Probability · Information theory · Machine learning

Abhijeet Mulgund

I am a mathematics PhD student at the University of Illinois Chicago. My research connects probability, geometry, and information theory with machine learning.

I study the structure of learned error-correcting codes, extremal Gaussian inequalities, such as in signal design and convex geometry, and learning from model outputs, such as weak-to-strong generalization. I am also interested in how AI can help produce mathematics that people can understand and build on. Before joining UIC, I worked as a software engineer. Before that, I studied computer science and mathematics at Rice University.