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As of July 1st 2026 I will be an Associate Professor with tenure 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 and semi-supervised learning algorithms providing theoretical complexity bounds (theorems), empirical bounds on performance, or computational hardness results. I have done so within the context of adversarial 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, open-world learning, 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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