Yingxi Li

prof_pic.jpg
yingxi at stanford dot edu

Hi, I’m Yingxi Li, a third year PhD candidate in Operations Research, Management Science and Engineering (MS&E) department at Stanford University. I am fortunate to be advised by Professor Ellen Vitercik. Previously, I graduated with a B.S. from the Operations Research and Information Engineering (ORIE) department of Cornell University, where I was also lucky enough to work with Professor Madeleine Udell and Professor Shane Henderson.

My interest sits at the intersection of artificial intelligent, algorithm, and discrete optimization. I am particularly interested in using machine learning to design faster, more scalable, and robust methods for discrete optimization problems. I am also interested in algorithmic reasoning capabilities of large language models (LLMs) with an eye toward leveraging them for algorithm design.

My research is generously supported by the Amazon Core AI Fellowship and Stanford MS&E departmental fellowship.

Publication and Preprints

* means equal contribution. For theory papers, authors are always listed alphabetically by last name.

  1. Smoothed Analysis of Online Metric Matching with a Single Sample: Beyond Metric Distortion
    Yingxi Li, Ellen Vitercik, and Mingwei Yang
    In Innovations in Theoretical Computer Science (ITCS), 2026
    arXiv
  2. Accelerating data-driven algorithm selection for combinatorial partitioning problems
    Vaggos Chatziafratis, Ishani Karmarkar, Yingxi Li, and Ellen Vitercik
    In Conference on Neural Information Processing Systems (NeurIPS), spotlight (top 3% of all submissions), 2025
    arXiv Code
  3. Can LLMs Reason Structurally? An Evaluation via the Lens of Data Structures
    Yu He*, Yingxi Li*, Colin White, and Ellen Vitercik
    In ICML AI4Math workshop, 2025
    arXiv Dataset Code
  4. LLMs for Cold-Start Cutting Plane Separator Configuration
    Connor Lawless*, Yingxi Li*, Anders Wikum*, Madeleine Udell, and Ellen Vitercik
    In Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2025
    arXiv Code

Miscellaneous

I pronounce my name as either EENG-shee or yìng-xī (first syllabal falling down, second syllabal with a high and even tone). I went by Diana in high school and the first year of undergrad, and still use it as my Starbucks name.