Speaker: Associate Professor Liang Zhao, Emory University
Date and Time: September 22, 2:00-3:00 p.m.
Location: Coda, Room 230
Host: School of CSE Assistant Professor Anqi Wu

Title: Representation Learning and Data Generation Guided by Structural and Functional Properties

Abstract: A core capability of deep learning is to learn the mapping between data and its representation. It results in two critical tasks: representation learning and data generation, which correspond to the mapping from data to representation, and the mapping from representation to data, respectively. On the other hand, in scientific and societal areas, a core goal is to explore the relation between data structure and its function, such as the molecule structure and its chemical properties, brain connectivity and mental disease, human trajectory and behavior and so on. Until now, the exploration of structure and function relation is still a largely open problem with two grand challenges: 1. Which structural properties determine a specific function? (e.g., what structural biomarkers indicate potential Alzheimer’s disease) 2. How to design a structure that has a specific function? (e.g., how to design a molecule that possesses specific properties of interests). In this talk, I will introduce our recent works on structure-guided representation learning to pursue the direction toward the first challenge and works on property-controllable data generation toward the second one. I will start with the background, and then introduce our recent work on representation learning of complex-structure data. Then I will briefly talk about our works on introducing structural inductive bias to guide the representation learning. Finally, I will talk about controllable data generation as a multi-objective optimization of the representations.

Bio: Dr. Liang Zhao is an associate professor at the Department of Computer Science at Emory University. Before that, he was an assistant professor in the Department of Information Science and Technology and the Department of Computer Science at George Mason University. He obtained his Ph.D. degree as Outstanding PhD student in 2016 from Computer Science Department at Virginia Tech in the United States. His research interests include data mining and machine learning, with special interests in spatiotemporal and network data mining, deep learning on graphs, nonconvex optimization, and interpretable machine learning. He has published over a hundred papers in top-tier conferences and journals such as KDD, TKDE, ICDM, ICLR, NeurIPS, Proceedings of the IEEE, TKDD, CSUR, IJCAI, AAAI, and WWW. He won NSF Career Award in 2020 and Jeffress Trust Award in 2019. He also won Amazon Research Award in 2020, Meta Research Award, and CRA Computing Innovation Mentor in 2021. He was ranked as “Top 20 Rising Star in Data Mining” by Microsoft Search in 2016. He won several the Best Paper Award and Candidates such as Best Paper Award in ICDM 2019, Best Paper Candidate in ICDM 2021, Best Paper Award Shortlist in WWW 2021, and Best paper Candidate in ACM SIGSPATIAL 2022. He is an IEEE senior member.