Nihar Shah Associate Professor CMU Scholars Page Office 8211 Gates and Hillman Centers Email nihars@cs.cmu.edu Phone (412) 268-7896 Department Machine Learning Department Computer Science Department Research Interests Artificial Intelligence Machine Learning Theory Advisees Alexander Goldberg Keerthana Gurushankar CSD Courses Taught 15281 - Spring, 2024 Research/Teaching Statement Nihar's research broadly lies in the areas of statistical learning, game theory, and information theory, with a focus on problems in crowdsourcing and learning from people. Nihar received his PhD from the EECS department at the University of California, Berkeley in 2017. His thesis received the 2017 David J. Sakrison Memorial Prize from EECS Berkeley for "truly outstanding and innovative research". He is also a recipient of the Microsoft Research PhD Fellowship 2014-16, the Berkeley Fellowship 2011-13, the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012, the SVC Aiya Medal from the Indian Institute of Science for the best master’s thesis, and an Outstanding Graduate Student Instructor award at UC Berkeley in 2016. Nihar's favorite sport is kite fighting. He is recently also excitedly playing lots of ultimate frisbee. Publications Journal Article The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms 2023 • Management science • 69(1):377-403 Kamble V, Shah N, Marn D, Parekh A, Ramchandran K Preprint Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented? 2022 • Proceedings of the AAAI Conference on Human Computation and Crowdsourcing • 10:207-218 Wang J, Baharav C, Shah NB, Williams Woolley A, Ravi R Conference Calibration with Privacy in Peer Review 2022 • IEEE International Symposium on Information Theory - Proceedings • 2022-June:1635-1640 Ding W, Kamath G, Wang W, Shah NB Journal Article Challenges, experiments, and computational solutions in peer review 2022 • Communications of the ACM • 65(6):76-87 Shah NB Conference No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise 2022 • IEEE International Symposium on Information Theory - Proceedings • 2022-June:3168-3173 Rastogi C, Stelmakh I, Shah N, Balakrishnan S
Journal Article The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms 2023 • Management science • 69(1):377-403 Kamble V, Shah N, Marn D, Parekh A, Ramchandran K
Preprint Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented? 2022 • Proceedings of the AAAI Conference on Human Computation and Crowdsourcing • 10:207-218 Wang J, Baharav C, Shah NB, Williams Woolley A, Ravi R
Conference Calibration with Privacy in Peer Review 2022 • IEEE International Symposium on Information Theory - Proceedings • 2022-June:1635-1640 Ding W, Kamath G, Wang W, Shah NB
Journal Article Challenges, experiments, and computational solutions in peer review 2022 • Communications of the ACM • 65(6):76-87 Shah NB
Conference No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise 2022 • IEEE International Symposium on Information Theory - Proceedings • 2022-June:3168-3173 Rastogi C, Stelmakh I, Shah N, Balakrishnan S
