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As of July 1st 2026 I am 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 work is a book chapter titled Fundamentals for Binary Classification under Label Noise contributed to a book that celebrates the 35 years of the International Symposium on Artificial Intelligence and Mathematics (ISAIM) series. The book is edited by the founders of the ISAIM series, Marty Golumbic and Fred Hoffman. The book chapter has been submitted and the book will be launched by Springer at the American Mathematical Society (AMS) Joint Mathematics Meetings (JMM) 2027 that will take place in Chicago in January of 2027. I plan to be there and I will be happy to discuss more about it with you there.

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 Conference on 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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