Name: Muskan Walia
Email: m.walia@nyu.edu
Program: Statistics and Computational Social Science
Research Interests: Artificial intelligence, machine learning, scientific computing methods, applied interventions, criminal justice, education, decision-making processes.
Principal Advisor(s): Alex Chohlas-Wood
Research description/bio: Muskan Walia is a PhD student in Statistics and Computational Social Science. Her research focuses on the development of artificial intelligence, machine learning, and scientific computing methods in collaboration with government agencies to tackle pressing social issues and improve institutional decision-making processes. In particular, Muskan designs automated systems that integrate statistical and computational methods — including benchmark data curation, uncertainty propagation, and LLMs-as-a-judge — to support the validation of generative AI in the public interest. She previously received an HBS in mathematics and philosophy from the University of Utah.
Presentations and Publications:
Walia, M. (2026, March). Race-Blind Charging: Using Generative AI to Promote Equality in High-Stakes Legal Contexts. International Biometric Society.
Walia, M. (2025, November). Can Generative AI Help Deliver Justice? A Case Study of Race-Blind Charging in California. Association for Public Policy Analysis and Management.
Walia, M. (2021 and 2022). What’s the Price of Mistakes? Balancing False Negatives and False Positives in COVID-19 Tests. ACCESS Research Symposium.
Walia, M. (2022, April). Mathematical Models of COVID-19 Tests: Tailoring Test Types to Public Health Objectives. University of Utah Research Symposium.
Walia, M. (2022, April). Mathematical Models of COVID-19 Tests: Tailoring Test Types to Public Health Objectives. Immunology, Inflammation, and Infectious Disease Initiative Symposium.
Walia, M. et al. (2022, April). Night Life: Connections between Lights, Median Income, Zip Codes, and Car Crashes. SQuARED.