I'm Harry Bendekgey,

a second-year computer science Ph.D. student at UC Irvine advised by Erik Sudderth. My primary research area is statistical machine learning.
I am obsessed with board games, (recent favorites are Azul, Betrayal Legacy and Root) I'm learning to surf, (I am not good at it) and I'm raising a puppy named Claude (Zoom is hard even for the best of us).

Academic Interests

  • Stochastic Processes

    Stochastic Processes are a powerful tool for modeling real-world phenomena, from national elections to microscopic diffusion. For these models to be useful, we must combine domain-specific knowledge with the mathematical tools and tricks necessary for efficient inference.

  • Fair Machine Learning

    As real-world decision-making become increasingly automated, ethical questions about the fairness of our models are no longer thought experiments. Left unchecked, algorithms can exacerbate disparities between genders, racial groups, or other legally-protected categories of identity.

  • Computational Biology

    The complexity of biological systems has historically made both understanding them and simulating them difficult. Recent gains in computational power and the creation of large-scale data sets provide the potential for many new discoveries.


In Review: Scaling study of diffusion in dynamic crowded spaces.
H Bendekgey, G Huber, and D Yllanes. ArXiv Link
Talks and Presentations: March Meeting 2020
In Review: Scalable & Stable Surrogates for Flexible Classifiers with Fairness Constraints.
H Bendekgey, E Sudderth.
Talks and Presentations: Southern California Machine Learning Symposium 2021
Clustering Player Strategies from Variable-Length Game Logs in Dominion
H Bendekgey, AAAI Workshop on Knowledge Extraction from Games (KEG), 2019. ArXiv Link
Consistency and Reproducibility in U.S. House of Representatives Forecasts
H Bendekgey, arXiv preprint arXiv:1811.12466, 2018. ArXiv Link

Please feel free to reach out!