I am an associate professor at Carnegie Mellon University, with joint appointments in the Machine Learning and Computer Science departments. My group's research is centered around the science of evaluation and the evaluation of science. We develop algorithms with strong theoretical guarantees, as well as conduct large-scale controlled experiments for evidence-based policy design and real-world deployments.

Our research uses human-AI collaboration to address fundamental questions about scientific work: Is the research correct? Is it high-quality? Is it fundable, or even authentic? These questions are critical because inaccurate or flawed scientific findings can disrupt subsequent research, resulting in significant setbacks to scientific advancement. Additionally, problems in research evaluation affect billions of dollars in funding decisions, have directly contributed to fatalities in areas such as biomedicine, and impose substantial stress on students whose careers depend on fair assessment. Moreover, ensuring the integrity and accuracy of published science is crucial for maintaining public trust. Our research has already been used in the evaluation of over 100,000 research papers and thousands of grant proposals, demonstrating substantial real-world impact. Beyond science, the challenges our research addresses extend naturally into other domains, and the algorithms we have developed are also deployed in diverse applications such as admissions decisions and competition judging.

I tend to keep my research group relatively small and work very closely with all of my students.

Survey on Challenges, Solutions, and Experiments in Peer Review, and associated tutorial slides

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

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