I am an assistant professor at CMU in the Machine Learning and the Computer Science departments. I work in the areas of machine learning, statistics, information theory and game theory. My current work addresses various biases and other challenges in human evaluations via principled and practical approaches. A focus application is scientific peer review, where our work has already made significant impact.

Survey on Systemic Challenges and Solutions on Bias and Unfairness in Peer Review, and associated tutorial slides

Google Scholar page

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


Many applications invovle distributed human evaluations: a set of items needs to be evaluated by a set of people, but each item is evaluated only by a small subset of people and each person evalutes only a subset of items. Such applications include scientific peer review, hiring and promotions, admissions, crowdsourcing, healthcare, judicial decisions, and online ratings and recommendations. These evaluation methods many challenges such as subjectivity, miscalibration, biases, dishonesty, privacy etc. My research addresses these important challenges at scale, in a principled and pragmatic manner. Our work encompasses establishing fundamental limits, designing algorithms, deriving theoretical guaranees for them, evaluations, and actual deployment and impact. As an important application domain for impact, we have focused on the backbone of all scientific research: peer review. Our work is now deployed in a number of top venues, helping improve various parts of the review process. Our experiments in collaboration with top conferences have informed various policy choices and their implications. Here are some vignettes of our work:

I really like this perspective: "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." - Steve Jobs (video)

Google Scholar page


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

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

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

Alexander Goldberg
Computer Science Department
(advised jointly with Giulia Fanti)


Ryan Liu
Computer Science

Carmel Baharav
Computer Science


Jingyan Wang
PhD, Robotics Institute

Komal Dhull
BS in Computer Science

Wenxin Ding
MS in Computer Science
BS in Mathematics and Computer Science
(advised jointly with Weina Wang)

Qiqi Xu
MS in Machine Learning
(advised jointly with Hoda Heidari)

We gratefully acknowledge support from the National Science Foundation, CMU Block center, ONR, a Google Research Scholar award, and an NSF-Amazon Fair AI research grant.


Spring 2022 15-780 Graduate Artificial Intelligence
Fall 2021 10-715 Advanced Introduction to Machine Learning
Spring 2021 15-780 Graduate Artificial Intelligence
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

In my spare time, I am also creating introductory machine learning short lectures in Hindi, accessible to anyone without requiring any math or programming knowledge: Link to Youtube videos


SERVICE (outside CMU)