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!


New: I am teaching the graduate-level course: CS/STAT 5525: Data Analytics in Spring 2022. If are interested in registering for this course, please visit the course website for more details.

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


Please check my Google Scholar profile for an updated list.


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).