Nihar B. Shah

Nihar B. Shah is an Associate Professor at Carnegie Mellon University with joint appointments in the Machine Learning and Computer Science departments within the School of Computer Science. His research focuses on developing theoretically grounded algorithms for evaluating scientific work, with applications in peer review, fairness, and human-AI collaboration. His work has impacted over 100,000 research papers and grant evaluations.

Education:

• Ph.D. in EECS, UC Berkeley
• M.E. in Telecommunications, Indian Institute of Science
• B.Tech. in Electronics, NIT Karnataka

Research Interests: Shah's group investigates the science of evaluation through machine learning, optimization, and large-scale experiments. Key areas include peer review systems, algorithmic fairness, LLM applications in science, and human-AI collaboration frameworks. Research addresses fundamental questions about research validity, funding allocation, and equitable assessment.

Publication Trends: Recent work focuses on improving peer review through randomized controlled trials, security against collusion, LLM-based review systems, and bias mitigation. Publications consistently appear in premier venues (NeurIPS, PLOS ONE, AAAI) with growing emphasis on real-world deployments.

Awards & Honors:

• Young Alumnus Medal (IISc 2024)
• NSF CAREER Award (2020-2025)
• Google Research Scholar Award (2021)
• Multiple best paper awards (HCOMP, ICLR)

Research Group & Funding: Leads a focused research team with NSF, Google, and JP Morgan support. Alumni hold positions in academia and industry. Current projects involve large-scale evaluations of scientific work and algorithmic fairness.