Vidisha specializes in the intersection of machine learning, economic modeling, and public policy. At PWBM, she focuses on the demographic components of the organization’s microsimulation framework, with specific expertise in immigration policy and natality forecasting. Her recent research examines the implications of AI adoption for work and the economic impacts of U.S. visa policy changes. To support this work, she has led the development of several foundational datasets that enable the estimation and analysis of complex immigration flows.
Vidisha holds an M.S. in Data Analytics and Public Policy from Carnegie Mellon University, an M.A. in Economics from the Delhi School of Economics, and a B.A. in Economics from the University of Delhi.
Her previous work centers on using data to inform proactive decision-making. As a Data Science for Social Good Fellow at the University of Washington, she developed a machine learning pipeline to predict residential heating energy demand, helping guide decarbonization efforts in Alaska. Her research experience includes two years as a Research Associate at the Indian Statistical Institute, where she evaluated the impacts of clean energy use on indoor air pollution in rural India. At Carnegie Mellon, she worked on several policy projects, including predictive modeling to optimize mental health outreach and forecasting legislative outcomes to support strategic advocacy.
