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Nihar B. Shah is an Associate Professor in the Department of Computer Science at Carnegie Mellon University, specializing in Machine Learning. His research interests span several interconnected domains, including statistics, machine learning, information theory, and game theory. His recent work focuses on enhancing the peer-review process through the design of computational methods that provide mathematical guarantees and experimental evaluations, ultimately leading to significant impacts in real-world applications. In addition to his academic role, Dr. Shah is actively involved in the artificial intelligence community, serving as the program chair for the Association for the Advancement of Artificial Intelligence conference and as an acting editor for the Transactions on Machine Learning Research. He has contributed as a committee member for numerous conferences, establishing a reputation for his scholarly work. His contributions to the field have been recognized with several awards, including the JP Morgan Faculty Research Award, Google Research Scholar Award, and the NSF CAREER Award, as well as various paper awards.
Admission is extremely competitive with no strict GPA cut-offs; holistic review is used.