Evelyn Xiao-Yue Gong

Assistant Professor

Carnegie Mellon University

Hello. I am an Assistant Professor of Operations Management at Carnegie Mellon University’s Tepper School of Business.

My main research lies in artificial intelligence for supply chain and sustainability. I am also interested in assortment optimization, pure exploration, algorithms and data-driven decision making in general.

I completed my PhD at the Operations Research Center at MIT, advised by Professor David Simchi-Levi and Professor Jim Orlin.

During my PhD, I spent some summers in the industry, including at Google Research, at Microsoft Research AI, at HelloFresh and at D.E. Shaw.

I am highly motivated by real problems and applications.

Applicants who are interested in a Ph.D. position or a Postdoctoral position should feel free to email me. Applicants for the Ph.D. positions can also directly apply at CMU Tepper School's Operations Management Ph.D. program.

Interests

Reinforcement Learning
Supply Chain
Sustainability
Artificial Intelligence

Education

PhD in Operations Research, 2023

Massachusetts Institute of Technology
BS in Honors Mathematics, 2017

BS in Interactive Media Arts, 2017

New York University, with Summa Cum Laude

Research

(2024) Xiao-Yue Gong, Michael Johnson “How Not to Overpackage? -- Towards a Sustainable HelloFresh Service Supply Chain.”.
Preprint available upon request.

HelloFresh Website

(2023) Xiao-Yue Gong, David Simchi-Levi Bandits Atop Reinforcement Learning: Tackling Online Inventory Models With Cyclic Demands.
Forthcoming in Management Science.

Publication

(2022) Xiao-Yue Gong, Vineet Goyal, Garud N Iyengar, David Simchi-Levi, Rajan Udwani, Shuangyu Wang Online Assortment Optimization with Reusable Resources.
Published in Management Science.

Publication

(2024) Rui Peng Liu, Konstantina Mellou, Xiao-Yue Gong, Beibin Li, Thomas Coffee, Jeevan Pathuri, David Simchi-Levi, Ishai Menache Efficient Cloud Server Deployment Under Demand Uncertainty.
Major Revision at Manufacturing & Service Operations Management.

Preprint

(2023) Xiao-Yue Gong, Mark Sellke Asymptotically Optimal Pure Exploration for Infinite-Armed Bandits.
Accepted at Neural Information Processing Systems (NeurIPS).

Preprint

(2021) Jim Orlin, Xiao-Yue Gong A Fast Maximum Flow Algorithm.
Published in Networks, Nov 2020.
Published in Special Issue on Celebrating 50 Years of Networks: Part 2.

Publication

(2020) Xiao-Yue Gong, David Simchi-Levi Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings.
Accepted in ICML Workshop (Theoretical Foundations of Reinforcement Learning).

Website

(2018) Yiming Zhang Quan Vuong Kenny Song Xiao-Yue Gong, Keith Ross Efficient Entropy For Policy Gradient with Multi-Dimensional Action Space.
Accepted in ICLR Workshop.

Website

Honors

First Place in the Best Dissertation Competition, Supply Chain Management in the Post-Pandemic and AI Age Conference, 2023
Recipient, Accenture Fellowship, 2022 - 2023
Bayer Women in Operations Research Scholarship Recipient, INFORMS Analytics Society, 2021
Best Presentation Award, MIT LIDS Student Conference, 2019
Grand Prize, MIT Lockheed Martin Tech for Truth Hackathon (Supply Chain Track), 2019
Winner, MIT IDEAS Global Challenge, 2018
ARCC Best Social Good Hack Prize, MIT Bitcoin Hackathon, 2018
NYU Shanghai Provost’s Award, NYU Shanghai, 2017
Summa Cum Laude, New York University, 2017
NYU President’s Service Award, New York University, 2016
Resolution Fellowship, Resolution Project at Youth Assembly at the United Nations Headquarters, 2016
Best Venture, NYU Reynolds Changemaker Challenge, 2015

Recent Invited Talks

Duke University Fuqua Operations Management Seminar

Apr 12, 2024 How Not to Overpackage? -- Towards a Sustainable HelloFresh Service Supply Chain.

CMU SQUALL Seminar

Apr 8, 2024 How Not to Overpackage? -- Towards a Sustainable HelloFresh Service Supply Chain.

YinzOR 2023 Conference

Aug 26, 2023 Provably Optimal Reinforcement Learning for Inventory Problems with Unknown Cyclic Demands

Supply Chain Management in the Post-Pandemic and AI Age Conference

Jun 13, 2023 Data-Driven Decision Making in Operations Management

Rotman OM&S Data Science Seminar

Apr 29, 2023 Bandits Atop Reinforcement Learning: Tackling Online Inventory Models With Cyclic Demands

Wharton OID Seminar

Mar 18, 2022 Bandits Atop Reinforcement Learning: Tackling Online Inventory Models With Cyclic Demands

MIT Data Science Lab Seminar

Oct 18, 2021 Bandits Atop Reinforcement Learning: Tackling Online Inventory Models With Cyclic Demands

MIT ORC Student Seminar

Sep 24, 2021 Provably Optimal Reinforcement Learning for Online Inventory Models With Cyclic Demands

Google Intern Research Talks

Jul 22, 2021 Provably Optimal Reinforcement Learning for Online Inventory Models With Cyclic Demands

Spotlight Session at Annual INFORMS Revenue Management and Pricing Section Conference

Jun 30, 2021 Online Assortment Optimization with Reusable Resources

MIT LIDS & Stats Tea Talk

Apr 28, 2021 Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings

Teaching

I am teaching 70371 Operations Management at the Tepper School of Business at Carnegie Mellon University in Spring 2024.

Professional Services

Reviewer for Management Science, Manufacturing & Service Operations Management, Mathematics of Operations Research and others.

Session Chair for INFORMS Annual Meeting 2023.

Session Co-chair for INFORMS Annual Meeting 2021.