Most recent CV can be found here.

Education

• Ph.D. in Statistics, Rutgers University, 2020
• M.S. in Financial Statistics and Risk Management, Rutgers University, 2015
• B.S. in Physics and Computer Softwares, Peking University, 2013

Experience

• Assistant Professor, Washington State University, 2023 – present
• Postdoctoral Research Associate, Temple University, 2020 – 2023
• Summer Quantitative Associate in Machine Learning, J.P. Morgan, 2018
• Research Assistant, Center for Statistical Science, Tsinghua University, 2013-2014
• Visiting Undergraduate Researcher, Department of Materials Science, Stanford University, 2012

Talks

• Automated Kronecker Product Approximation and Its Applications in Matrix Compression, Denoising and Completion. Algorithms Seminar. Google Research. 2023.
• Discussion on “Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design”. Online Causal Inference Seminar. 2023.
• Optimal Randomized Designs for Causal Inference in the Presence of Interference. Washington State University. 2023.
• Optimizing Randomized Saturation Designs under Interference. 2022 AMS Fall Eastern Sectional Meeting. UMass, Amherst.
• Optimal Randomization Strategies for Disentangling Peer-influence from Homophily. ENAR 2022 Spring Meeting. Houston, TX.
• Optimizing Randomized and Deterministic Saturation Designs under Interference. Complex Networks: Analysis, Numerics, and Applications. University of Maryland. 2021.
• Estimating peer-influence effects under homophily: Randomized treatments and insights. CMStatistics 2021.
• Optimizing Randomized Saturation Designs under Interference. 2021 Conference on Digital Experimentation (CODE 2021).
• Automated Kronecker Product Approximation. JSM 2020
• High Dimensional Matrix Compression, Denoising and Completion using Kronecker Products. Temple University, 2020