Publications

Publications

Please visit Google Scholar DBLP, ResearchGate, or Dr Kumar's full vita  for more complete information.

(links to articles are currently being updated; thank you for your patience.) 

2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 earlier 

 

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.
April Earth Syst. Sci. Data, 12, 1–11, 2020 (accepted April 2020) https://doi.org/10.5194/essd-12-1-2020 

Integrating Physics-Based Modeling with Machine Learning: A Survey.
Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar.
April 2020. https://arxiv.org/abs/2003.04919 

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. https://arxiv.org/pdf/2001.11086.pdf

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. https://doi.org/10.1186/s12911-019-1009-3

Inverse Problems, Deep Learning, and Symmetry Breaking.
Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar, Ju Sun.
March, 2020. https://arxiv.org/pdf/2003.09077.pdf

Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data.
Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Varun Mithal, Vipin Kumar.
January, 2020. https://arxiv.org/abs/2001.00994

 

2019

AI, Machine Learning, and Deep Learning for Climate Science and Extreme Weather Prediction I.
Donald D Lucas, Brian L White, Antonia Sebastian, Gemma Jayne Anderson, Vipin Kumar, Adrian Albert.
AGU Fall Meeting, December, 2019. https://agu.confex.com/agu/fm19/meetingapp.cgi/Session/89106

MINT: An intelligent interface for understanding the impacts of climate change on hydrological, agricultural and economic systems
Deborah Khider, Yolanda Gil, Kelly M Cobourn, Ewa Deelman, Christopher Duffy, R Ferreira da Silva, Armen Kemanian, Craig Knoblock, Vipin Kumar, Scott Dale Peckham, Yao-Yi Chiang, Dan Feldman, Daniel Garijo, D Hardesty Lewis, Ankush Khandelwal, Rajiv Mayani, Maximiliano Osorio, Minh Pahm, Suzanne A Pierce, Jay Pujara, Varun Ratnakar, Lele Shu, Hae Jin Song, Basel Shbita, Maria Stoica, Binh Vu, Lissa Pearson
AGU 2019 Fall Meeting Abstracts. https://ui.adsabs.harvard.edu/abs/2019AGUFMPA33C1108K/abstract

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. https://onlinelibrary.wiley.com/doi/abs/10.1111/agec.12531

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)https://www.nature.com/articles/s41467-019-12131-7

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. doi: 10.3233/SHTII90229

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. https://eco.confex.com/eco/2019/meetingapp.cgi/Paper/81387

Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning.
Xiaowei Jia, Ankush Khandelwal, Vipin Kumar.
April 2019. https://arxiv.org/abs/1904.04329

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. https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/19-004.pdf

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. https://dl.acm.org/doi/abs/10.1145/3308557.3308711

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. https://www.nature.com/articles/s41398-019-0390-0

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. https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/356420

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. https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/356835

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. https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/356419

Plantation Mapping in Southeast Asia.
Xiaowei Jia, Ankush Khandelwal, Kimberly Carlson, James S Gerber, Paul C West, Vipin Kumar.
Frontiers in Big Data, 2019. https://www.frontiersin.org/articles/10.3389/fdata.2019.00046/full

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, Jauary 2019. https://dl.acm.org/doi/pdf/10.1145/3192335

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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6568139/

Spatio-temporal classification at multiple resolutions using multi-view regularization
G. Nayak, R. Ghosh, V. Mithal, X. Jia, V. Kumar
9th International Workshop on Climate Informatics, October, 2019. https://doi.org/10.1109/BigData47090.2019.9006597

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. doi:10.24963/ijcai.2019/365 (link)

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. doi:10.1145/3292500.3330957 (link)

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. (link)

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. doi:10.1137/1.9781611975673.63 (link)

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. doi:10.1137/1.9781611975673.61 (link)

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.  doi:10.1137/1.9781611975673.58 (link)

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.  doi:10.1109/TKDE.2019.2911681 (link)

Physics Guided Machine Learning: A New Paradigm for Modeling Dynamic Systems.
Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Stuart Read, Jacob A Zwart, Alison Appling, Paul C Hanson, Vipin Kumar.
AGU Fall Meeting, December 2019https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/545195

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.  doi: 101137/1.9781611975673.63

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). https://doi.org/10.1029/2019WR024922

 

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. DOI:https://doi.org/10.1145/3192335

Emerging Data Science and Machine Learning Opportunities in the Weather and Climate Sciences.
Christiane Jablonowski, Sudhir R Shrestha, Vipin Kumar, Orhun Aydin, Amy McGovern, Imme Ebert-Uphoff, Daniel S Cooley, Kevin A Reed, Dawn Wright, Stephan Rasp, Ryan Lagerquist, Anuj Karpatne, Karthik Kashinath, Richard Loft, John Williams, Joshua Hacker
AGU Fall Meeting 2018https://agu.confex.com/agu/fm18/prelim.cgi/Session/54829

Physics guided machine learning: a new paradigm for modeling dynamical systems.
V Kumar, X Jia, A Karpatne, JS Read, PC Hanson
AGU Fall Meeting 2018, https://agu.confex.com/agu/fm18/prelim.cgi/Paper/371403

Computational Approaches for Early Emerging Heterogeneity and Disorder Risk.
S Jacob, J Wolff, C Doyle, M Steinbach, V Kumar, J Elison
NEUROPSYCHOPHARMACOLOGY 43, S97-S98

Land Cover Classification for Soil Conservation Assessment Based on Remote Sensing, Deep Learning and Simulation Modeling.
D Mulla, LG Olmanson, BK Gelder, A Khandelwal, V Kumar
AGU Fall Meeting Abstracts

Process-Guided Data-Driven modeling of water temperature: Anchoring predictions with thermodynamic constraints in the Big Data era.
JS Read, J Willard, X Jia, A Karpatne, A Appling, JA Zwart, S Oliver, ...
AGU Fall Meeting Abstracts

Towards real-time water quality forecasts for streams of the United States.
JA Zwart, A Appling, L De Cicco, DL Blodgett, F Salas, J Willard, X Jia, ...
AGU Fall Meeting Abstracts

Integrating Models Through Knowledge-Powered Data and Process Composition.
D Garijo, Y Gil, KM Cobourn, E Deelman, C Duffy, R Ferreira da Silva, ...
AGU Fall Meeting Abstracts

Global surface water storage dynamics using satellite remote sensing.
R Tortini, N Noujdina, SM Yeo, M Ricko, CM Birkett, SP Coss, MT Durand, ...
AGU Fall Meeting Abstracts

Physics guided machine learning: a new paradigm for modeling dynamical systems.
A Karpatne, V Kumar, X Jia, JS Read, PC Hanson
AGU Fall Meeting Abstracts

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, doi:10.1137/1.9781611975321.29 (link)

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; doi:10.3390/rs10010069 (link)

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, doi:10.1016/j.accre.2018.02.002 (link)

Mining Sub-Interval Relationships In Time Series Data.
S Agrawal, S Verma, G Atluri, A Karpatne, S Liess, A Macdonald III, ...
arXiv preprint 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

Heterogeneous metric learning of categorical data with hierarchical couplings.
C Zhu, L Cao, Q Liu, J Yin, V Kumar
IEEE Transactions on Knowledge and Data Engineering 30 (7), 1254-1267

Mining Electronic Health Records (EHRs) A Survey.
P Yadav, M Steinbach, V Kumar, G Simon
ACM Computing Surveys (CSUR) 50 (6), 1-40

Actuation studies on polyacrylic rubber based dielectric elastomer transducers.
SS Gautam, V Das, SK Toppo, A Singh, V Kumar, AK Pandey
NISCAIR-CSIR, India

MINT: model integration through knowledge-powered data and process composition.
Y Gil, K Cobourn, E Deelman, C Duffy, RF da Silva, A Kemanian, ...
9th International Congress on Environmental Modelling and Software, 2019.

 

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. Society for Industrial and Applied Mathematics, 2017. DOI:10.1137/1.9781611974973.20. (link)

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. DOI:10.1145/3097983.3098099. (link)

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. ACM, 2017 (link)

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

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 (2017) doi: 10.1016/j.rse.2017.05.039 (link)

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 (link)

Monitoring Dynamics of Water Bodies at Global Scale using Remote Sensing Data: Opportunities, Challenges and New Research Directions
A. Khandelwal, A. Karpatne, and V. Kumar
Workshop on Mining Big Data in Climate and Environment (MBDCE 2017), 17th SIAM International Conference on Data Mining (SDM 2017) (link)

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. PP (99) DOI: 10.1109/TKDE.2017.2720168 (link)

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 DOI:10.1175/JCLI-D-15-0884.1 (link)

How Can Physics Inform Deep Learning Methods in Scientific Problems?: Recent Progress and Future Prospects. 
Anuj Karpatne, William Watkins, Jordan Read,Vipin Kumar. 
Workshop on Deep Learning for Physical Sciences (DLPS 2017), NIPS 2017, Long Beach, CA, USA. https://dl4physicalsciences.github.io/files/nips_dlps_2017_19.pdf

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. https://doi.org/10.1002/2016EA000196

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, March 2017 https://doi.org/10.1145/3068777.3068779

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. 2017EGUGA..1911858S 

Mining Electronic Health Records : A Survey 
Pranjul Yadav, Michael Steinbach, Vipin Kumar, Gyorgy Simon. 
Technical Report, University of Minnesota. TR17-003, Formerly TR 15-016. April 2017. https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/17-003.pdf

Multiple Instance Learning for bags with Ordered instances 
Guruprasad Nayak, Varun Mithal, Vipin Kumar. 
Technical Report, University of Minnesota. TR17-007. June 2017. https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/17-007.pdf

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. https://doi.org/10.1145/3097983.3105810

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. https://www.atmos.colostate.edu/~iebert/PAPERS/CI2017_paper_15.pdf

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. doi: 10.4338/ACI-2017-03-RA-0042

Physics-guided neural networks (pgnn): An application in lake temperature modeling. 
Anuj Karpatne, William Watkins, Jordan Read, Vipin Kumar. 
October 2017. pp. 21-22. https://arxiv.org/abs/1710.11431

Orbit: Ordering based information transfer across space and time for global surface water monitoring. 
Ankush Khandelwal, Anuj Karpatne, Vipin Kumar. 
November 2017. https://arxiv.org/abs/1711.05799

Automated Plantation Mapping in Indonesia Using Remote Sensing Data. 
Xiaowei Jia, Ankush Khandelwal, Anuj Karpatne,Vipin Kumar. 
Proceedings of the AGU 2017. December 2017. pp. 228-230. https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/300597

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks. 
Anuj Karpatne,Vipin Kumar. 
Proceedings of the AGU 2017. December 2017. pp. 228-230. https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/295246

Building Bridges Between Geoscience and Data Science through Benchmark Data Sets. 
mme Ebert-Uphoff, David R Thompson, Ibrahim Demir, Yulia Gel, Mary C Hill, Anuj Karpatne, Mariana Güereque, Vipin Kumar, Enrique Cabral, Padhraic Smyth. 
Proceedings of the AGU 2017. December 2017. pp. 228-230. https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/232651

Building Daily 30-meter Spatial Resolution Maps of Surface Water Bodies from MODIS Data Using a Novel Technique for Transferring Information Across Space and Time. 
Ankush Khandelwal, Anuj Karpatne,Vipin Kumar. 
Proceedings of the AGU 2017. December 2017. pp. 228-230. https://ui.adsabs.harvard.edu/abs/2017AGUFMIN13E..07K/abstract

 

 

 

 

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)  DOI: 10.1109/BigData.2016.7840723 (link)

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) DOI: 10.1109/BigData.2016.7840738(link)

Multiple Instance Learning for burned area mapping using multitemporal reflectance data
Guruprasad Nayak, Varun Mithal, Vipin Kumar
6th International Workshop on Climate Informatics, 2016. DOI:10.5065/D6K072N6. (link)

Mapping Burned Areas in Tropical forests using MODIS data
V. Mithal, G. Nayak, A. Khandelwal, R. Nemani, V. Kumar, and N. Oza
September 2016. (link)

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. DOI: 10.1137/1.9781611974348.19 (link)

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
International Conference on Advanced Data Mining and Applications (ADMA) 2016. DOI: 10.1007/978-3-319-49586-6_26 (link)

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. DOI: 10.1109/MGRS.2016.2528038 (link)

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
K. Morik, J. Lässig, and K. Kersting, Eds. Computational Sustainability, Springer. 2016. DOI: 10.1007/978-3-319-31858-5_7 (link)

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. DOI: 10.1175/JCLI-D-14-00452.1(link)

 

2015

Evaluation of Global Climate Models Based on Global Impacts of ENSO
S. Agrawal, T. Rehberger, S. Liess, G. Atluri, and V. Kumar
Machine Learning and Data Mining Approaches to Climate Science, Springer pp 101-109. 2015. DOI: 10.1007/978-3-319-17220-0_10 (link)

Computing and Climate 
J. Faghmous, V. Kumar, and S. Shekhar
Computing in Science and Engineering 17(6): 6-8, 2015. DOI: 10.1109/MCSE.2015.114 (link)

Patent: Automated mapping of land cover using sequences of aerial imagery 
S. Boriah, A. Khandelwal, V. Mithal, V. Kumar, and K. Steinhaeuser
February 2015 (link)

Tripoles: A New Class of Climate Teleconnections 
G. Atluri, S. Agrawal, S. Liess, V. Kumar, and S. Chatterjee
November 2015 (link)

Unsupervised Method for Water Surface Extent Monitoring Using Remote Sensing Data 
X. Chen, A. Khandelwal, S. Shi, J. Faghmous, S. Boriah, and V. Kumar
Machine Learning and Data Mining Approaches to Climate Science, Springer pp 51-58. 2015. DOI: 10.1007/978-3-319-17220-0_5 (link)

Evaluation of Global Climate Models Based on Global Impacts of ENSO 
S. Agrawal, T. Rehberger, S. Liess, G. Atluri, and V. Kumar
Machine Learning and Data Mining Approaches to Climate Science, Springer pp 101-109. 2015. (link)

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, Nature Publishing Group, 2. 2015. (link)

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. (pdf)

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. (pdf)

Online change point detection for remote sensing time series
X. Chen, Y. Yao, S. Shi, V. Kumar, and J. Faghmous
Fifth International Workshop on Climate Informatics, Boulder, CO. September 24- 25, 2015. (pdf)

Understanding Dominant Factors for Precipitation over the Great Lakes Region
S. Chatterjee, V. Kumar, A. Banerjee, and S. Liess
5th International Workshop on Climate Informatics, September 24-25 2015. (pdf)

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, 2015. (pdf)

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). pp. 730-738. Vancouver, Canada. April 30-May 2, 2015. (pdf)

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 Discussions 1: pp. 479-517. January 13, 2015. (link)

 

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): pp. 8466-8486. 2014. (link)

On the Data-driven Inference of Causal Networks: Examining the relationship between global Sea Surface Temperatures and Sahel Rainfall
D. Gonzalez , I. Tetteh , M. Angus, S. Pendse , S. Srinivas , P. Kanchana, F. Semnazzi , V. Kumar , and N. Samatova
The Third International Workshop on Climate Informatics- NCAR (pdf)

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 (pdf)

Predictive Learning in the Presence of Heterogeneity and Limited Training Data 
A. Karpatne , A. Khandelwal , Shyam Boriah, and V. Kumar
Proceedings of the 2014 SIAM International Conference on Data Mining (link)

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 (link)

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 (pdf)

State-of-the-art, challenges, and opportunities for data-driven research. 
J. H. Faghmous and V. Kumar
J. Lässig, K. Kersting, and K. Morik, Eds. Computational Sustainability Springer

 

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 (pdf)

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 (link)

Spatio-Temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities
J. Faghmous and V. Kumar
Advances in Data Mining, W. Chu, Editor, 2013 (pdf)

Contextual Time Series Change Detection
X. C. Chen, , K. Steinhaeuser, S. Boriah, S. Chatterjee, and V. Kumar
13th SIAM International Conference on Data Mining (SDM 2013), Austin, Texas, USA, May 2-4, 2013 (pdf)

Change Detection from Temporal Sequences of Class Labels: Application to Land Cover Change Mapping
V. Mithal, Khandelwal, S. Boriah, K. Steinhaeuser, and V. Kumar
13th SIAM International Conference on Data Mining (SDM 2013), Austin, Texas, USA, May 2-4, 2013 (pdf)

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
13th SIAM International Conference on Data Mining (SDM 2013), Austin, Texas, USA, May 2-4, 2013 (pdf)

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, online publication April 2013, doi: 10.1002/sam.11181 (link)

Earth Science Applications of Sensor Data
A. Karpatne, J. Faghmous, J. Kawale, L. Styles, M. Blank, V. Mithal, X. Chen, A. Khandelwal, S. Boriah, K. Steinhaeuser, M. Steinbach, V. Kumar, and S. Liess
Managing and Mining Sensor Data, C. C. Aggarwal (Ed.), Springer, 505-530, January 15, 2013, ISBN:9781461463085 (link)

 

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 (ICDM), pp. 781-788, December 10, 2012. Brussels, Belgium (link)

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
CIDU 2012: Proceedings of the IEEE Conference on Intelligent Data Understanding, pages 104-111, October,2012 (link)

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
AAAI 2012: AAAI Conference on Artificial Intelligence, July 2012 (link)

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), pages 1 - 6, 4-8 June 2012 (link)

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
SIAM International Conference on Data Mining (SDM), April 26-28, 2012 (link)

 

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 and V. Kumar
NASA Conference on Intelligent Data Understanding, October 19-21, 2011 (pdf)

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

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 (pdf | link)

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
Proceedings of the 22nd International Joint Conference on Artificial Intelligence, July 16-22, 2011 (link)

Discovering Dynamic Dipoles in Climate Data
J. Kawale, M. Steinbach, and V. Kumar
SIAM International Conference on Data Mining (SDM), April 28-30, 2011 (pdf)

 

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), October, 2010 (pdf)

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), October, 2010, 175-188 (pdf)

2009 and earlier

 

 

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