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Nihar Shah is an Associate Professor at Carnegie Mellon University in the Computer Science Department. His research broadly encompasses areas such as statistical learning, game theory, and information theory, with a special focus on problems related to crowdsourcing and learning from people. Nihar completed his PhD in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2017, where his thesis received the 2017 David J. Sakrison Memorial Prize for truly outstanding innovative research. He was a recipient of the Microsoft Research PhD Fellowship from 2014 to 2016 and the Berkeley Fellowship from 2011 to 2013. His accolades include the IEEE Data Storage Paper Student Paper Awards in 2011 and 2012, and the SVC Aiya Medal from the Indian Institute of Science for the best master’s thesis. In addition to his academic achievements, he received the Outstanding Graduate Student Instructor award from UC Berkeley in 2016.
Carnegie Mellon University • Pittsburgh, PA
Teaching and conducting research in computer science and machine learning.
Admission is extremely competitive with no strict GPA cut-offs; holistic review is used.