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, and my projects focus on fairness in algorithmic systems and causal inference in public health. I am regularly quoted as an expert on racial 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, where I worked in the Stanford Computational Policy Lab and the Golub Capital Social Impact Lab under the guidance of my reading committee: Susan Athey, Sharad Goel, and Hal Varian. My research was generously funded by an NSF Graduate Research Fellowship.

You can reach me at: koenecke at cornell.edu


Prior to starting my PhD at Stanford, 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). 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 (such as speech-to-text and online ad targeting services developed by big tech companies).


Selected Publications

   (more on Google Scholar)

In Preparation

  • Federated Causal Inference in Heterogeneous Observational Data (2021)

    Ruoxuan Xiong, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T. Vogelstein, and Susan Athey
    → Recorded talk: MSR Summit

  • Equitable Advertising as a Trolley Problem

    Allison Koenecke, Eric Giannella, Susan Athey, Robb Willer, and Sharad Goel

  • The Effect of Firearm Laws on Gun-Inflicted Suicides

    Allison Koenecke and Alanna Flores

Written Tutorials