Hi there, welcome to my homepage! I’m Wenjing Chen, a PhD student in the Department of Computer Science & Engineering at Texas A&M University. I’m advised by Dr. Victoria G.Crawford.

I received my MS in Electrical Engineering from Texas A&M University and my BS in Applied Mathematics from the University of Science and Technology of China. My research focuses on combinatorial optimization (particularly submodular maximization), reinforcement learning, and efficiency in large language models.

In Summer 2025, I interned as a Machine Learning Engineer at Meta on the Ads Delivery team, where I worked on Bayesian optimization and resource allocation problems for infrastructure optimization.

News

• (05/2025) Completed summer internship at Meta as Machine Learning Engineer in Ads Delivery.

• (05/2025) Paper “Adaptive Threshold Sampling for Pure Exploration in Submodular Bandits” accepted at UAI 2025.

• (01/2025) Two papers accepted: “Linear Submodular Maximization with Bandit Feedback” at AISTATS 2025 and “Fair Submodular Cover” at ICLR 2025.

• (09/2024) Paper “Bicriteria Approximation Algorithms for the Submodular Cover Problem” accepted at NeurIPS 2024.

• (05/2022) Paper “Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality” accepted at 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 and “On Top-k Selection from m-wise Partial Rankings via Borda Counting” in IEEE Transactions on Signal Processing.

• (03/2020) Paper “On Top-k Selection from m-wise Partial Rankings via Borda Counting” accepted at IEEE ISIT 2020.