About Me

Hello everyone! I am an Assistant Professor in the Department at Computer Science at Virginia Tech and my primary interest is in developing data-enabled solutions for scientific and socially relevant problems. My current research is geared towards shaping the emerging field of research in Science-guided Machine Learning (SGML) (also refered to as physics-guided ML or theory-guided data science), where machine learning methods are systematically coupled with scientific knowledge (or physics) to accelerate scientific discovery. I enjoy working on inter-disciplinary projects and my research group is actively developing novel SGML formulations in a diverse range of scientific applications including lake modeling, remote sensing, fluid dynamics, quantum science, optics, radar physics, mechano-biology, and ichthyology. If you are interested in collaborating together, please feel free to contact me!

Short Bio

Anuj Karpatne is an Assistant Professor in the Department of Computer Science at Virginia Tech, where he develops data mining and machine learning methods to solve scientific and socially relevant problems. A key focus of Dr. Karpatne’s research is to advance the field of science-guided machine learning for applications in several domains ranging from climate science, hydrology, and ecology to cell cycle biology, mechano-biology, quantum science, and fluid dynamics. Dr. Karpatne co-organized the FEED 2018 workshop, served as the workshop co-chair for SIGKDD 2019, and has co-organized sessions at AAAS Annual Meeting 2019 and AGU Fall Meetings 2017 and 2018. He is currently serving as the co-Editor-in-Chief for the SIGAI “AI Matters” and the Review Editorial Board Member for “Data-driven Climate Sciences” section in Frontiers in Big Data journal. In recognition of his interdisciplinary research efforts in geosciences, Dr. Karpatne was named the Inaugural Research Fellow by the IS-GEO (Intelligent Systems for Geosciences) Research Coordination Network in 2018. Dr. Karpatne is also a co-author of the second edition of the textbook, Introduction to Data Mining. He received his Ph.D. in Computer Science at the University of Minnesota in 2017 under the guidance of Prof. Vipin Kumar.


Recent Updates (Last Updated: 07/07/2020)

[07-2020]Our recent work on "Learning Neural Networks with Competing Physics Objectives: An Application in Quantum Mechanics" is now available on Arxiv. See here.
[07-2020]Our recent work on "Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach" is now available on Arxiv. See here.
[07-2020]Paper published in the Atmospheric Measurement Techniques (AMT) journal (see here).
[05-2020]Received an NSF EAGER grant with the project title: “Collaborative Research:III: Exploring Physics Guided Machine Learning for Accelerating Sensing and Physical Sciences," (NSF-IIS-2026710, VT PI, $54,452, Duration: 05/01/2020-04/30/2021). This is a collaborative project with Ohio State University, SUNY Binghamton, and University of Massachussetts at Lowell.
[05-2020]Two papers published in the SDM 2020 Conference (see here and here).
[05-2020]Paper published in the International Journal on Ecological Modelling and Systems Biology (see here).
[1-2020]Gave a seminar talk in the Dept. of Atmospheric, Oceanic, and Planetary Physics at the University of Oxford.
[12-2019]Served on the program committees of AAAI, SDM, KDD, ICML, and NeurIPS in the year of 2019.
[11-2019]Gave an invited talk at the Macromolecules Innovation Institute (MII) workshop.
[10-2019]Received an NSF "Harnessing the Data Revolution (HDR)" grant with the project title: “Collaborative Research: Biology-guided neural networks for discovering phenotypic traits," (NSF-OAC-1940247, VT PI, $422,000, Duration: 10/01/2019-09/30/2021). This is a collaborative project with Battelle, Tulane University, Drexel University, and University of Washington Seattle.
[10-2019]Gave a keynote talk at DARPA Physics of AI (PAI) Review Meeting.
[09-2019]Paper published in the Water Resources Research (WRR) Journal (see here).
[09-2019]Gave an invited talk at the Oak Ridge National Lab (ORNL) AI Workshop.
[08-2019]Served as Workshop Co-chair for KDD 2019.”.
[08-2019]Served on the panel discussion at the Earth Day session at KDD 2019.
[08-2019]Paper published in the IJCAI 2019 Conference (see here).
[07-2019]Invited to serve as a Co-Editor-in-Chief (EiC) of the ACM Special Interest Group in Artificial Intelligence (SIGAI) quarterly newsletter, “AI Matters.”.
[05-2019]Three papers published in the SDM 2019 Conference (see here, here, and here).
[04-2019]Gave an invited talk at the VT Office of GIS and Remote Sensing (OGIS) Research Symposium.
[01-2019]Co-organizer of session on “How AI and Knowledge Centers are Changing Societal Views of Critical Earth Resources” at American Association for the Advancement of Science (AAAS) Annual Meeting, 2019.
[01-2019]Named the Inaugural Research Fellow by the Intelligent Systems for Geosciences (IS-GEO), sponsored by Petrobras, for 2019.
[12-2018]Gave a lightning talk representing the NSF Expeditions project: ``Understanding Climate Change: A Data-driven Approach'' at the NSF Expeditions in Computing PI Meeting: 10 Years of Transforming Science and Society.
[12-2018]Served on the panel discussion at a session in the American Geophysical Union (AGU) Annual Meeting.
[12-2018]Paper published in the IEEE Big Data 2018 Conference (see here).
[12-2018]Gave a lightning talk representing the NSF Expeditions project: ``Understanding Climate Change: A Data-driven Approach'' at the NSF Expeditions in Computing PI Meeting: 10 Years of Transforming Science and Society.
[11-2018]Gave an invited talk at UCLA IPAM Workshop on HPC for Computationally and Data-Intensive Problems.
[09-2018]Gave an invited talk at IS-GEO Seminar for Energy Industry (with support from Petrobras) at Texas Advanced Computing Center (TACC).
[08-2018]Joined as an Assistant Professor in the Department of Computer Science at Virginia Tech.

Publications


Note: The list below is out-of-date. Please check my Google Scholar profile for an updated list.

Book

[B1] P. Tan, M. Steinbach, A. Karpatne, and V. Kumar, Introduction to Data Mining, Pearson Addison–Wesley (Second Edition), ISBN-13: 978-0133128901, 2018 [Book Website].  

Journal Articles

[J10] A. Karpatne, G. Atluri, J. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, and V. Kumar, Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data, IEEE Transactions on Knowledge and Data Engineering (TKDE), 29(10), 2318–2331, 2017 [arXiv, DOI].  
[J9] G. Atluri^{star}, A. Karpatne^{star}, and V. Kumar, Spatio-temporal Data Mining: A Survey of Problems and Methods, ACM Computing Surveys, 2017 (accepted; ^{star} equal contribution ) [arXiv].  
[J8] A. Karpatne, I. Ebert-Uphoff, S. Ravela, H. A. Babaie, and V. Kumar, Machine Learning for the Geosciences: Challenges and Research Opportunities, IEEE TKDE, 2017 (in review) [arXiv].  
[J7] A. Khandelwal^{star}, A. Karpatne^{star}, M.E. Marlier^{star}, J. Kim, D. P. Lettenmaier, and V. Kumar, An Approach for Global Monitoring of Surface Water Extent Variations using MODIS Data, Remote Sensing of Environment, Elsevier, 2017 (^{star} equal contribution) [DOI].  
[J6] A. Karpatne, Z. Jiang, R. R. Vatsavai, S. Shekhar, and V. Kumar, Monitoring Land Cover Changes: A Machine Learning Perspective, IEEE Geoscience and Remote Sensing Magazine, 4(2), 8–21, 2016. [DOI].  
[J5] A. Karpatne and S. Liess, A Guide to Earth Science Data: Summary and Research Challenges, IEEE Computing in Science & Engineering, 17(6), 14–18, 2015. [DOI].  
[J4] F. Schrodt, J. Kattge, H. Shan, F. Fazayeli, J. Joswig, A. Banerjee, M. Reichstein, G. Bonisch, S. Diaz, J. Dickie, A. Gillison, A. Karpatne, S. Lavorel, P.W. Leadley, C. Wirth, I. Wright, S.J. Wright, and P.B. Reich, BHPMF - A Hierarchical Bayesian Approach to Gap-filling and Trait Prediction for Macroecology and Functional Biogeography, Global Ecology and Biogeography, 24(12), 1510–1521, 2015. [DOI].
[J3] R. Khemchandani, A. Karpatne, and S. Chandra, Twin Support Vector Regression for the Simultaneous Learning of a Function and its Derivatives, International Journal of Machine Learning and Cybernetics, 4(1), 51–63, 2013. [DOI].
[J2] R. Khemchandani, A. Karpatne, and S. Chandra, Proximal Support Tensor Machines, International Journal of Machine Learning and Cybernetics, 4(6), 703–712, 2013. [DOI].
[J1] R. Khemchandani, A. Karpatne, and S. Chandra, Generalized Eigenvalue Proximal Support Vector Regressor, Expert Systems with Applications, 38(10), 13136–13142, 2011 [DOI].

Peer-reviewed Conference Papers

[C9] A. Karpatne, W. Watkins, J. Read, and V. Kumar, Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling, SIAM International Conference on Data Mining (SDM), 2018 (in review) [arXiv].  
[C8] X. Jia, Y. Hu, A. Khandelwal, A. Karpatne, and V. Kumar, Joint Sparse Auto-encoder: A Semi-supervised Spatio-temporal Approach in Mapping Large-scale Croplands, IEEE International Conference on Big Data, 2017.  
[C7] S. Agrawal, G. Atluri, A. Karpatne, S. Chatterjee, S. Liess, and V. Kumar, Tripoles: A New Class of Relationships in Time Series Data, ACM International Conference on Knowledge Discovery and Data Mining (KDD), 697–706, 2017 [DOI].  
[C6] X. Jia, X. Chen, A. Karpatne, and V. Kumar, Identifying Dynamic Changes with Noisy Labels in Spatial-temporal Data: A Study on Large-scale Water Monitoring Application, IEEE International Conference on Big Data, 1328–1333, 2016 [DOI].  
[C5] A. Karpatne and V. Kumar, Adaptive heterogeneous ensemble learning using the context of test instances, IEEE International Conference on Data Mining (ICDM), 787–792, 2015. [DOI].  
[C4] A. Karpatne, A. Khandelwal, and V. Kumar, Ensemble learning methods for binary classification with multi-modality within the classes, SDM, (82) 730–738, 2015. [DOI].  
[C3] A. Karpatne, A. Khandelwal, S. Boriah, and V. Kumar, Predictive learning in the presence of heterogeneity and limited training data, SDM, (29) 253–261, 2014. [DOI].  
[C2] A. Karpatne, M. Blank, M. Lau, S. Boriah, K. Steinhaeuser, M. Steinbach, and V. Kumar, Importance of vegetation type in forest cover estimation, NASA Conference on Intelligent Data Understanding (CIDU), 71–78, 2012. [DOI].  
[C1] X. Chen^{star}, A. Karpatne^{star}, Y. Chamber^{star}, 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, A new data mining framework for forest fire mapping, CIDU, 104–111, 2012 (^{star} equal contribution). [DOI].

Book Chapters

[BC2] A. Karpatne, A. Khandelwal, X. Chen, V. Mithal, J. Faghmous, and V. Kumar, Global monitoring of inland water dynamics: State-of-the-art, challenges, and opportunities, In Computational Sustainability, J. Lassig, K. Kersting, and K. Morik (Eds.), Springer, 121–147, 2016. [DOI].  
[BC1] A. Karpatne, J. Faghmous, J. Kawale, L. Styles, M. Blank, V. Mithal, X. Chen, A. Khandelwal, S. Boriah, K. Steinhaeuser, M. Steinbach, and V. Kumar, Earth science applications of sensor data, In Managing and Mining Sensor Data, C. Aggarwal (Ed.), Springer, 505–530, 2013. [DOI].

Peer-reviewed Workshop Proceedings

[W7] A. Karpatne and V. Kumar, Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks, American Geophysical Union (AGU) Fall Meeting, 2017.
[W6] A. Karpatne, W. Watkins, J. Read, and V. Kumar, Physics-guided Learning of Neural Networks: An Application in Lake Temperature Modeling, NIPS Workshop on Deep Learning for Physical Sciences, 2017.
[W5] A. Karpatne, H. Babaie, S. Ravela, V. Kumar, and I. Ebert-Uphoff, Machine Learning for the Geosciences--Opportunities, Challenges, and Implications for the ML process, SDM Workshop on Mining Big Data in Climate and Environment, 2017.
[W4] S. Gopal, A. Karpatne, and V. Kumar, Modeling the Food-Energy-Water Nexus in Critical Biodiverse Landscapes: A Case Study of Tonle Sap, Cambodia and Tulalip Tribe, USA, ACM KDD Workshop on Data Science for Food, Energy and Water, 2016 [Video].
[W3] A. Karpatne, A. Khandelwal, R. Anderson, M. Blank, S. Boriah, and V. Kumar, Group-specific local learning for global lake monitoring, Fourth International Workshop on Climate Informatics, 2014.
[W2] A. Karpatne, J. Faghmous, M. Blank, R. Anderson, S. Boriah, S. Liess, and V. Kumar, Understanding the Influence of Sea Surface Temperatures on Terrestrial Ecosystem Disturbances, Third International Workshop on Climate Informatics, 2013.
[W1] A. Karpatne, M. Blank, J. Middleton, S. Boriah, K. Steinhaeuser, M. Steinbach, S. Chatterjee, and V. Kumar, Understanding relationships between fire activity and sea surface temperature anomalies, American Geophysical Union (AGU) Fall Meeting, 2012.

Ph.D. Dissertation

Predictive Learning with Heterogeneity in Populations, University of Minnesota, 2017.  


For Prospective Students

I am always looking forward to work with bright and ambitious students who are motivated to pursue research in machine learning and enable solutions to problems of great scientific and societal relevance. I find working on real-world problems to be both intellectually stimulating and socially rewarding, given the variety of challenges faced in analyzing complex physical data that offer fertile grounds for novel research. A major focus of my current work is in the area of science-guided machine learning and I have several exciting projects in this space of research. If you are an undergraduate or graduate student who is interested in working with me, please feel free to shoot me an email with your CV/resume, and information about yourself including your major, technical background, specific application areas of interest (if any), and prior research experience (if any).