•   When: Monday, February 14, 2022 from 02:00 PM to 03:00 PM
•   Speakers: Junzhou Huang
•   Location: ZOOM only
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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 and their applications to molecule graph data for predicting the chemical or biological properties of drug molecules.

Bio:

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, big data analytic, medical image analysis and bioinformatics. 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, 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. He enjoys to develop efficient algorithm! s with nice theoretical guarantees to solve practical problems involved large scale graph data.