Nirmit Joshi is a PhD student at Northwestern University advised by Julia Gaudio and Aravindan Vijayaraghavan. He is also working with Nati Srebro at the Toyota Technological Institute at Chicago.
His research focuses on the mathematical foundations of machine learning and optimization, especially in deep learning theory, random graphs theory, and federated learning. His research aims to design efficient algorithms with provable performance guarantees for various learning and optimization problems.
- Summer Cluster: Deep Learning Theory, Summer 2022. Visiting Graduate Student.