About Me
Hi, welcome to my homepage! I am Wenjing Chen, a Ph.D. student in the Department of Computer Science & Engineering at Texas A&M University, advised by Dr. Victoria G. Crawford.
I received my M.S. in Electrical Engineering from Texas A&M University and my B.S. in Applied Mathematics from the University of Science and Technology of China.
My research lies at the intersection of combinatorial optimization (with a focus on submodular optimization), reinforcement learning, and efficient methods for large language models.
In Summer 2025, I worked as a Machine Learning Engineer intern at Meta (Ads Delivery), where I focused on Bayesian optimization and resource allocation problems for large-scale infrastructure systems.
In Spring 2026, I joined Meta as a research scientist.
News
(04/2026) Our paper “Multi-Agent Reinforcement Learning with Submodular Reward” was accepted to ICML 2026.
(03/2026) Joined Meta Ads Core ML as a research scientist.
(02/2026) Successfully defended my Ph.D.
(08/2025) Completed my internship at Meta as a Machine Learning Engineer on the Ads Delivery team.
(05/2025) Our paper “Adaptive Threshold Sampling for Pure Exploration in Submodular Bandits” was accepted to UAI 2025.
- (01/2025) Two papers accepted:
- “Linear Submodular Maximization with Bandit Feedback” at AISTATS 2025
- “Fair Submodular Cover” at ICLR 2025
(09/2023) Our paper “Bicriteria Approximation Algorithms for the Submodular Cover Problem” was accepted to NeurIPS 2023.
(05/2022) Our paper “Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality” was accepted to UAI 2022.
- (04/2022) Two papers accepted:
- “A New Approach to Compute Information Theoretic Outer Bounds and Its Application to Regenerating Codes” at IEEE ISIT 2022
- “On Top-k Selection from m-wise Partial Rankings via Borda Counting” in IEEE Transactions on Signal Processing
- (03/2020) Our paper “On Top-k Selection from m-wise Partial Rankings via Borda Counting” was accepted to IEEE ISIT 2020.