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.

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."

Papers

Stochastic Compositional Gradient Descent: Algorithms for Minimizing Nonlinear Functions of Expected Values
Mengdi Wang, Ethan X. Fang, Han Liu
Mathematical Programming Series A, 161(1), pp 419-449, 2017
2016 Best Paper Prize for Young Researchers in Continuous Optimization (1 Paper Selected Every 3 Years)
[Arxiv] [Journal]
Generalized Alternating Direction Method of Multipliers: New Theoretical Insight and Application
Ethan X. Fang, Bingsheng He, Han Liu, Xiaoming Yuan
Mathematical Programming Computation, 7(2), pp 149-187, 2015
[Journal] [PDF]
Max-Norm Optimization for Robust Matrix Recovery
Ethan X. Fang, Han Liu, Kim-Chuan Toh, Wen-Xin Zhou,
Mathematical Programming, Accepted, 2017+
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, Accepted, 2016+
2015 IMS Laha/Travel Award
2016 ENAR Distinguished Student Paper (1/2)
[Arxiv] [Journal] [Code]
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, Accepted, 2016+
[PDF] [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, Accepted, 2016+
2016 ENAR Distinguished Student Paper (2/2)
[JHU Biostat]
Blessing of Massive Scale: Spatial Graphical Model Estimation with a Total Cardinality Constraint Approach
Ethan X. Fang, Han Liu, Mengdi Wang
2016 IMS Laha/Travel Award
[Optimization Online]
Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials Using Sparse Linear Programming
Michael Rosenblum, Ethan X. Fang, Han Liu
[JHU Biostat]
Accelerating Stochastic Composition Optimization
Mengdi Wang, Ji Liu, Ethan X. Fang
Advances in Neural Information Processing Systems (NIPS), 2016 (short version)
[Arxiv]
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.
[PDF]

More to come...