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I am an Assistant Professor at the School of Computer Science at the University of Oklahoma. I am primarily interested in designing and analyzing machine learning algorithms with rigorous guarantees.

In particular, I have done work on the statistical and computational efficiency of supervised learning algorithms providing complexity or empirical bounds, or computational hardness results, within the context of adversarial supervised learning (different noise models, poisoning attacks, adversarial examples), randomized and local-search heuristic methods (evolvability), multiple-instance learning, and imbalanced data.

During the last couple of years my students and I are investigating semi-supervised learning, learning with streaming data, regularization methods, and related topics.

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Recent Work

My most recent paper is AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography,
The paper describes the work done within AI2ES and is written jointly with the following people: Amy McGovern, Imme Ebert-Uphoff, Elizabeth A. Barnes, Ann Bostrom, Mariana G. Cains, Phillip Davis, Julie L. Demuth, Andrew H. Fagg, Philippe Tissot, John K. Williams.
It appeared in the AI Magazine, Volume 45, Issue 1, 2024.

Another recent paper is ISAIM-2022: international symposium on artificial intelligence and mathematics,
This was written jointly with Martin Charles Golumbic and Frederick Hoffman with whom we organized the International Symposium on Artificial Intelligence and Mathematics (ISAIM) 2022. This is a foreword written due to the organization of ISAIM 2022.
The foreword appeared in the Annals of Mathematics and Artificial Intelligence, Volume 92, pages 1–4, (2024).

Last year I had the following paper:

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