Shengyi He
Columbia University
Industrial Engineering and Operations Research
500 W. 120 St.
New York, NY 10027

sh3972@columbia.edu
Phone: +1 (646) 206-1390

RESEARCH INTERESTS
I work in the intersection of statistics and machine learning with operations research. My PhD
research topics include error reduction and uncertainty quantification in Monte Carlo methods andstochastic optimization.
EDUCATION
Columbia University
Ph.D. Operations Research
M.S. Operations Research
Overall GPA: 4.24/4.33

New York, NY
September 2019 - April 2024
September 2019 - June 2020

Peking University
B.S. Statistics
Overall GPA: 3.8/4.0

Beijing, ChinaSeptember 2015 - June 2019

WORK EXPERIENCE
Citadel Securities
Quantitative Researcher
Quantitative Research InternMiami, FL
July 2024 June - August 2023

AmazonResearch Scientist Intern

Bellevue, WAMay - August 2022

AWARDS AND FELLOWSHIPS• INFORMS I-SIM PhD Colloquium Award, Winter Simulation Conference (WSC) 2021
• Presidential Fellowship, Columbia University, 2019
• Outstanding Graduate, Peking University, 2019
• Yizheng Scholarship, Peking University, 2018
• Founder Scholarship, Peking University, 2017
• First Prize in Mathematics Competition of Chinese College Students, 2017• May Fourth Scholarship, Peking University, 2016
PUBLICATIONSJournal Papers• Shengyi He, Michael Fu, Guangxin Jiang, and Henry Lam. Adaptive importance samplingfor efficient stochastic root finding and quantile estimation, Articles in Advance, Operations
Research, 2023.
• Shengyi He and Henry Lam. Higher-order coverage errors of batching methods via Edgeworth expansions on t-statistics, to appear in Annals of Statistics.
• Mansur Arief, Yuanlu Bai, Wenhao Ding, Shengyi He, Zhiyuan Huang, Henry Lam, andDing Zhao. Certifiable deep importance sampling for rare-event simulation of black-boxsystems, under minor revision in Operations Research.
• Shengyi He and Henry Lam. Higher-order expansion and Bartlett correctability of distributionally robust optimization, under revision in Mathematics of Operations Research.
• Shengyi He and Henry Lam. Robust sensitivity analysis to assess input distributional
uncertainty: Asymptotic approximations and computations, in preparation.
• Shengyi He and Henry Lam. Achieving higher-order coverage accuracy with computationally cheaper iterated bootstraps, in preparation.
• Shengyi He and Henry Lam. Statistical optimality in low-computation uncertainty quantification, in preparation.
Conference Proceedings• Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He,Wenhao Ding, Henry Lam, and Ding Zhao. Deep Probabilistic Accelerated Evaluation: Arobust certifiable rare-event simulation methodology for black-box safety-critical systems,
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics(AISTATS), 2021.
• Shengyi He and Henry Lam. Higher-order coverage error analysis for batching and sectioning, Proceedings of the Winter Simulation Conference (WSC), 2021.
[WSC 2021 INFORMS I-SIM PhD Colloquium Award]• Shengyi He and Henry Lam. Batching on biased estimators, Proceedings of the Winter
Simulation Conference (WSC), 2022.
• Yuanlu Bai, Shengyi He, Henry Lam, Guangxin Jiang, and Michael Fu. Importance sampling for rare-event gradient estimation, Proceedings of the Winter Simulation Conference(WSC), 2022.
• Shengyi He and Henry Lam. Optimal batching under computation budget, Proceedings ofthe Winter Simulation Conference (WSC), 2023.

TALKS• INFORMS Annual Meeting, Oct 2023
Winter Simulation Conference (WSC), Dec 2023
Title: Optimal batching under computational budget• Winter Simulation Conference (WSC), Dec 2022
Title: Batching on biased estimators• Winter Simulation Conference (WSC), Dec 2021
Title: Higher-order coverage error analysis for batching and sectioning• Canadian Operational Research Society (CORS), Jun 2021
SIAM Conference on Uncertainty Quantification, April 2022
Cornell Young Researchers Workshop (poster), Oct 2022
SIAM Conference on Optimization, June 2023
Title: Adaptive importance sampling for efficient stochastic root finding and quantile estimation• INFORMS Annual Meeting, Nov 2020
Title: Sequential importance sampling in stochastic root-finding and optimization• PhD seminar, Oct 2020
New England Statistics Symposium (NESS), May 2022
International Conference on Continuous Optimization (ICCOPT), July 2022
INFORMS Annual Meeting, Oct 2022
Title: Higher-order expansion and Bartlett correctability of distributionally robust optimization
RESEARCH PROJECTS• The asymptotic behavior of maximum likelihood estimator for partially observable diffusion
processes, advisor: Chenxu Li, Peking University, 2017-2019• Deep learning methods for Airbnb room quality analysis, advisor: David Simchi-Levi, MIT,2018
TEACHING EXPERIENCE
Teaching assistant at Columbia University for• IEOR E4630: Asset Allocation, Spring 2024
• IEOR E4004: Optimization Models and Methods, Spring 2023
• IEOR 6711: Stochastic Modeling I (a PhD core course), Fall 2020, Fall 2021, Fall 2023
• IEOR E4725: Networks: Formation, Contagion, and Epidemics, Spring 2021

SKILLS• Computer skills: Python, C/C++, Matlab, SQL, R• Language: Mandarin (native), English (advanced)