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


My research focusses on two inter-related themes:

  1. Distributed human evaluations: Many applications invovle distributed human evaluations, where a collection 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, online ratings and recommendations, etc. The distributed nature of evaluations leads to a number of problems including fraud, subjectivity, miscalibration, biases, loss of privacy, inefficiencies etc.
  2. Research on research Our research also focuses on problems in scientific research itself and its evaluations. Problems in this domain have an outsized influence: they affect the awarding of billions of dollars of grants annually, harm careers of researchers due to the rich-getting-richer effects in academia, and can significantly hurt the public perception of science.
Our 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. Our algorithms are now used widely for the peer review of tens of thousands of papers in computer science and other fields, our experiments have designed policies of many peer-review venues in an evidence based fashion, and our work has also influenced applications beyond peer review.



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)

Keerthana Gurushankar
Computer Science Department


Ryan Liu
Computer Science


Janet Hsieh
Computer Science

Priyanshi Garg
Computer Science


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

Jingyan Wang
PhD, Robotics Institute

Carmel Baharav
BS in Computer Science

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, CMU CyLab, ONR, a Google Research Scholar award, a JP Morgan Faculty Research Award, and an NSF-Amazon Fair AI research grant.


Fall 2022 10-715 Advanced Introduction to Machine Learning
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)