Current Students:
Jacob Adamczyk is a PhD student in the Applied Physics department. His work focuses on developing new algorithms in reinforcement learning inspired by connections to non-equilibrium statistical mechanics.
Homepage: https://jacobha.github.io
Sho Inaba is a PhD student in the Computational Sciences program. He has worked on models for stochastic gene expression, with a focus on large deviations. He also developed a free energy based approach to unsupervised machine learning.
Previous Students:
Argenis Arriojas graduated in 2023 with a PhD from the Computational Sciences program. As part of the group, he initiated the the study of reinforcement learning algorithms within the framework of large deviation theory. His thesis, entitled “Analytical Framework for Entropy Regularized Reinforcement Learning Using Probabilistic Inference” can be found here.