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I am a theoretical computer scientist working at the intersection of randomized algorithms, online decision-making, and stochastic optimization. I study how to design and analyze algorithms for sequential decision-making under uncertainty, with a particular emphasis on online matching, resource allocation, submodular maximization, and related stochastic optimization problems.

My research aims to bridge discrete and continuous optimization, with a particular focus on randomized online algorithms. I systematically apply established tools from functional analysis, variational calculus, and differential equations to develop analytical frameworks for addressing fundamental challenges in algorithm design and analysis. Beyond expected performance, I also study variance, robustness, and fairness, with the broader goal of establishing rigorous foundations for reliable and equitable algorithmic decision-making.

Email: panxu257 AT gmail.com