Proceedings of the 4^th International Conference
on Statistics: Theory and Applications (ICSTA'22)

Dr. Jianqing Fan
Princeton University, USA

Jianqing Fan is Frederick L. Moore Professor, Princeton University. After receiving his Ph.D. from the University of California at Berkeley, he has been appointed as professor at the University of North Carolina at Chapel Hill (1989-2003), the University of California at Los Angeles (1997-2000), and professor at the Princeton University (2003–). He was the past president of the Institute of Mathematical Statistics and International Chinese Statistical Association. He is co-editing _Journal of Business and Economics Statistics _and was the co-editor of The Annals of Statistics, Probability Theory and Related Fields, and Journal of Econometrics. His published work on statistics, economics, finance, and computational biology has been recognized by The 2000 COPSS Presidents’ Award, The 2007 Morningside Gold Medal of Applied Mathematics, Guggenheim Fellow, P.L. Hsu Prize, Royal Statistical Society Guy medal in silver, Noether Senior Scholar Award, and election to Academician of Academia Sinica and follows of IMS, ASA, AAAS and SoFiE.

Topic of Plenary:
Structural Deep Learning in Conditional Asset Pricing

Dr. Yi Li
University of Michigan, USA

Yi Li is a Professor of Biostatistics and Professor of Global Public Health at the University of Michigan (UM). After receiving a Ph.D. from the UM in 1999, he was an Assistant Professor and Associate Professor at the Harvard School of Public Health for 12 years before rejoining the UM to lead a major research center in 2011. He has made important contributions in a wide range of statistical areas, including data science, survival analysis, high-dimensional data analysis, measurement error problems, spatial data analysis, random-effects models, clinical trial design, and high-dimensional data analysis. He has published more than 200 papers in major statistical journals as well as in premium medical journals. He has led or is leading numerous federal projects funded by NIH. He is an ASA fellow, has served as a regular member for several NIH study sessions and is serving as an associate editor for 6 major statistical journals.

Topic of Plenary:
Machine Learning for Precision Medicine: Model Selection, Estimation, and Inference

Dr. Matias D. Cattaneo
Princeton University, USA

Matias D. Cattaneo is a Professor of Operations Research and Financial Engineering (ORFE) at Princeton University, where he is also an Associated Faculty in the Department of Economics, the Center for Statistics and Machine Learning (CSML), and the Program in Latin American Studies (PLAS). His research spans econometrics, statistics, data science and decision science, with particular interests in program evaluation and causal inference. Most of his work is interdisciplinary and motivated by quantitative problems in the social, behavioral, and biomedical sciences. As part of his main research agenda, he has developed novel semi-/non-parametric, high-dimensional, and machine learning inference procedures with demonstrably superior robustness to tuning parameter and other implementation choices. Matias was elected Fellow of the Institute of Mathematical Statistics (IMS) in 2022. He also serves in the editorial boards of the Journal of the American Statistical Association, Econometrica, Operations Research, Econometric Theory, the Econometrics Journal, and the Journal of Causal Inference. In addition, Matias is an Amazon Scholar, and has advised several governmental, multilateral, non-profit, and for-profit organizations around the world.

Matias earned a Ph.D. in Economics in 2008 and an M.A. in Statistics in 2005 from the University of California at Berkeley. He also completed an M.A. in Economics at Universidad Torcuato Di Tella in 2003 and a B.A. in Economics at Universidad de Buenos Aires in 2000. Prior to joining Princeton in 2019, he was a faculty member in the departments of economics and statistics at the University of Michigan.

Topic of Keynote:
On Binscatter

Dr. Christopher Hans
The Ohio State University, USA

Christopher M. Hans is an Associate Professor in the Department of Statistics at The Ohio State University, where he has been a faculty member since receiving his Ph.D. in Statistics and Decision Sciences from Duke University in 2005. His research has focused on aspects of Bayesian regression modeling, including computational methods for Bayesian model averaging with many predictors. His recent work has concentrated on studying how commonly used prior distributions for regression coefficients impact posterior inference, which has led to the development of new, structured priors that avoid a range of undesirable behaviors. He also has a long-standing interest in understanding and advancing connections between Bayesian regression and penalized optimization approaches to regularized regression.

Selected in 2022 as a Fellow of the American Statistical Association, Hans is also a lifetime member of the International Society for Bayesian Analysis. He has served as an associate editor for Bayesian Analysis, Computational Statistics and Data Analysis, the Journal of American Statistical Association, and the Journal of Computational and Graphical Statistics. At Ohio State he serves as Director of the Statistics Department’s undergraduate majors and minors, including the interdisciplinary major in Data Analytics that he co-developed and has co-directed since 2014.

Topic of Keynote:
Prior Dependence in L1-regularized Bayesian Regression

Dr. Kimberly Sellers
Georgetown University, USA

Kimberly F. Sellers, Ph.D. is a Professor of Mathematics and Statistics, specializing in Statistics at Georgetown University in Washington, DC; and a Principal Researcher with the Center for Statistical Research and Methodology Division of the U.S. Census Bureau. Prof. Sellers completed her BS and MA degrees in Mathematics at the University of Maryland College Park, and then obtained her PhD in Mathematical Statistics at The George Washington University. Her research areas of interest and expertise are in generalized statistical methods involving count data that contain data dispersion; and in image analysis techniques, particularly low-level analyses including preprocessing, normalization, feature detection, and alignment. Prof. Sellers held previous faculty positions at Carnegie Mellon University as a Visiting Assistant Professor of Statistics, and the University of Pennsylvania School of Medicine as an Assistant Professor of Biostatistics and Senior Scholar at the Center for Clinical Epidemiology and Biostatistics before her return to the DC area. Sellers is an Elected Member of the International Statistical Institute, and an American Statistical Association (ASA) Fellow. Meanwhile, she is an active contributor to efforts to diversify the fields of mathematical and statistical sciences, both with respect to gender and race/ethnicity. She is the inaugural chairperson of the ASA’s Justice, Equity, Diversity, and Inclusion (JEDI) Outreach Group (serving in 2021-2022), and is a former Chairperson for the ASA’s Committee on Women in Statistics.

Topic of Keynote:
Dispersed Methods for Handling Dispersed Count Data

Dr. Jianjun Shi
Georgia Institute of Technology, USA

Dr. Jianjun Shi is the Carolyn J. Stewart Chair and Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology.

Dr. Shi’s research focuses on data enabled manufacturing, and system informatics and control. His methodologies integrate system informatics, advanced statistics, and control theory, and fuse engineering system models with data science methods for design and operational improvements of manufacturing systems. The technologies developed by Dr. Shi’s research group have been implemented in a wide variety of production systems and produced significant economic impacts.

Dr. Shi’s research focuses on data enabled manufacturing, and system informatics and control. His methodologies integrate system informatics, advanced statistics, and control theory, and fuse engineering system models with data science methods for design and operational improvements of manufacturing systems. The technologies developed by Dr. Shi’s research group have been implemented in a wide variety of production systems and produced significant economic impacts.

Topic of Keynote:
Machine Learning Enabled Quality Improvement in Smart Manufacturing Systems

Dr. Richard O. Sinnott
The University of Melbourne, Australia

Professor Richard O. Sinnott is Professor of Applied Computing Systems at the University of Melbourne. He has been technical lead on a multitude of large-scale international projects with emphasis on big data and security worth over $500m. This includes numerous projects in the defence, intelligence and biomedical domains. He has over 450 peer-reviewed publications across a range of computing and application-specific domains.

Topic of Keynote:
A Decade of Lessons Learned in Supporting a National Big Data Platform for Urban Research