Asensio is assistant professor in the Georgia Tech School of Public Policy. His research focuses at the intersection of data science and public policy, with applications to energy systems and behavior, smart cities, vehicle electrification and applied machine learning in transportation and mobility. Asensio conducts randomized field experiments and uses statistical and computational tools to identify the causal effects of policies and interventions at regional and global scales.

His research has been published in general interest journals such as Nature Energy and PNAS, as featured in NBC news, CBS radio, NPR, Yahoo! News, Scientific American, the Economic Times and the Washington Post. His work has been featured in policy advisory communications by the European Commision and the World Bank's World Development Report. He is a faculty affiliate at the Institute for Data Engineering & Science (IDEaS), the Machine Learning Center, and the Climate and Energy Policy Laboratory. He holds a doctorate in environmental science and engineering from UCLA, with dual training in engineering and economics. 

The School of Public Policy is a unit of the Ivan Allen College of Liberal Arts.