About

Naman Agarwal is a Research Scientist at Google Brain with a focus on Machine Learning and Optimization. With a PhD from Princeton University, Naman has gained expertise in developing fast optimization methods for machine learning tasks, particularly in deep learning. He has also worked on privacy issues and distributed optimization in machine learning and analysis of stochastic networks. Naman has an impressive five years of experience as a Research Scientist, with a strong background in software engineering and deep learning. His work at Google as a Research Internship focused on developing communication-efficient private algorithms for distributed mean estimation, which is an important primitive in Federated Learning. His research has been featured in the Google Research Blog. In his current role at Google, Naman is working on various projects related to Machine Learning and Optimization. He is also an expert in developing deep learning models for natural language processing and computer vision. Naman's educational background includes a PhD in Computer Science from Princeton University, a Master of Science in Computer Science from the University of Illinois, and a Bachelor of Technology in Computer Science from the Indian Institute of Technology, Bombay. He completed his high school education at Doon School Dehradun. Naman's technical skills include expertise in Research Science, Machine Learning, Software Engineering, and Deep Learning. With nearly six years of relevant experience, he is a valuable asset to any organization looking to develop cutting-edge machine learning models.