Harry Bendekgey

I am a Ph.D. candidate at UC Irvine advised by Erik Sudderth. In Fall 2025 I will be joining Tufts Computer Science as an Assistant Teaching Professor. I teach courses about machine learning, artificial intelligence, and the mathematical foundations of CS/ML/AI.

My research focuses on how we can introduce structure and constraints to deep generative models when we have prior knowledge but limited data. If you want to connect about research or pedagogy, feel free to contact me at firstname.last@gmail.com.

I love board games and am always excited to talk about them.

Publications

A Systematic Literature Review of Undergraduate Data Science Education Research
M Dogucu, S Demirci, H Bendekgey, FZ Ricci, and CM Medina,
Journal of Statistics and Data Science Education, 2025. Paper. ArXiv link.
Talks and Presentations: Electronic Conference on Teaching Statistics, 2024.

Scaling study of diffusion in dynamic crowded spaces.
H Bendekgey, G Huber, and D Yllanes,
Journal of Physics A: Mathematical and Theoretical, 2024. Paper. ArXiv link.
Talks and Presentations: American Physical Society March Meeting, 2022.

Unbiased Learning of Deep Generative Models with Structured Discrete Representations.
H Bendekgey, G Hope, and E Sudderth,
Conference on Neural Information Processing Systems, 2023. Paper. ArXiv link.
Talks and Presentations: Pomona College Computer Science Colloquium Series

Scalable & Stable Surrogates for Flexible Classifiers with Fairness Constraints.
H Bendekgey and E Sudderth,
Conference on Neural Information Processing Systems, 2021. Paper.
Talks and Presentations: Southern California Machine Learning Symposium, 2021