I am Avrajit Ghosh. I am currently postdoc at Simons Institute for the Theory of Computing and UC Berkeley EECS, BAIR. I am affiliated with the Machine Learning Research Pod where I am fortunate to be advised by Peter Bartlett and Bin Yu

My PhD thesis is titled Implicit Regularization of Hyperparameters in Deep Learning: Beyond Convexity and Small Steps. Through four chapters, the thesis explores the practical role of 1) learning-rate, 2) momentum, 3) weight perturbation and  4) architecture in implicitly regularizing the hypothesis space in deep learning. I graduated from Computational Mathematics Science and Engineering department at Michigan State University where I was fortunate to be advised by Rongrong Wang and Saiprasad Ravishankar

My research aim is to bridge the gap between practical deep learning and theory by investigating the training dynamics across various deep learning tasks and to develop a theoritical framework to understand the dynamics. 

Contact email: ghoshavr@berkeley.edu