Junchi Yan, Shanghai Jiao Tong University, China
Dr. Junchi Yan is currently an Associate Professor with Department of Computer Science and Engineering, Shanghai Jiao Tong University. He is currently the PIs of the MOST 2030 Next AI Major Project and the NSFC Outstanding Youth Fund, and the Chief Expert for MOE curricula development in the Deep Learning Area. He was once the (Principal) Researcher with IBM Research from 2011-2018. He was the awardee of CCF Outstanding PhD Thesis. His main research interests include machine learning and its intersection with quantum computing and operational research. He has published nearly 100 CCF-A papers as first/correspondence authors and have 30+ authorized US patents, with Google Scholar citations over 8000. He regularly served as AC for ICML/NeurIPS/CVPR etc. and is on the editorial board of Pattern Recognition Journal.
Speech Title: Machine Learning for Graph and Combinatorial Problems and Beyond
Graph and combinatorial problems often incur inherent high complexity, and the traditional solvers are mostly based on human expert design which can be costive and challenging. On the other hand, data-driven approaches like machine learning, especially deep learning, have shown promising impact in many perception tasks while their role in the above problems are relatively in its early stage. In this talk, I will share our recent progress in the area of machine learning for solving combinatorial problems on graphs, and their applications in EDA, federated learning, etc. I will also briefly give some initial results on quantum graph learning. The results o f this talk have been published in TPAMI、NeurIPS、ICML、ICLR, SIGKDD, CVPR.