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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 On Imbalanced Regression with Hoeffding Trees.
This is joint work with my PhD student Pantia-Marina Alchirch.
The paper has been accepted for publication at the Pacific-Asia Knowledge Discovery and Data Mining 2026 special session on Data Science: Foundations and Applications (PAKDD/DSFA), 2026.

Last year I had the following papers:

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