Lead: Kristofer Reyes
Our lab works at the interface of computation, mathematics, statistics, and materials science. We are specifically interested in meaningful machine learning in the small-data regime that dominates much of materials science. In this regime, we must be:
We work on Bayesian models, decision-making under uncertainty, reinforcement learning, and methods to fuse data with physics-based knowledge and expert opinion. We do not view materials science as a black-box source of yet-another-dataset. Instead, we understand that the field offers a rich problem setting with nuance, constraints, and structure. Therefore, we develop methods that appreciate this nuance, respect constraints, and utilize such structure.
Contact
134 Bell Hall Department of Materials Design and Innovation University at Buffalo Buffalo, NY 14260
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