva, Rajiv Mayani, Armen Kemanian, Yuning Shi, Lorne Leonard, Scott Peckham, Maria Stoica, Kelly Cobourn, Zeya Zhang, Christopher Duffy, and Lele Shu. ACM Transactions on Interactive Intelligent Systems, (Accepted for publication) 2021.

Xiaowei Jia, Jacob Zwart, Jeffery Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, and Vipin Kumar. "Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks." SIAM International Conference on Data Mining, April 2021.

2020

Model-agnostic Methods for Text Classification with Inherent Noise. Kshitij Tayal, Rahul Ghosh, Vipin Kumar. Proceedings of the 28th International Conference on Computational Linguistics: Industry Track. Pp. 202-213, December 2020.

Regularized Graph Convolutional Networks for Short Text Classification. Kshitij Tayal, Nikhil Rao, Saurabh Agarwal, Xiaowei Jia, Karthik Subbian, Vipin Kumar. Proceedings of the 28th International Conference on Computational Linguistics: Industry Track. Pp. 236-242, December 2020.

Predicting diabetes clinical outcomes using longitudinal risk factor trajectories. Gyorgy J Simon, Kevin A Peterson, M Regina Castro, Michael S Steinbach, Vipin Kumar, Pedro J Caraballo. BMC medical informatics and decision making, Volume 20, Issue 1, pp 1-9. December 2020.

Teaching deep learning causal effects improves predictive performance. Jia Li, Xiaowei Jia, Haoyu Yang, Vipin Kumar, Michael Steinbach, Gyorgy Simon. arXiv:2011.05466, November 2020.

Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning. Jared D Willard, Jordan S Read, Alison P Appling, Samantha K Oliver, Xiaowei Jia, Vipin Kumar. arXiv, November 2020.

Learning with Small Data. Huaxiu Yao, Xiaowei Jia, Vipin Kumar, Zhenhui Li. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 3539-3540, August 2020.

Personalized Image Retrieval with Sparse Graph Representation Learning. Xiaowei Jia, Handong Zhao, Zhe Lin, Ajinkya Kale, Vipin Kumar. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2735-2743, August 2020.

Predicting lake surface water phosphorus dynamics using process-guided machine learning. Paul C Hanson, Aviah B Stillman, Xiaowei Jia, Anuj Karpatne, Hilary A Dugan, Cayelan C Carey, Joseph Stachelek, Nicole K Ward, Yu Zhang, Jordan S Read, Vipin Kumar. Ecological Modelling Volume 430, August 2020.

Global River Monitoring Using Semantic Fusion Networks. Zhihao Wei, Kebin Jia, Xiaowei Jia, Ankush Khandelwal, Vipin Kumar. Water, 12(8), 2258, August 2020.

End-to-End Learning for Phase Retrieval. Raunak Manekar, Kshitij Tayal, Vipin Kumar, Ju Sun. ICML workshop on ML Interpretability for Scientific Discovery, July 2020.

Inverse Problems, Deep Learning, and Symmetry Breaking. Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar, Ju Sun. ICML workshop on ML Interpretability for Scientific Discovery, July 2020.

Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data. Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Varun Mithal, Vipin Kumar. SIAM International Conference on Data Mining (SDM20), May 2020.

Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018. Riccardo Tortini, Nina Noujdina, Samantha Yeo, Martina Ricko, Charon M. Birkett, Ankush Khandelwal, Vipin Kumar, Miriam E. Marlier, Dennis P. Lettenmaier.
Earth System Science Data, 12: 2, 1–11, 2020.

Integrating Physics-Based Modeling with Machine Learning: A Survey. Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar. April 2020.

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations. Xiaowei Jia, Ankush Khandelwal, Kimberly M. Carlson, James S. Gerber, Paul C. West, Leah H. Samberg, Vipin Kumar. Remote Sensing 2020, 12(4), 636.

Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, Vipin Kumar. January 2020.

Predicting diabetes clinical outcomes using longitudinal risk factor trajectories. Gyorgy J Simon, Kevin A Peterson, M Regina Castro, Michael S Steinbach, Vipin Kumar, Pedro J Caraballo. BMC Medical Informatics and Decision Making, vol 20, 1-9.

Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data. Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Varun Mithal, Vipin Kumar. January, 2020.

2019

Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale. Xiaowei Jia, Ankush Khandelwal, David J Mulla, Philip G Pardey, Vipin Kumar. Agricultural Economics, Volume 50, Issue S1, October 2019.

Discovering genetic interactions bridging pathways in genome-wide association studies. Gang Fang, Wen Wang, Vanja Paunic, Hamed Heydari, Michael Costanzo, Xiaoye Liu, Xiaotong Liu, Benjamin VanderSluis, Benjamin Oately, Michael Steinbach, Brian Van Ness, Eric E Schadt, Nathan D Pankratz, Charles Boone, Vipin Kumar, Chad L Myers. Nature Communications volume 10, Article number: 4274, 2019.

Evaluating the Impact of Data Representation on EHR-Based Analytic Tasks. Wonsuk Oh, Michael S Steinbach, M Regina Castro, Kevin A Peterson, Vipin Kumar, Pedro J Caraballo, Gyorgy J Simon. 17th World Congress on Medical and Health Informatics, MEDINFO 2019, pages 288-292.

Theory-guided data science improves understanding and predictions of lake phosphorus dynamics. Paul C Hanson, Cayelan C Carey, Xiaowei Jia, Vipin Kumar. 2019 ESA Annual Meeting. August 11-16, 2019.

Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning. Xiaowei Jia, Ankush Khandelwal, Vipin Kumar. arXiv, April 2019.

GLADD-R: A new Global Lake Dynamics Database for Reservoirs created using machine learning and satellite data. Ankush Khandelwal, Anuj Karpatne, Zhihao Wei, Huangying Kuang, Rahul Ghosh, Hilary Dugan, Paul Hanson, Vipin Kumar. April 2019.

An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models. Daniel Garijo, Deborah Khider, Varun Ratnakar, Yolanda Gil, Ewa Deelman, Rafael Ferreira Da Silva, Craig Knoblock, Yao-Yi Chiang, Minh Pham, Jay Pujara, Binh Vu, Dan Feldman, Rajiv Mayani, Kelly Cobourn, Chris Duffy, Armen Kemanian, Lele Shu, Vipin Kumar, Ankush Khandelwal, Kshitij Tayal, Scott Peckham, Maria Stoica, Anna Dabrowski, Daniel Hardesty-Lewis, Suzanne Pierce. Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion, 111-112, March 2019. 111-112.

Neurodevelopmental heterogeneity and computational approaches for understanding autism. Suma Jacob, Jason J Wolff, Michael S Steinbach, Colleen B Doyle, Vipan Kumar, Jed T Elison. Translational Psychiatry, Vol. 9, pages 1-12.

Physics guided ML: Emerging AI opportunities for weather and climate. Vipin Kumar, Valliappa Lakshmanan, Lucas Joppa, Gregory Dudek, Surya Karthik Mukkavilli, Amy McGovern. 99th American Meteorological Society Annual Meeting, January 2019.

EnviroNet: ImageNet analog for environment & global AI challenge. Gregory Dudek, Lucas Joppa, Valliappa Lakshmanan, Vipin Kumar, Surya Karthik Mukkavilli. 99th American Meteorological Society Annual Meeting, January 2019.

Climate Change & AI: Present and potential role of AI in assessment and response. Lucas Joppa, Valliappa Lakshmanan, Vipin Kumar, Gregory Dudek, Surya Karthik Mukkavilli, Philippe Tissot. 99th American Meteorological Society Annual Meeting, January 2019.

Plantation Mapping in Southeast Asia. Xiaowei Jia, Ankush Khandelwal, Kimberly Carlson, James S Gerber, Paul C West, Vipin Kumar. Frontiers in Big Data, 2019.

Intelligent systems for geosciences: an essential research agenda. Yolanda Gil, Suzanne A Pierce, Hassan Babaie, Arindam Banerjee, Kirk Borne, Gary Bust, Michelle Cheatham, Imme Ebert-Uphoff, Carla Gomes, Mary Hill, John Horel, Leslie Hsu, Jim Kinter, Craig Knoblock, David Krum, Vipin Kumar, Pierre Lermusiaux, Yan Liu, Chris North, Victor Pankratius, Shanan Peters, Beth Plale, Allen Pope, Sai Ravela, Juan Restrepo, Aaron Ridley, Hanan Samet, Shashi Shekhar. Communications of the ACM, January 2019.

A Framework for Visualizing Data Quality for Predictive Models and Clinical Quality Measures. Steven G Johnson, Lisiane Pruinelli, Alexander Hoff, Vipin Kumar, György J Simon, Michael Steinbach, Bonnie L Westra. AMIA Summits on Translational Science Proceedings, May,2019.

Spatio-temporal classification at multiple resolutions using multi-view regularization. G. Nayak, R. Ghosh, V. Mithal, X. Jia, V. Kumar. 2019 IEEE International Conference on Big Data (Big Data), October, 2019.

Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring. X. Jia, M. Wang, A. Khandelwal, A. Karpatne, V. Kumar. Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 2628-2634. AAAI Press, August 2019.

Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training? X. Jia, S. Li, H. Zhao, S. Kim,V. Kumar. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1665-1673. ACM, August 2019.

A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series. S. Agrawal, M. Steinbach, D. Boley, S. Chatterjee, G. Atluri, A. T. Dang, S. Liess, V. Kumar. Proceedings of the 36th International Conference on Machine Learning, June 2019.

Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles. X. Jia, J. Willard, A. Karpatne, J. Read, J. Zwart, M. Steinbach, V. Kumar. Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 558-566. Society for Industrial and Applied Mathematics. May 2019.

Classifying Heterogeneous Sequential Data by Cyclic Domain Adaptation: An Application in Land Cover Detection. X. Jia, G. Nayak, A. Khandelwal, A. Karpatne, V. Kumar. Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 540-548, May 2019.

Spatial Context-Aware Networks for Mining Temporal Discriminative Period in Land Cover Detection. X. Jia, S. Li, A. Khandelwal, G. Nayak, A. Karpatne, V. Kumar. Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 513-521, May 2019.

Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks. S. Agrawal, M. Steinbach, D. Boley, S. Chatterjee, G. Atluri, A. T. Dang, S. Liess, V. Kumar. IEEE Transactions on Knowledge and Data Engineering, April 2019.

Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles. Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, Vipin Kumar. Proceedings of the 2019 SIAM International Conference on Data Mining, May 2019.

Process-Guided Deep Learning Predictions of Lake Water Temperature. Jordan S. Read, Xiaowei Jia, Jared Willard, Alison P. Appling, Jacob A. Zwart, Samantha K. Oliver, Anuj Karpatne, Gretchen J.A. Hansen, Paul C. Hanson, William Watkins, Michael Steinbach, Vipin Kumar. 2019. Water Resources Research (55).

2018

Intelligent systems for geosciences: an essential research agenda. Yolanda Gil, Suzanne A. Pierce, Hassan Babaie, Arindam Banerjee, Kirk Borne, Gary Bust, Michelle Cheatham, Imme Ebert-Uphoff, Carla Gomes, Mary Hill, John Horel, Leslie Hsu, Jim Kinter, Craig Knoblock, David Krum, Vipin Kumar, Pierre Lermusiaux, Yan Liu, Chris North, Victor Pankratius, Shanan Peters, Beth Plale, Allen Pope, Sai Ravela, Juan Restrepo, Aaron Ridley, Hanan Samet, and Shashi Shekhar. 2018.Communications of the ACM 62 (1), 76-84.

Computational Approaches for Early Emerging Heterogeneity and Disorder Risk. S Jacob, J Wolff, C Doyle, M Steinbach, V Kumar, J Elison. Neuropsychopharmacology 43 (Suppl 1), 77–227, M37, 2018.

Don't Do Imputation: Dealing with Informative Missing Values in EHR Data Analysis. J Li, M Wang, MS Steinbach, V Kumar, GJ Simon. 2018 IEEE International Conference on Big Knowledge (ICBK), 415-422.

Spatio-temporal data mining: A survey of problems and methods. G Atluri, A Karpatne, V Kumar. ACM Computing Surveys (CSUR) 51 (4), 1-41.

Machine learning for the geosciences: Challenges and opportunities. A Karpatne, I Ebert-Uphoff, S Ravela, HA Babaie, V Kumar.
IEEE Transactions on Knowledge and Data Engineering 31 (8), 1544-1554.

Classifying Multivariate Time Series by Learning Sequence-level Discriminative Patterns. G. Nayak , V. Mithal , X. Jia , and V. Kumar. Proceedings of the 2018 SIAM International Conference on Data Mining, May 2018.

Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework. V. Mithal , G. Nayak , A. Khandelwal , V. Kumar , R. Nemani , and N. Oza. Remote Sensing 2018, 10(1), 69.

A cautionary note on decadal sea level pressure predictions from GCMs. S. Liess , P. Snyder , A. Kumar , and V. Kumar.
Advances in Climate Change Research, Volume 9, Issue 1, March 2018, Pages 43-56.

Mining Sub-Interval Relationships In Time Series Data. S Agrawal, S Verma, G Atluri, A Karpatne, S Liess, A Macdonald III, Stefan Liess, Angus Macdonald III, Snigdhansu Chatterjee, Vipin Kumar. arXiv: 1802.06095.

Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience. S Agrawal, M Steinbach, D Boley, S Liess, S Chatterjee, V Kumar, G Atluri. IEEE Transactions on Knowledge and Data Engineering 30 (7), 1254-1267.

Mining Electronic Health Records (EHRs): A Survey. ACM Computing Surveys (CSUR), Volume 50, Issue 6, Article No.: 85, pp 1 - 40, 2018.

2017

Predict land covers with transition modeling and incremental learning. X. Jia , A. Khandelwal , G. Nayak , J. Gerber , K. Carlson , P. West , and V. Kumar. Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 171-179.

Tripoles: A New Class of Relationships in Time Series Data. S. Agrawal, G. Atluri, A. Karpatne,W. Haltom, S. Liess, S. Chatterjee, V. Kumar. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 697-706. August 2017.

Incremental Dual-memory LSTM in Land Cover Prediction. X. Jia , A. Khandelwal , G. Nayak , J. Gerber , K. Carlson , P. West , and V. Kumar. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 867-876, 2017.

Joint Sparse Auto-encoder: A Semi-supervised Spatio-temporal Approach in Mapping Large-scale Croplands. X. Jia , A. Khandelwal, A. Karpatne , and V. Kumar. IEEE International Conference on Big Data (Big Data), 2017.

An approach for global monitoring of surface water extent variations in reservoirs using MODIS data. A. Khandelwal, A. Karpatne, M.E. Marlier, J. Kim, D.P. Lettenmaier, and V. Kumar. Remote Sensing of Environment, Volume 202, pp 113-128, 2017.

RAPT: Rare Class Prediction in Absence of True Labels. V. Mithal, G. Nayak, A. Khandelwal, V. Kumar, N. Oza, and R. Nemani. IEEE Transactions on Knowledge and Data Engineering 29, no. 11, 2017: 2484-2497.

Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data. A. Karpatne , G. Atluri, J. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, and V. Kumar. IEEE Transactions on Knowledge and Data Engineering, 2017: 2318 - 2331.

A Teleconnection between the West Siberian Plain and the ENSO Region. S. Liess, S. Agrawal, S. Chatterjee, and V. Kumar.
Journal of Climate, Volume 30, Issue 1, pp. 301-315. 2017.

Active Learning. Varun Chandola, Arindam Banerjee,Vipin Kumar. Encyclopedia of Machine Learning and Data Mining. 2017, pp 42-56.

High spatiotemporal resolution of river planform dynamics from Landsat: The RivMAP toolbox and results from the Ucayali River.
Jon Schwenk, Ankush Khandelwal, Mulu Fratkin, Vipin Kumar, Efi Foufoula‐Georgiou. Earth and Space Science 4 (2), 46-75.

Data science for food, energy and water: A workshop report. Naoki Abe, Yiqun Xie, Shashi Shekhar, Chid Apte, Vipin Kumar, Mitch Tuinstra, Raju Vatsavai. ACM SIGKDD Explorations Newsletter, Volume 18, Issue 2: pp 1 - 4, March 2017.

River morphodynamics from space: the Landsat frontier. J. Schwenk, A. Khandelwal, M. Fratkin, V. Kumar, E. Foufoula-Georgiou.
Proceedings of the 19th EGU General Assembly, EGU2017, pp. 11858. April 2017.

Big Data in Climate: Opportunities and Challenges for Machine Learning. Anuj Karpatne,Vipin Kumar. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery. August 2017. pp. 21-22.

A vision for the development of benchmarks to bridge geoscience and data science. Imme Ebert-Uphoff, David R. Thompson, Ibrahim Demir, Yulia R. Gel, Mary C. Hill, Anuj Karpatne, Mariana Guereque, Vipin Kumar, Enrique Cabral-Cano, Padhraic Smyth. Proceedings of the 17th International Workshop on Climate informatics. September 2017.

Quantifying the Effect of Data Quality on the Validity of an eMeasure. Steven G. Johnson, Stuart Speedie, Gyorgy Simon, Vipin Kumar, Bonnie L. Westra. Appl Clin Inform. 2017 Oct; 8(4): 1012–1021.

Orbit: Ordering based information transfer across space and time for global surface water monitoring. Ankush Khandelwal, Anuj Karpatne, Vipin Kumar. arXiv, November 2017.

2016

Learning large-scale plantation mapping from imperfect annotators. X. Jia, A. Khandelwal, J. Gerber, K. Carlson, P. West, and V. Kumar. 2016 IEEE International Conference on Big Data (Big Data).

Identifying dynamic changes with noisy labels in spatial-temporal data: A study on large-scale water monitoring application. X. Jia, X. Chen, A. Karpatne, and V. Kumar. 2016 IEEE International Conference on Big Data (Big Data).

A General Framework to Increase the Robustness of Model-based Change Point Detection Algorithms to Outliers and Noise. X. Chen, Y. Yao, S. Shi, V. Kumar, S. Chatterjee, and J. Faghmous. Proceedings of the 2016 SIAM International Conference on Data Mining, 2016.

Causality-Guided Feature Selection. M. Chaudhary, D. Gonzalez II, G. Bello, M. Angus, D. Desai, S. Harenberg, P. Doraiswamy, F. Semazzi, V. Kumar, and N. Samatova. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science, 2016.

Monitoring Land-Cover Changes: A Machine-Learning Perspective. A. Karpatne, Z. Jiang, R. Vatsavai, S. Shekhar, and V. Kumar.
IEEE Geoscience and Remote Sensing Magazine 4(2): 8-21. 2016.

Global monitoring of inland water dynamics: State-of-the-art, challenges, and opportunities. A. Karpatne, A. Khandelwal, X. Chen, V. Mithal, J.H. Faghmous, and V. Kumar. In: Lässig, J., Kersting, K., Morik, K. (eds) Computational Sustainability. Studies in Computational Intelligence, vol 645. Springer, Cham.

Exploring the Predictability of 30-Day Extreme Precipitation Occurence using a Global SST-SLP Correlation Network. M. Lu, U. Lall, J. Kawale, S. Liess, and V. Kumar. February 2016.

2015

Evaluation of Global Climate Models Based on Global Impacts of ENSO. S. Agrawal, T. Rehberger, S. Liess, G. Atluri, and V. Kumar. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham, pp 101-109. 2015.

Computing and Climate. J. Faghmous, V. Kumar, and S. Shekhar. Computing in Science and Engineering 17(6): 6-8, 2015.

Unsupervised Method for Water Surface Extent Monitoring Using Remote Sensing Data. X. Chen, A. Khandelwal, S. Shi, J. Faghmous, S. Boriah, and V. Kumar. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham, pp 51-58, 2015.

A Daily Global Mesoscale Ocean Eddy Dataset from Satellite Altimetry. J. H. Faghmous, I. Frenger, Y. Yao, R. Warmka, A. Lindell, and V. Kumar. Scientific Data, Article number: 150028. 2015.

Post Classification Label Refinement Using Implicit Ordering Constraint Among Data Instances. A. Khandelwal, V. Mithal, and V. Kumar. International Conference on Data Mining (ICDM 2015), Atlantic City, NJ. November 14-17, 2015.

Adaptive Heterogeneous Ensemble Learning Using the Context of Test Instances. A. Karpatne and V. Kumar. International Conference on Data Mining (ICDM 2015), Atlantic City, NJ. November 14-17, 2015.

Clustering Dynamic Spatio-Temporal Patterns in the Presence of Noise and Missing Data. X. Chen, J. H. Faghmous, A. Khandelwal, and V. Kumar. 24th International Joint Conference on Artificial Intelligence (IJCAI-15). Buenos Aires, Argentina. July 25-31, pp 2575 - 2581, 2015.

Ensemble learning methods for binary classification with multi-modality within the classes. A. Karpatne, A. Khandelwal, and V. Kumar. Proceedings of the SIAM International Conference on Data Mining (SDM 2015), Vancouver, Canada, pp. 730-738.

On the data-driven inference of modulatory networks in climate science: an application to West African rainfall. D. L. González, M. Angus, I. K. Tetteh, G. A. Bello, K. Padmanabhan, S. V. Pendse, S. Srinivas, J. Yu, F. Semazzi, V. Kumar, and N. F. Samatova. Nonlinear Processes in Geophysics, 22(1): 33–46, 2015.

2014

Different Modes of Variability over the Tasman Sea: Implications for Regional Climate
S. Liess, A. Kumar, P. Snyder, J. Kawale, K. Steinhaeuser, F. Semazzi, A. Ganguly, N. Samatova, and V. Kumar
Journal of Climate, 27(22): 8466-8486. 2014.

Online Discovery of Group Level Events in Time Series. X. C. Chen, A. Mueen, V. K. Narayanan, N. Karampatziakis, G. Bansal, and V. Kumar. Proceedings of the 2014 SIAM International Conference on Data Mining, pp 632-640, 2014.

Predictive Learning in the Presence of Heterogeneity and Limited Training Data. A. Karpatne, A. Khandelwal, S. Boriah, and V. Kumar. Proceedings of the 2014 SIAM International Conference on Data Mining, pp 253-261.

Spatio-temporal Consistency for Autonomous Dynamic Object Identification in Continuous Spatio-Temporal Fields. J. H. Faghmous, H. Nguyen, M. Le, and V. Kumar. Twenty-Eighth Conference on Artificial Intelligence (AAAI). Quebec, Canada.

Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques.
A. Ganguly, E. Kodra, A. Banerjee, S. Boriah, S. Chatterjee, A. Choudhary, D. Das, J. Faghmous, P. Ganguli, S. Ghosh, K. Hayhoe, C., Hays, W. Hendrix, Q. Fu, D. Kumar, V. Kumar, S. Liess, R. Mawalagedara, V. Mithal, R. Oglesby, K. Salvi, P. Snyder, K. Steinhaeuser, D. Wang, and D. Wuebbles. Nonlinear Processes in Geophysics, 21(4): 777-795, 2014.

2013

Multiple Hypothesis Object Tracking For Unsupervised Self-Learning: An Ocean Eddy Tracking Application. J. Faghmous, M. Uluyol, L. Styles, M. Le, V. Mithal, S. Boriah, and V. Kumar. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 27(1), 2013.

A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics. J. Faghmous, M. Le, M. Uluyol, V. Kumar, and S. Chatterjee. 2013 IEEE 13th International Conference on Data Mining, pp 151-160, 2013.

Contextual Time Series Change Detection. X. C. Chen, K. Steinhaeuser, S. Boriah, S. Chatterjee, and V. Kumar. Proceedings of the 2013 SIAM International Conference on Data Mining, pp 503-511, 2013.

Change Detection from Temporal Sequences of Class Labels: Application to Land Cover Change Mapping. V. Mithal, Khandelwal, S. Boriah, K. Steinhaeuser, and V. Kumar. Proceedings of the 2013 SIAM International Conference on Data Mining, pp 650-658, 2013.

Automatic Detection and Correction of Multi-class Classification Errors Using System Whole-part Relationships. Z. Chen, J. Jenkins, J. Rao, A. Choudhary, F. Semazzi, A. V. Melechko, V. Kumar, and N. F. Samatova. Proceedings of the 2013 SIAM International Conference on Data Mining, pp 494-502, 2013.

A graph-based approach to find teleconnections in climate data. J. Kawale, S. Liess, A. Kumar, M. Steinbach, P. Snyder, V. Kumar, A.R. Ganguly, N.F. Samatova, and F. Semazzi. Statistical Analysis and Data Mining, pp 158-179, 2013.

2012

Hierarchical Classifier-Regression Ensemble for Multi-Phase Non-Linear Dynamic System Response Prediction: Application to Climate Analysis. D. L. Gonzalez, Z. Chen, I. K. Tetteh, T. Pansombut, F. Semazzi, V. Kumar, A.V. Melechko, and N. F. Samatova. 2012 IEEE 12th International Conference on Data Mining Workshops, Brussels, Belgium, pp. 781-788, 2012.

A New Data Mining Framework for Forest Fire Mapping. X.C. Chen, A. Karpatne, Y. Chamber, V. Mithal, M. Lau, K. Steinhaeuser, S. Boriah, M. Steinbach, V. Kumar, C. S. Potter, S. A. Klooster, T. Abraham, J.D. Stanley, and J. C. Castilla-Rubio. 2012 Conference on Intelligent Data Understanding, Boulder, CO, pp 104-111, 2012.

A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring. J. H. Faghmous, Y. Chamber, S. Boriah, S. Liess, F. Vikebo, M. dos Santos Mesquita, and V. Kumar. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1): 281-287, 2012.

On the path to sustainable, scalable, energy-efficient data analytics: Challenges, future directions. S. Lakshminarasimhan, P. Kumar, W.-K. Liao, A. N. Choudhary, V. Kumar, and N. F. Samatova. 2012 International Green Computing Conference (IGCC), pp 1-6, 2012.

Toward Data-driven, Semi-automatic Inference of Phenomenological Physical Models: Application to Eastern Sahel Rainfall. S.V. Pendse, I.K. Tetteh, F. Semazzi, V. Kumar, and N.F. Samatova. Proceedings of the 2012 SIAM International Conference on Data Mining, pp 35-46, 2012.

2011

Data Guided Discovery of Dynamic Climate Dipoles. J. Kawale, S. Liess, A. Kumar, A. Ganguly, M. Steinbach, N. Samatova, F. Semazzi, P. Snyder, and V. Kumar. NASA Conference on Intelligent Data Understanding, October 19-21, 2011.

Anomaly construction in Climate data: Issues and Challenges. J. Kawale, S. Chatterjee, A. Kumar, S. Liess, M. Steinbach, and V. Kumar. NASA Conference on Intelligent Data Understanding, October 19-21, 2011.

Monitoring Global Forest Cover Using Data Mining. V. Mithal, A. Garg, S. Boriah, M. Steinbach, V. Kumar, C. Potter, S. Klooster, and J.-C. Castilla-Rubio. ACM Transactions on Intelligent Systems and Technology, Volume 2, Number 4, 2011.

Classification of Emerging Extreme Event Tracks in Multi-Variate Spatio-Temporal Physical Systems Using Dynamic Network Structures: Application to Hurricane Track Prediction. H. Sencan, Z. Chen, W. Hendrix, T. Pansombut, F. Semazzi, A. Choudhary, V. Kumar, N.F. Samatova, and A. Melechko. IJCAI'11: Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two, pp 1478-1484, 2011.

Discovering Dynamic Dipoles in Climate Data. J. Kawale, M. Steinbach, and V. Kumar. Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM 2011), Mesa, Arizona, pp 110-118, 2011.

2010

A Knowledge Discovery Strategy for Relating Sea Surface Temperatures to Frequencies of Tropical Storms and Generating Predictions of Hurricanes Under 21st-Century Global Warming Scenarios. C. Race, M. Steinbach, A. Ganguly, F. Semazzi, and V. Kumar. Proceedings of Annual Conference on Intelligent Data Understanding (CIDU), pp 204-212, October, 2010.

A Comparative Study of Algorithms for Land Cover Change. S. Boriah, V. Mithal, A. Garg, V. Kumar, M. Steinbach, C. Potter, and S. Klooster. Proceedings of Annual Conference on Intelligent Data Understanding (CIDU), pp 175-188, October, 2010.

2009 and earlier

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Addtional links, outdated, but publications by topic:

Software Packages

METIS/ParMETIS/hMETIS - Serial and Parallel Graph Partitioning Libraries

PSPASES - A Scalable Parallel Direct Solver Library for Sparse SPD Systems