Hi! I am Mo Liu, a fifth-year Ph.D. candidate in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. I am advised by Zuo-Jun (Max) Shen. I am also fortunate to work with Paul Grigas. Prior to joining Berkeley, I received my B.S. degree in Industrial Engineering from Tsinghua University in 2019.
My research interests include data-driven decision-making and machine learning (specifically statistical learning and active learning), with applications in operations management. I am particularly interested in decision-focused learning, a methodology that designs and trains prediction models to account for decision-making in downstream optimization problems. These downstream problems include real-world applications in revenue management, such as product recommendation, assortment optimization and inventory management.
Methodologies: Statistical learning, active learning, decision-focused learning.
Applied Area: Revenue management, pricing, supply chain management.
I am on the 2023-2024 academic job market. Below are my upcoming talks:
INFORMS Workshop on Data Science 2023 Best Student Paper Nominee
- Title: Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach
- Time: Room 126A, 9:40 am- 10:40 am, Oct 14, 2023
INFORMS Service Science Student Competition 2023
- Title: Pricing under the Generalized Markov Chain Choice Model: Learning through Large-scale Click Behaviors
- Time: MB63, 10:45 am-noon, Monday, October 16
INFORMS Annual Meeting Talk
- Title: Active Label Acquisition with Personalized Incentives in Assortment Optimization
- Time: CC-North 122A, 2023 2:15 PM - 3:30 PM, Tuesday Oct 17
“mo_liu” AT “berkeley.edu”