PeopleofSuperintelligence

Researcher

Ellen Vitercik

Ellen Vitercik is an assistant professor at Stanford who studies how machine learning can improve algorithms for optimization. She grew up in Lincoln, Vermont, studied mathematics at Columbia, and earned a computer science PhD at Carnegie Mellon in 2021. Her research used machine learning to guide tree search and produced pricing algorithms that account for customer reviews. After a Miller fellowship at UC Berkeley, she joined Stanford's Management Science and Engineering and Computer Science departments in 2022 and received a Schmidt Sciences AI2050 Early Career Fellowship in 2024. At ICML 2026, she coauthored work on DSR-Bench, a benchmark for testing how language models reason about data structures.

Works
63
Citations
387
h-index
11

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. By 2018

    Mathematics at Columbia

    She grows up in Lincoln, Vermont. By 2018, she holds a bachelor's degree in mathematics from Columbia University and studies for a computer science PhD at Carnegie Mellon University, advised by Nina Balcan and Tuomas Sandholm.

  2. 2018

    Learning to branch

    In 2018, she coauthors Learning to Branch with Maria-Florina Balcan, Travis Dick, and Tuomas Sandholm. The paper uses machine learning to select weights for branching rules in tree search and gives bounds on the training data needed to predict performance on new problems.

  3. August 2021

    A doctorate in algorithm configuration

    She completes a computer science PhD at Carnegie Mellon in August 2021, advised by Maria-Florina Balcan and Tuomas Sandholm. Her thesis, Automated Algorithm and Mechanism Configuration, develops methods for tuning algorithm parameters with machine learning. It wins the 2021 ACM SIGecom Doctoral Dissertation Award.

  4. 2022

    Joining two Stanford departments

    In 2022, Vitercik joins Stanford as an assistant professor with joint appointments in Management Science and Engineering and Computer Science. Before the move, she works as a Miller fellow at UC Berkeley, hosted by Michael Jordan and Jennifer Chayes.

  5. 2023

    Pricing with customer reviews

    In 2023, she coauthors Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty with Wenshuo Guo, Nika Haghtalab, and Kirthevasan Kandasamy. The paper develops a pricing algorithm for markets where customer reviews help buyers assess product value. It receives the Exemplary Artificial Intelligence Track Paper Award at the 2023 ACM Conference on Economics and Computation.

  6. 2024

    An AI2050 fellowship

    She receives a Schmidt Sciences AI2050 Early Career Fellowship in 2024. Her project aims to align machine learning models with algorithmic reasoning tasks to improve optimization algorithms, help users select algorithms, and develop new ones.

  7. 2025

    Algorithms with calibrated predictions

    In 2025, she coauthors Algorithms with Calibrated Machine Learning Predictions with Judy Hanwen Shen and Anders Wikum. The paper studies how algorithms can use predictions with calibrated uncertainty, with case studies in ski rental and online job scheduling.

  8. 2026

    Testing reasoning with DSR-Bench

    At ICML 2026, she coauthors Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures with Yu He, Yingxi Li, and Colin White. The work presents DSR-Bench, with 20 data structures, 35 operations, and 4,140 problem instances for testing structural reasoning in language models.

  9. 2026

    Teaching at Stanford

    In 2026, she continues as an assistant professor in Stanford's Management Science and Engineering and Computer Science departments. Her 2026–27 courses include Introduction to Probability and Design and Analysis of Algorithms.

Career

  1. Since 2022Stanford UniversityAssistant professor of Management Science and Engineering and Computer Science
  2. University of California, BerkeleyMiller fellow

Education

  1. 2021Carnegie Mellon UniversityPhD in computer science
  2. Columbia UniversityBachelor's degree in mathematics

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Ellen Vitercik yet. Send us a free-licensed photo.

  1. Ellen Vitercik vitercik.github.io. Checked 27 October 2026.
  2. MIT Rising Stars risingstars-eecs.mit.edu. Checked 27 October 2026.
  3. [1803.10150] Learning to Branch arxiv.org. Checked 27 October 2026.
  4. Ellen Vitercik | Carnegie Mellon University Computer Science Department csd.cs.cmu.edu. Checked 27 October 2026.
  5. ACM SIGecom: Doctoral Dissertation Award sigecom.org. Checked 27 October 2026.
  6. Ellen Vitercik joins MS&E as Assistant Professor | Management Science and Engineering msande.stanford.edu. Checked 27 October 2026.
  7. [2302.09700] Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty arxiv.org. Checked 27 October 2026.
  8. Plenary Speakers/Awards - EC 2023 ec23.sigecom.org. Checked 27 October 2026.
  9. Ellen Vitercik - AI2050 ai2050.schmidtsciences.org. Checked 27 October 2026.
  10. [2502.02861] Algorithms with Calibrated Machine Learning Predictions arxiv.org. Checked 27 October 2026.
  11. publications | Ellen Vitercik vitercik.github.io. Checked 27 October 2026.
  12. [2505.24069] Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures arxiv.org. Checked 27 October 2026.
  13. Ellen Vitercik's Profile | Stanford Profiles profiles.stanford.edu. Checked 27 October 2026.