Events

Computer Science Department Colloquium Knowledge-Guided Machine Learning for Scientific Discovery: Challenges and Opportunities
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Speaker: Assistant Professor Xiaowei Jia

Bio

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He got his Ph.D. degree from the University of Minnesota under the supervision of Prof. Vipin Kumar. His research interests include knowledge-guided machine learning and spatio-temporal data mining for real-world applications of great societal relevance. He is the recipient of the NSF CAREER Award, NASA Early Career Investigator Award, the University of Minnesota Best Dissertation Award, and multiple best paper awards from ICDM, SDM, ASONAM, and BIBE.

Location : CoRE 301

Committee: 

Event Type: Computer Science Department Colloquium

Abstract: Data science and machine learning (ML) models, which have found tremendous success in several commercial applications where large-scale data is available, e.g., computer vision and natural language processing, have met with limited success in scientific domains. Traditionally, physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to incomplete or inaccurate representations of the physical processes being modeled. Given rapid data growth due to advances in sensor technologies, there is a tremendous opportunity to systematically advance modeling in these domains by using machine learning methods. However, capturing this opportunity is contingent on a paradigm shift in data-intensive scientific discovery since the "black box" use of ML often leads to serious false discoveries in scientific applications. Because the hypothesis space of scientific applications is often complex and exponentially large, an uninformed data-driven search can easily select a highly complex model that is neither generalizable nor physically interpretable, resulting in the discovery of spurious relationships, predictors, and patterns. This problem becomes worse when there is a scarcity of labeled samples, which is quite common in science and engineering domains.My work aims to build the foundations of knowledge-guided machine learning (KGML) by exploring several ways of bringing scientific knowledge and machine learning models together. In particular, we discuss gaps and opportunities in scientific discovery and show the effectiveness of KGML in multiple applications of great societal and scientific relevance. My work also has the potential to greatly advance the pace of discovery in a number of scientific and engineering disciplines where physics-based models are used, e.g., hydrology, agriculture, climate science, materials science, power engineering and biomedicine.

Organization: 

Contact  Professor Ahmed Elgammal

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https://rutgers.zoom.us/j/2014444359?pwd=WW9ybFNCNVFrUWlycHowSHdNZjhzUT09

Meeting ID: 201 444 4359
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