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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 Meta Co-Training: Two Views are Better than One.
This is joint work with my PhD student Jay Rothenberger.
The paper has been accepted and will appear at the European Conference on Artificial Intelligence (ECAI), 2025.
🥇 Meta Co-Training is a semi-supervised learning method that achieves State-of-the-Art performance on ImageNet-10% and does very well on ImageNet-1% too.

Last year I had the following papers:

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