Ethan X. Fang

I am an assistant professor at the Department of Statistics and Department of Industrial and Manufacturing Engineering of Penn State University.

I received my PhD in Operations Research and Financial Engineering from Princeton University. During my time at Princeton, I was extremely fortunate to have Profs. Han Liu and Robert Vanderbei as my advisors. Before going to Princeton, I got my bachelor's degree from National University of Singapore, where I had the privilege to write my undergraduate thesis under the supervision of Prof. Kim-Chuan Toh. Prior to my college life, I spent three wonderful years at Chengdu No.7 High School.

I work on different problems from both statistical and computational perspectives. You can find my manuscripts and awards below.

Besides academic awards, I am particularly proud of my Breathtaking Talent Award given by Princeton Graduate School, where the citation of this award is

"For a person who has a phenomenal talent outside of their academic ability, they have 'wow-ed' us with their talents and shown us the range of ability that we have in our community."


Multi-Level Stochastic Gradient Methods for Nested Composition Optimization
Shuoguang Yang, Mengdi Wang, Ethan X. Fang
SIAM Journal on Optimization, Accepted, 2018+
Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Zhuoran Yang, Lin Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov
Mathematical Programming, Accepted, 2019+
Adipocyte OGT Regulates a Fat-Sensing Adipose-to-Brain Axis That Induces Hyperphagia and Obesity
Min-Dian Li, ..., Ethan X. Fang, et al.
Nature Communications, 2018
Stochastic Compositional Gradient Descent: Algorithms for Minimizing Nonlinear Functions of Expected Values
Mengdi Wang, Ethan X. Fang, Han Liu
Mathematical Programming, 2017
2016 Best Paper Prize for Young Researchers in Continuous Optimization (1 Paper Selected Every 3 Years)
[Arxiv] [Journal]
Blessing of Massive Scale: Spatial Graphical Model Estimation with a Total Cardinality Constraint Approach
Ethan X. Fang, Han Liu, Mengdi Wang
Mathematical Programming, Accepted, 2018+
2016 IMS Laha/Travel Award
[Optimization Online]
Max-Norm Optimization for Robust Matrix Recovery
Ethan X. Fang, Han Liu, Kim-Chuan Toh, Wen-Xin Zhou,
Mathematical Programming, 2018
2017 IMS Laha/Travel Award
[Optimization Online]
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
2015 IMS Laha/Travel Award
2016 ENAR Distinguished Student Paper (1/2)
[Arxiv] [Journal] [Code]
Accelerating Stochastic Composition Optimization
Mengdi Wang, Ji Liu, Ethan X. Fang
Journal of Machine Learning Research, 2017
Advances in Neural Information Processing Systems (NIPS), 2016 (short version)
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
[PDF] [Journal]
Generalized Alternating Direction Method of Multipliers: New Theoretical Insight and Application
Ethan X. Fang, Bingsheng He, Han Liu, Xiaoming Yuan
Mathematical Programming Computation, 2015
[Journal] [PDF]
Inequality in Treatment Benefits: Can We Determine if a New Treatment Benefits the Many or the Few?
Emily Huang, Ethan X. Fang, Daniel Hanley, Michael Rosenblum
Biostatistics, Accepted, 2017
2016 ENAR Distinguished Student Paper (2/2)
[JHU Biostat]
Constructing a Confidence Interval for the Fraction Who Benefit from Treatment, Using Randomized Trial Data
Emily Huang, Ethan X. Fang, Daniel Hanley, Michael Rosenblum
[JHU Biostat]
Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials Using Sparse Linear Programming
Michael Rosenblum, Ethan X. Fang, Han Liu
[JHU Biostat]
Using a Distributed SDP Approach to Solve Simulated Protein Molecular Conformation Problems
X.Y. Fang, Kim-Chuan Toh
Distance Geometry: Theory, Methods, and Applications, A. Mucherino, C. Lavor, L. Liberti, and N. Maculan eds., Springer, 2013, pp. 351--376.

More to come...