Jiale Chen

I am a fourth-year Ph.D. student at Stanford University, advised by Prof. Aaron Sidford. My theory research focuses on the design and analysis of graph algorithms.

In the past two years, I mainly worked on the fully dynamic \((1-\varepsilon)\)-approximate matching problem, with particular attention to the gaps between unweighted and weighted matchings, and between bipartite and non-bipartite settings. Our work [BCDLST25] and [BC26] makes significant progress toward closing these gaps by developing a meta-algorithm that converts any \((1-\mathrm{poly}(\varepsilon))\)-approximate algorithm for unweighted bipartite graphs into a \((1-\varepsilon)\)-approximate algorithm for weighted general graphs, with only \(\mathrm{poly}(\log n/\varepsilon)\) overhead.

Recently, I have been deeply excited by the rapid advancements in AI, particularly its reasoning capabilities in mathematics and coding. While I continue to immerse myself in the field, I am especially interested in advancing its potential for autonomous research and complex decision-making, understanding its societal influence including reshaping the beliefs of experts within their own domains, and aligning AI systems more deeply and robustly with human values.