About Me

I'm an Assistant Professor of Information Science at Cornell University. My research interests lie broadly at the intersection of economics and computer science, focusing on algorithmic fairness. My projects apply computational methods, such as machine learning and causal inference, to study societal inequities in domains including online services and public health. I am regularly quoted as an expert on disparities in automated speech-to-text systems.

Previously, I was a postdoc at Microsoft Research New England in the Machine Learning and Statistics group. Before that, I received my PhD from Stanford's Institute for Computational & Mathematical Engineering under the guidance of my reading committee: Susan Athey, Sharad Goel, and Hal Varian. Awards won include the NSF Graduate Research Fellowship and Forbes 30 Under 30 in Science.

You can reach me at: koenecke at cornell.edu


At Cornell, I teach a range of Information Science classes, including Introduction to Data Science (Fall '22, '23), Data Science for Global Development (Spring '23), and Algorithmic Fairness (Spring '24).

I spent several summers during my PhD interning as a data scientist at Facebook, Google, and Microsoft. Much of my recent research focuses on fairness in algorithmic systems developed by big tech companies (such as speech-to-text, online ad targeting services, recommendation systems, and A/B experimentation pipelines). During my PhD, I also co-founded Women in Math, Stats, and Computational Engineering (WiMSCE).

Prior to beginning my graduate studies, I received my Bachelor's in Mathematics with Computer Science from MIT, and then worked at NERA Economic Consulting in New York (leading teams of research analysts in the M&A and antitrust litigation space).


Selected Publications

   (more on Google Scholar)

In Preparation