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Ellen Vitercik joins MS&E as Assistant Professor

Prof. Vitercik's research revolves around artificial intelligence, algorithm design, and the interface between economics and computation, with a focus in machine learning theory.
Professor Ellen Vitercik

MS&E welcomes Ellen Vitercik to our faculty!

Prof. Vitercik received her PhD in Computer Science from Carnegie Mellon University, where she was co-advised by Maria-Florina Balcan and Tuomas Sandholm. Her dissertation, "Automated algorithm and mechanism configuration," won the 2021 Association for Computing Machinery Special Interest Group on Economics and Computation (ACM SIGecom) Dissertation Award. At Stanford, Prof. Vitercik holds joint appointments in the departments of Management Science and Engineering and Computer Science.

We caught up with Prof. Vitercik to ask about her current work and why she chose MS&E. Read the brief Q&A below.

Can you describe your recent work and the impact you hope it makes, both in your field and in society?

An important property of algorithms used in practice is broad applicability—the ability to solve problems across diverse domains. For example, integer programming solvers are used to solve problems in scheduling, routing, manufacturing, planning, finance, and many other areas. These broadly applicable algorithms, however, can have unsatisfactory default, out-of-the-box performance on problems in any one specific domain, for example slow runtime or poor solution quality, among other downfalls. In my research, I study how we can use data about specific domains together with machine learning to optimize an algorithm’s performance in a given situation.

The major application areas of my research have been the design and optimization of algorithms for mathematical programming (in particular, integer linear, integer quadratic, and linear programming), as well as algorithms for economics, or in other words, the mechanisms and auctions we use to sell goods. These types of mechanisms can also be optimized using data and machine learning to improve their revenue. 

I believe this area has the potential to transform algorithm design, both from a theoretical and practical perspective, and that data-driven approaches will be integral to the design of large-scale, commercial algorithms going forward.

What does being a part of MS&E mean, or what makes it the right place for you?

I'm excited to join MS&E because it's a department that values research on the technical, quantitative foundations of operations research—such as mathematical programming—as well as research on the broader impact of technology on society—such as the repercussions of using machine learning in economic contexts. I can't wait to work with the department's incredible faculty and students. I also look forward to being a part of the broader Stanford community, interacting with researchers in computer science, statistics, and economics.