Neeti Pokhriyal is an information scientist at RAND with expertise developing responsible artificial intelligence technologies for societal wellbeing and national security. Her research background is in building interpretable machine learning models that model scenarios where data come from novel, heterogeneous sources, often in limited training data settings, and that involve quantifying model uncertainties. She also works in AI ethics, exploring the importance of quantifying biases in digital data. She has developed computational methods for efficient mapping of poverty, forecasting energy deficits, estimating economic well-being, and building secure biometric systems.
Prior to RAND, she was at the National Science Foundation's (NSF) as an AAAS Science and Technology Policy fellow working on the National Artificial Intelligence Research Institutes program. She was part of the core team of NSF's ExpandAI program. She was the 2022 Mirzayan Policy Fellow at the National Academies of Sciences, Engineering, and Medicine, where she contributed to the work of the Committee on National Statistics. She is a member of the Association for Computing Machinery's (ACM) U.S. Technology Policy Committee and contributes to their policy pieces. She was given numerous talks on AI for poverty analysis and was invited for Expert Group Meeting on Poverty Eradication by the UN in 2023. She was the finalist for the Falling Walls Science Breakthroughs of the Year 2023, Engineering and Technology category.
She finished her postdoc from Dartmouth, and her Ph.D. from the State University of New York at Buffalo both in the Computer Science Department. Her doctoral work was awarded the Chih Foundation Research Award. She was awarded the Doctoral Consortium Scholarship for AAAS Conference on Artificial Intelligence in 2019 and was the winner of the Data for Development Challenge at the International Conference on the Analysis of Mobile Phone Datasets in 2015. Before her Ph.D., she was a researcher at Oak Ridge National Laboratory. She obtained her M.S. in Computer Science from the University of California, Riverside, where she received the Dean's Distinguished Fellowship.