Hello! I am currently a Ph.D. candidate in the Department of Industrial Engineering and Operations Research (IEOR) at the University of California, Berkeley, advised by Prof. Max Shen. Previously, I received my Bachelor’s degree in Physics from Tsinghua University in 2016.
My research interest is mainly about data-driven decision making with uncertainty in operations management. I am seeking to provide both methodologies and practical solutions combining tools and concepts from optimization, machine learning, and statistics. I have studied interconnected problems, focusing mostly on two themes: (i) prescriptive analysis that combines prediction and optimization and (ii) distributionally robust data-driven decisions. From an applications perspective, my research investigates practical problems in supply chain management and retail operations. As a part of it, I actively collaborate with industrial partners in e-commerce.
- The interface of Operations Management and Machine Learning
- Supply Chain Management and Retail Operations
- Data-driven Prescriptive Analysis
- Distributionally Robust Optimization
I am on the 2021-2022 academic job market. I will present at INFORMS 2021 in the following sessions:
- Virtual session: VSD69, Oct 24 2:45-4:15 (PDT)
- In-person session: TD21, Oct 26, 2:45-4:15 (PDT), CC-Room 204A
Job market papers:
- A Practical End-to-End Inventory Management Model with Deep Learning
- Integrated Conditional Estimation-Optimization
- The manuscript of my paper “Integrated Conditional Estimation-Optimization” (joint work with Prof. Paul Grigas and Prof. Zuo-Jun Shen) is available (here).
- The End-to-End inventory management model is shortlisted as semi-finalists of Gartner Power of the Profession Supply Chain Awards 2022. The finalists and winners will be announced later. See more about this award and the paper.