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, and specifically, use these tools to augment human evaluations. My current work addresses various systemic challenges in peer review via principled and practical approaches.

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

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

Google Scholar page

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




RESEARCH

My research interests lie in the areas of machine learning, statistics, 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? This is an exciting and challenging area of research that has many important applications including scientific peer review, hiring and promotions, admissions, crowdsourcing, healthcare, judicial decisions, and online ratings and recommendations. 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:


RESEARCH THEMES:
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)



PUBLICATIONS
Google Scholar page





GROUP
PHD STUDENTS


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
Robotics Institute

MASTERS STUDENTS


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

UNDERGRADUATE STUDENTS


Ryan Liu
Computer Science

Carmel Baharav
Computer Science

ALUMNI


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)


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



TEACHING

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



CURRICULUM VITAE
EDUCATION
PUBLICATIONS

HONORS

SERVICE (outside CMU)

INDUSTRY EXPERIENCE