I am an assistant professor at CMU in the Machine Learning and the Computer Science departments. I work in the areas of machine learning, game theory and crowdsourcing, with a focus on learning from people with objectives of fairness, accuracy, and robustness. My current work addresses various systemic challenges in peer review via principled and practical approaches.

Tutorial on problems in peer review

Blog on various aspects of academia, research, and peer review

Google Scholar page

nihars [at] cs.cmu.edu
Office: GHC 8211


My research interests lie in the areas of statistics, machine learning, information theory and game theory, with a focus on "learning from people": How to elicit high-quality data from people? How to draw inferences from such data? I am particularly interested in dealing with issues of fairness, accuracy, and robustness. This is an exciting and challenging area of research that has many important applications including hiring, admissions, crowdsourcing, A/B testing, online ratings and recommendations, peer grading, and peer review. My research aims to address these important challenges at scale, in a principled and pragmatic manner. I am presently particularly excited about developing principled approaches towards improving the backbone of all scientific research: Peer Review! Here are some vignettes from my recent research:

     Peer review (2018-). NSF CAREER Award, AAMAS 2019 Best Student Paper Award and Best Paper Nomination, Google Research Scholar Award 2021.
     Crowdsourcing (2013-17). Awarded the David J. Sakrison Memorial Prize at UC Berkeley.
     Distributed storage (2009-13). Awarded the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011 & 12.

I really like this perspective: https://www.youtube.com/watch?v=H8eP99neOVs (Steve jobs) "People think focus means saying yes to the thing you've got to focus on. But that's not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully."

Google Scholar page


Jingyan Wang
Robotics Institute, CMU

Ivan Stelmakh
Machine Learning Department, CMU
(advised jointly with Aarti Singh)

Charvi Rastogi
Machine Learning Department, CMU
(advised jointly with Ken Holstein)

Steven Jecmen
Computer Science Department, CMU
(advised jointly with Fei Fang)


Qiqi Xu
Machine Learning Department, CMU
(advised jointly with Hoda Heidari)

Wenxin Ding
Mathematics and Computer Science, CMU
(advised jointly with Weina Wang)


Komal Dhull
Computer Science, CMU

Ryan Liu
Computer Science, CMU

Carmel Baharav
Computer Science, CMU

We gratefully acknowledge support from the National Science Foundation, CMU Block center, and a Google Research Scholar award.


Fall 2020 10-715 Advanced Introduction to Machine Learning
Spring 2020 15-780 Graduate Artificial Intelligence
Fall 2019 10-715 Advanced Introduction to Machine Learning
Spring 2019 15-780 Graduate Artificial Intelligence
Fall 2017 10-709 Fundamentals of Learning from the Crowd