Graduate Seminar: Knowledge-Guided Machine Learning
Knowledge-guided Machine Learning:
Advances in An Emerging Field Combining Scientific Knowledge with Machine Learning
Anuj Karpatne
Friday, September 9, 2022
2:30 PM
2150 Torgersen Hall
Abstract
Given their tremendous success in commercial applications, Machine Learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these “black-box” ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific Knowledge-guided ML (KGML), seeks a distinct departure from existing “data-only” or “scientific knowledge-only” methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field.
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Biography
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 including climate science, hydrology, ecology, geophysics, trait-based biology, mechanobiology, quantum mechanics, and fluid dynamics. He has received the Outstanding New Assistant Award by the College of Engineering at VT in 2022, the Rising Star Faculty Award by the Department of Computer Science at VT in 2021 and was named the Inaugural Research Fellow by the IS-GEO (Intelligent Systems for Geosciences) Research Coordination Network for 2019. Dr. Karpatne currently serves as the editor-in-chief of the quarterly newsletter SIGAI AI Matters. 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.