Keegan W. Harris

Office: GHC 8021
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
Email: keeganh [at]
Twitter: @keegan_w_harris
Google Scholar

Hello! I am a third-year Ph.D. student in the School of Computer Science at Carnegie Mellon University. I am fortunate to be advised by Nina Balcan and Steven Wu, and supported by the NDSEG Fellowship. Before coming to CMU, I graduated summa cum laude from Penn State University with Bachelor of Science degrees in Computer Science and Physics.

I am interested in topics at the intersection of machine learning, econometrics, and algorithmic game theory. Specifically, my goal is to develop principled algorithms for modern data-driven decision-making. To this end, my research has been focused on three (often interleaved) areas:

  1. Learning and decision-making using panel data: In settings such as e-commerce, content recommendation systems, and clinical trials, one often observes repeated, noisy, measurements from a collection of individuals over a period of time. Such settings are ubiquitous in today's digital world, and are often referred to as panel data settings. I am interested in leveraging the temporal structure and similarity between individuals typically present in panel data to design better algorithms for decision-making in real-world settings.
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  2. Algorithmic decision-making under incentives: When algorithmic assessment tools are used in high-stakes domains such as lending, education, or employment, decision-subjects have an incentive to modify their input to the algorithm in order to receive a more desirable outcome. As a result, machine learning systems deployed in these settings need to take such strategic interactions into consideration in order to make reliable predictions (and decisions). To this end, I have investigated the effects of incomplete information, causal learning, and repeated interactions in the domain of algorithmic decision-making under incentives.
    Relevant Publications:
  3. Learning in non-stationary environments: While classical machine learning focuses on learning a single model for a single task using data from a stationary distribution, the real world is often non-stationary and requires reasoning about many different-but-related tasks, often with just a small amount of data from each. For example, in online advertising auctions, the advertiser’s value for different keywords adapts based on current marketing trends. In online marketplaces, the price consumers are willing to pay for different products varies based on the current economic outlook. My research in this area has been focused on developing meta-learning algorithms for decision-making with provable performance guarantees, which are capable of operating under partial feedback and in the presence of other strategic agents.
    Relevant Publications:

Feel free to reach out if you'd like to chat about research or graduate school admissions in computer science. The best way to reach me is at the email address listed above.