Ying Zhu

I value rigorous and interdisciplinary methodological and theoretical research in data science (such as statistics, econometrics, machine learning, and business analytics) that is potentially applicable to many real-world problems. 

Education: PhD, Haas School of Business, U.C. Berkeley (2010-2015); MA, Dept. of Statistics, U.C. Berkeley (2012-2013); MS, Dept. of Civil and Environmental Engineering (Transportation Operations Research), M.I.T.; BA, Dept. of Mathematics, Carroll College, MT

Current Employment: Research Associate in “Big Data Analytics”, Dept. of Economics, Social Science Data Analytics Initiative, Michigan State University, MI (September 2015 – Present)

Methodological Areas: High-Dimensional (Causal) Estimation and Inference, Nonasymptotic Statistics, Semiparametric Models, Panel/Longitudinal Data Models (with Both i.i.d. and Temporal Features)Nonparametric Estimation 

Applications of Interest: Big Data Methods for Problems in Social Science and Business in general such as Education, Healthcare, Industrial Organization, Management, Marketing Strategy, Trade and Growth, Transportation

Working Papers
  • High Dimensional Inference in Partially Linear Models by Ying Zhu, Zhuqing Yu, Guang Cheng 
  • Inference in Linear Models with Correlated Random Effects and Unbalanced Panel Data by Jeffrey Wooldridge, Ying Zhu (alphabetical author ordering) 
  • Behavior of Pooled and Joint Estimators in Probit Model with Random Coefficients and Serial Correlation by Alyssa Carlson, Jeffrey Wooldridge, Ying Zhu 

PhD Dissertation 
Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications - University of California, Berkeley, Spring 2015

M.S. Thesis 
Evaluating Airline Delays: The Role of Airline Networks, Schedules, and Passenger Demands School of Engineering, Massachusetts Institute of Technology, Feb. 2009. (Results from this thesis appeared in ABC, CBS, and NBC in 2007)

Email: yzhu@msu.edu; yingzhu@berkeley.edu