About

Kunal Pattanayak is a PhD candidate at Cornell University, specializing in the intersection of machine learning, artificial intelligence, and information economics. With a GPA of 4.01/4.0, Kunal has excelled academically and has been recognized for his research contributions. He is a recipient of the Cornell PhD McMullen Fellowship and has been a speaker at the 2020 Sloan-NOMIS Conference on Attention and Applied Economics. Kunal's research interests lie in the intersection of inverse reinforcement learning, deep learning, and behavioral economics. He has published several papers in top-tier conferences and journals, including the Journal of Machine Learning Research (JMLR) and the IEEE International Conference on Information Fusion (FUSION). His research has also been submitted to arXiv, a leading repository of electronic preprints in the fields of mathematics, physics, and computer science. Apart from his research, Kunal has served as a graduate teaching assistant at Cornell University, where he taught signals and systems. He has also worked as a teaching assistant at the Indian Institute of Technology, Kharagpur, where he taught digital communication and analog communication laboratories. Kunal has a strong academic background, having completed his integrated bachelor's and master's degree in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, and is currently pursuing his PhD in statistical signal processing from Cornell University. He has also completed several graduate-level courses in probability theory, statistical learning theory, Bayesian estimation, and stochastic optimization, among others. Kunal's technical skills include AI, machine learning, reinforcement learning, deep learning, search, and mobile development. With 3.04 years of relevant experience, Kunal is a highly skilled researcher and a promising young professional in the field of machine learning and AI.