Ellen Vitercik

Ellen Vitercik is an Assistant Professor at Stanford University with joint appointments in the Management Science and Engineering and Computer Science departments. She holds a PhD from Carnegie Mellon University, advised by Nina Balcan and Tuomas Sandholm, and was a Miller Fellow at UC Berkeley under Michael Jordan and Jennifer Chayes. Her research focuses on machine learning, algorithm design, discrete optimization, and the intersection of economics and computation. She explores how machine learning can enhance discrete optimization and algorithmic reasoning, with applications to fairness, efficiency, and decision-making systems.

Her work has garnered prestigious awards including the Schmidt Sciences AI2050 Early Career Fellowship and the NSF CAREER Award. Her doctoral thesis received multiple accolades, including the SIGecom Doctoral Dissertation Award and the CMU School of Computer Science Distinguished Dissertation Award. Notable research contributions include developing neural algorithmic reasoning frameworks, evaluating LLMs' structural reasoning abilities, and advancing techniques for optimization formulation equivalence checking.

Her academic contributions span algorithm design, learning-augmented optimization, and mechanism design. She has pioneered methods for offline tuning in online decision-making systems and explored data-driven approaches for algorithm selection and configuration. Her research often bridges theoretical foundations with practical applications, addressing challenges in marketing, resource allocation, and social network dynamics.

Ellen's interdisciplinary work integrates computer science, economics, and operations research, with a focus on creating scalable, fair, and efficient systems. Her current projects include investigating cold-start algorithm configuration using LLMs, optimizing decision-making under uncertainty, and designing robust systems resilient to data limitations.