Matt Raymond

My research is at the intersection of Machine Learning (ML) theory and Computational (Bio)Chemistry applications. Currently, I’m interested in developing generative models for complex, low-data settings, such as nanochemistry. I am particularly interested in compositional learning, diffusion models, weakly-supervised learning, and graph neural networks. My work has applications in drug discovery, the prediction of macromolecular interactions, and exploration of biochemical reactions. Previous interests include multitask feature selection, surrogate modeling of plasma physics, detection of cross-feeding interactions in the human gut microbiome, the implicit bias of multiclass logistic regression, artificially-curious agents for space exploration, and the handling of catastrophic errors for real-time flight software.

I received my BSc in Computer Science (‘20, Magna Cum Laude) from Chapman University and my MSc in Computer Science and Engineering from the University of Michigan (‘22). I am current studying for my PhD in Electrical and Computer Engineering at the University of Michigan, specializing in Signal and Image Processing and Machine Learning.