UMN Machine Learning Seminar: Deep Graph Learning for Drug Property Prediction

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Junzhou Huang (University of Texas at Arlington), will be giving a talk titled "Deep Graph Learning for Drug Property Prediction."

Abstract

Graphs are powerful mathematical structures to describe relations or interactions among objects in different fields, such as biology, social science, economics and so on. Recent technological innovations are enabling scientists to capture enormous graph-structured data at increasing speed and scale. Thus, a compelling need exists to develop novel learning tools to foster and fuel the next generation of scientific discovery in graph data related research. However, the major computational challenges are due to the unprecedented scale and complexity of complex graph data analytics. There is a critical need for large-scale learning strategies with theoretical guarantees to bridge the gap and facilitate knowledge discovery from complex graph data. This talk will introduce our recent work on developing novel deep graph learning methods to efficiently and effectively process atom graph data for predicting the chemical or biological properties of drug molecules.

Biography

Dr. Junzhou Huang is a professor in the department of computer science and engineering at the University of Texas, Arlington. He received the Ph.D. degree in Computer Science at Rutgers, The State University of New Jersey. His major research interests include machine learning, computer vision, computational pathology, computational drug discovery and clinical science. He was selected as one of the 10 emerging leaders in multimedia and signal processing by the IBM T.J. Watson Research Center in 2010. His work won the MICCAI Young Scientist Award 2010, the FIMH Best Paper Award 2011, the STMI Best Paper Award 2012, the MICCAI Best Student Paper Award 2015, the 1st place of the Tool Presence Detection Challenge at M2CAI 2016, the 6th place in the 3D Structure Prediction Challenge and the 1st place in the Contact and Distance Prediction Challenge at CASP14, 2020 and the Google TensorFlow Model Garden Award 2021. He received the NSF CAREER Award 2016.

Start date
Thursday, Nov. 4, 2021, Noon
End date
Thursday, Nov. 4, 2021, 1 p.m.
Location

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