Prateek Yadav
Continual Learning, Generalization, Sparsity, MoE, Parameter Efficiency, Graphs.
praty@cs.unc.edu
Hey! I am a PhD student at the MURGe-Lab at the University of North Carolina - Chapel Hill, where I work with Prof. Mohit Bansal. My research goal is to make deep learning models learn continually and generalize to multiple domains. I am interested in efficient methods that lead to generalization and exploiting sparsity, memory, and mixture-of-expert models for continual learning.
Previously, I have worked on a diverse set of topics – 1) Interpretability, 2) compositional reasoning in NLP, 3) deep learning methods for Graph and Hypergraph structured data and their application to NLP, 4) estimated and controlled for uncertanity in the learned representations from these methods, and 5) worked on Bayesian modeling of temporal data.
Over the past few years, I have been fortunate to work with Ming Tan, Qing Sun, Xiaopeng Li at Amazon AWS AI Labs, with Prof. Partha Talukdar at MALL-Lab at Indian Institute of Science (IISc) Bangalore, with Dr. Prateek Jain at Microsoft Research India and with Prof. Arun Rajkumar at Indian Institute of Technology, Madras. I also worked for a year with some amazing people at LinkedIn AI Bangalore. Before all this, I completed my undergraduate degree in pure mathematics in 2018 from IISc Bangalore where I was supervised by Prof. Partha Talukdar.
Check out my publication page to know more about my current research. I am also actively involved in improving the way science is taught to students, through simple experiments and conducting workshops via Notebook Drive.