Ethan X. Fang

I am an associate professor at the Department of Biostatistics & Bioinformatics and affiliated with Rhodes Information Initiative and the Decision Sciences Group of Fuqua School of Business of Duke University. Previously, I was an assistant professor of Statistics at 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 Han Liu and Robert Vanderbei as my advisors for my thesis with Jianqing Fan and Mengdi Wang in my committee. 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 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. My current research is partially sponsored by NSF, NIH, and DOD grants.

I serve as Associate Editor for Annals of Statistics and Operations Research.

Advertisement: Prof. Alex Belloni at Fuqua and me are hiring one or two postdocs. Feel free to reach out.

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."

Selected Papers

Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
Ethan X. Fang, Yajun Mei, Yuyang Shi, Qunzhi Xu, Tuo Zhao
Journal of Machine Learning Research, 2023
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Shuting Shen, Xi Chen, Ethan X. Fang, Junwei Lu
Abstract at EC'23
PASTA: Pessimistic Assortment Optimization.
Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi,, Ethan X. Fang, Vahid Tarokh
Short version at ICML'23
Data-Driven Compositional Optimization in Misspecified Regimes
Shuoguang Yang, Ethan X. Fang, Uday Shanbhag
Operations Research, Accepted, 2024+
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
Yi Chen, Yining Wang, Ethan X. Fang, Zhaoran Wang, Runze Li
Journal of the American Statistical Association, Accepted, 2022+
Lagrangian Inference for Ranking Problems
Yue Liu, Ethan X. Fang, Junwei Lu
Operations Research, Accepted, 2022+

Fairness-Oriented Individualized Treatment Regime
Ethan X. Fang, Zhaoran Wang, Lan Wang
Journal of American Statistical Association, Accepted, 2022+
Offline Personalized Pricing with Censored Demand
Zhengling Qi, Jingwen Tang, Ethan X. Fang, Cong Shi
Test of Significance for High-Dimensional Longitudinal Data
Ethan X. Fang, Yang Ning, Runze Li
Annals of Statistics, 2020
Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials Using Sparse Linear Programming
Michael Rosenblum, Ethan X. Fang, Han Liu
Journal of Royal Statistical Society: Series B, 2020
[JHU Biostat] [Code]
High-dimensional Interactions Detection with Sparse Principal Hessian Matrix
Cheng-Yong Tang, Ethan X. Fang, Yuexiao Dong
Journal of Machine Learning Research, 2020
Constructing a Confidence Interval for the Fraction Who Benefit from Treatment, Using Randomized Trial Data
Emily Huang, Ethan X. Fang, Daniel Hanley, Michael Rosenblum
Biometrics, Accepted, 2020+
[JHU Biostat]
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
2016 IMS Laha/Travel Award
[Optimization Online]
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
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]
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]
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, 2017
2016 ENAR Distinguished Student Paper (2/2)
[JHU Biostat]
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]
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Yan Li, Ethan X. Fang, Huan Xu, Han Liu
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...