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Our paper SCAN on networked multi-agent reinforcement learning for decentralized anomaly detection received the Best Refereed Paper Award of the INFORMS QSR section at the 2026 INFORMS Annual Meeting, San Francisco.

Learning Collision-Free Dispatch Policies for Route-Wise Decision-Dependent Anomaly Detection accepted at NeurIPS 2026.

Multi-Agent Reinforcement Learning for Anomaly Detection with Hierarchical Adaptive Sampling Strategy conditionally accepted by Technometrics.

Finalist, IISE QCRE Best Track Paper Competition, 2025 IISE Annual Meeting.

Started my Ph.D. at the University of Wisconsin-Madison.

A Deep Reinforcement Learning Method Solving Bilevel Optimization for Product Design Considering Remanufacturing published in IEEE Transactions on Engineering Management.

Preprint Multi-Agent Causal Discovery Using Large Language Models on arXiv.