Ellen Vitercik is an Assistant Professor at Stanford University with a joint appointment between the Management Science & Engineering department and the Computer Science department. Her research revolves around machine learning theory, discrete optimization, and the interface between economics and computation. Before joining Stanford, she spent a year as a Miller Postdoctoral Fellow at UC Berkeley and received a PhD in Computer Science from Carnegie Mellon University. Her research has been recognized with an NSF CAREER award, the SIGecom Doctoral Dissertation Award, and the CMU School of Computer Science Distinguished Dissertation Award, among other honors.

AI2050 Project

Many of the most important optimization problems in practice are NP-hard, affecting critical sectors across society, such as optimizing renewable energy storage, allocating medical resources in healthcare systems, and streamlining supply chains in global logistic networks. While machine learning holds the potential to transform both applied, large-scale optimization and theoretical, combinatorial optimization, algorithmic reasoning poses a significant challenge for artificial intelligence. The goal of Ellen’s AI2050 project is to bridge this gap by aligning machine learning models with algorithmic reasoning tasks to enhance existing optimization algorithms, help practitioners select among algorithms, and develop entirely new algorithms.