Jiashun Jin, Carnegie Mellon University, USA 

Jiashun Jin is Professor in Statistics & Data Science and an Affiliated Professor in Machine Learning at Carnegie Mellon University. His earlier work is on Higher Criticism and phase transition for rare and weak signals. His more recent interest is on the analysis of complex network and text data where he has proposed several interesting tools including SCORE. He has also led a team generating a large-scale new data set on the publications of statisticians called the MADStat. Professor Jin is an elected fellow of both the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). He has delivered the IMS Medallion Lecture (2015) and the IMS Annals of Applied Statistics Lecture (2016), and is a recipient of the NSF CAREER Award and the IMS Tweedie Award. He is currently serving as the IMS Treasurer and executive editor of SLADS, a recently launched international journal on statistical learning and data science. Beyond academia, he has gained industry experience through his work at Two-Sigma Investments and Google LLC

Can Le, University of California, Davis, USA 

Can Le is an Associate Professor in the Department of Statistics at the University of California, Davis. He earned his Ph.D. in Statistics from the University of Michigan, Ann Arbor. His research interests include network analysis, high-dimensional statistics, and random matrix theory. In particular, he has developed theory and methodology for the regularization of sparse random networks, network clustering, and inference for network-linked data. More recently, he has also become interested in the theoretical properties of neural networks and their applications. His research has been published in leading journals in statistics and machine learning, including JASA, AoS, and JMLR.