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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 recent work develops new analytical frameworks for randomized online algorithms, including approaches based on variational calculus, ordinary differential equations, and randomized primal–dual analysis. A recurring theme is to move beyond expected performance by also quantifying variance, robustness, and fairness, with the broader goal of establishing rigorous foundations for reliable and equitable algorithmic decision-making.

Email: panxu257 AT gmail.com