I am a computer scientist with an interest in policy issues related to developing responsible artificial intelligence and data science technologies. I am the 2022-23 AAAS Science and Technology Policy fellow at the National Science Foundation’s National Artificial Intelligence (AI) Research Institutes program, which focuses on long-term fundamental and use-inspired AI research towards issues of national importance. As a fellow, I focus on understanding the AI strategies and policies across the federal space and participating in multi-institution and inter-agency efforts in expanding AI infrastructure and access to underserved communities via NSF’s ExpandAI program.
I was the 2022 Mirzayan Policy Fellow at the National Academies of Sciences, Engineering, and Medicine, where I contributed to the Committee on National Statistics on aspects of data, privacy, and equity.
I completed my postdoc from the Department of Computer Science, at Dartmouth. As a postdoc, I was awarded a seed grant from the Irving Institute for Energy and Society at Dartmouth to develop computational methods for understanding energy accessibility in developing economies. I completed my Ph.D. in Computer Science from the State University of New York at Buffalo, where my doctoral work was awarded the Chih Foundation Research Award. Before my Ph.D., I was a researcher in the Computer Science and Mathematics Division, at Oak Ridge National Laboratory. I obtained my M.S. in Computer Science from the University of California, Riverside, where I received the Dean’s Distinguished Fellowship.
My research background is building interpretable machine learning models that model scenarios where data come from novel and heterogeneous sources, often in limited training data settings and involving quantifying model uncertainty. I have developed computational methods to map poverty and inequality (PNAS’17, IADB Working Paper’20), to forecast energy deficits given Sustainable Development Goals (EPJ-Data Science’22), to estimate economic well-being (ACM TDS’21), as well as to build secure biometric systems (IEEE TIFS’16, IEEE Access’20). My recent work (under review) explores quantifying biases in digital data regarding whose voices they represent and who they miss.
I have collaborated with the Overseas Development Institute, London, Datapop Alliance, and the Inter-American Development Bank, DC on using novel data and newer methods for human development.