Knowledge-guided Machine Learning

Talk title: Knowledge-guided Machine Learning: Advances in an Emerging Paradigm Combining Scientific Knowledge with Machine Learning for Accelerating Scientific Discovery

Abstract: This talk will introduce knowledge-guided machine learning (KGML), a rapidly growing field of research where scientific knowledge is deeply integrated in machine learning frameworks to produce scientifically grounded, explainable, and generalizable predictions even on out-of-distribution data. This talk will present a multi-dimensional view to organize prior research in KGML in terms of the nature and format of scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML for diverse scientific use-cases. These KGML concepts will be illustrated using a variety of case studies in physics, ecology, and biology applications including modeling the quality of water in lakes across the US and discovering novel biological traits of organisms linked with evolution from biodiversity images. The talk will conclude with a discussion of emerging opportunities in KGML  especially in the age of Foundation models with potential applications in a broad range of scientific disciplines.

Bio: Anuj Karpatne is an Associate Professor in the Department of Computer Science at Virginia Tech, where he leads the knowledge-guided machine learning (KGML) lab. A key focus of Anuj‘s research is to make foundational innovations in AI/ML driven by the needs of inter-disciplinary problems in science that impact society. Anuj‘s contributions to the field of KGML have been pivotal in creating, nurturing, and steering this emerging field from its formal conceptualization in 2017 to developing its research themes in the context of a variety of application domains including hydrology, ecology, geophysics, organismal biology, mechanobiology, quantum mechanics, and fluid dynamics. He has received the NSF CAREER Award in 2023, the Outstanding New Assistant Professor 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 the Inaugural Research Fellow by the IS-GEO Research Coordination Network in 2019. He organizes several workshops and tutorials on KGML every year and currently serves as an Associate Editor for the ACM Transactions on Knowledge Discovery from Data (TKDD) journal. Anuj is also a co-author of the second edition of the textbook, “Introduction to Data Mining”, and the lead editor of the first comprehensive book on “Knowledge-guided Machine Learning”. He received his Ph.D. in Computer Science at the University of Minnesota in 2017 under the guidance of Prof. Vipin Kumar.