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Ethan (Xingyuan)
Assistant Professor of Statistics
Ethan Fang


Ethan X. Fang is an Assistant Professor of Statistics at Penn State.

Fang received his Ph.D. in Operations Research and Financial Engineering from Princeton University in 2016. He received his B.S. in Mathematics from National University of Singapore in 2010.

He works on different problems from both computational and statistical perspectives.


Honors and Awards

Best Paper Prize for Young Researchers in Continuous Optimization in 2016



  • Test of Significance for High-Dimensional Longitudinal Data. Ethan X. Fang Yang Ning, Runze Li, Annals of Statistics, 2020+
  • High-dimensional Interactions Detection with Sparse Principal Hessian Matrix, Cheng-Yong Tang, Ethan X. Fang, Yuexiao Dong, Journal of Machine Learning Research, Tentatively Accepted, 2020+
  • Multi-Level Stochastic Gradient Methods for Nested Composition Optimization. Shuoguang Yang, Mengdi Wang, Ethan X. Fang, SIAM Journal on Optimization, 2019
  • Misspecified Nonconvex Statistical Optimization for Phase Retrieval. Zhuoran Yang, Lin Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov, Mathematical Programming, 2019
  • Blessing of Massive Scale: Spatial Graphical Model Estimation with a Total Cardinality Constraint Approach. Ethan X. Fang, Han Liu, Mengdi Wang, Mathematical Programming, 2019
  • Max-Norm Optimization for Robust Matrix Recovery. Ethan X. Fang, Han Liu, Kim-Chuan Toh, Wen-Xin Zhou, Mathematical Programming, 2018
  • Stochastic Compositional Gradient Descent: Algorithms for Minimizing Nonlinear Functions of Expected Values. Mengdi Wang, Ethan X. Fang, Han Liu, Mathematical Programming, 2017
  • Testing and Confidence Intervals for High Dimensional Proportional Hazards Model. Ethan X. Fang, Yang Ning, Han Liu, Journal of the Royal Statistical Society: Series B, 2017
  • Mining Massive Amounts of Genomic Data: A Semiparametric Topic Modeling Approach, Ethan X. Fang, Min-Dian Li, Michael I. Jordan, Han Liu, Journal of the American Statistical Association: Applications and Case Studies, 2017



STAT 440 -  Computational Statistics

STAT 500 - Applied Statistics

STAT 597 - Modern Optimization in Statistics

IME 322 - Probabilistic Models in Industrial Engineering