Invited Speakers

Speaker Name: Profressor Robert Tibshirani, Stanford University

Talk Title: Statistical Learning and Sparsity

Biography:

Rob Tibshirani is a Professor of Statistics, and Biomedical Data Science at Stanford University. His main interests are in applied statistics, biostatistics, and data science. He is most well-known for the LASSO, which is a shrinkage and selection method for linear regression. He is the co-author of the books Generalized Additive Models (with T. Hastie), An Introduction to the Bootstrap (with B. Efron), An Introduction to Statistical Learning (with G. James, D. Witten and T. Hastie), Sparsity in Statistics (with T. Hastie and M. Wainwright) and the widely used Elements of Statistical Learning (with T. Hastie and J. Friedman). His current research focuses on problems in biology and genomics, medicine, and industry.

Speaker Name: Professor Po-Ling Loh, University of Wisconsin-Madison

Talk Title: Data science for networked data

Biography:

Po-Ling Loh is an assistant professor in the ECE department at the UW-Madison, with a secondary appointment in the statistics, computer science, and industrial and systems engineering departments. From 2014-2016, Po-Ling was an assistant professor in the statistics department at the Wharton School at the University of Pennsylvania. Po-Ling received an MS in computer science and a PhD in statistics from Berkeley in 2013 and 2014, and a BS in math with a minor in English from Caltech in 2009. She was the recipient of the 2014 Erich L. Lehmann Citation from the Berkeley statistics department for an outstanding PhD dissertation in theoretical statistics, and a best paper award at the NIPS conference in 2012. Po-Ling is a recipient of an NSF CAREER award in statistics.

Speaker Name: Professor Zhi-Hua Zhou, Nanjing University

Talk Title: An exploration to non-NN deep models based on non-differentiable modules

Biography:

Zhi-Hua Zhou is a Professor of Computer Science and Artificial Intelligence at Nanjing University. He is the founding director of the LAMDA Group and head of the department of computer science. His main research interests are in machine learning and data mining, involving ensemble methods, weakly supervised learning, multi-label learning, etc. He authored the books "Ensemble Methods: Foundations and Algorithms" and "Machine Learning (in Chinese)", and published more than 200 papers in top-tier international journals/conferences. According to Google Scholar, his publications have received more than 35,000 citations, with an H-index of 90. He has received various awards, including the National Natural Science Award of China, PAKDD Distinguished Contribution Award, Microsoft Professorship Award, etc. He served as General chair of IEEE ICDM 2016, Program chair of AAAI 2019, IJCAI 2015 Machine Learning track, SDM 2013, etc. He is a Fellow of the ACM, AAAI, AAAS, IEEE and IAPR.