Omar Isaac Asensio
Associate Professor
- School of Public Policy
- Climate and Energy Policy Laboratory
Overview
Dr. Omar I. Asensio is an Associate Professor in the School of Public Policy and Director of the Data Science & Policy Lab at the Georgia Institute of Technology. He is a faculty affiliate at the Institute for Data Engineering & Science (IDEaS), the Machine Learning Center, and the Strategic Energy Institute. He is a Brook Byers Institute for Sustainable Systems (BBISS) Faculty Fellow. For the 2023-2024 academic year, Professor Asensio is on leave as a climate fellow at Harvard Business School.
Professor Asensio's research focuses on climate and electrification strategies at the intersection of technology, AI, and sustainability. His work uses statistical and computational tools to advance our understanding of how large-scale data and field experiments can be used to increase civic participation, while addressing pressing resource conservation and environmental sustainability challenges. Professor Asensio’s research has been published in leading journals such as Nature Energy, Nature Sustainability, and PNAS. His research and teaching have been supported by awards from the National Science Foundation, the U.S. Department of Energy, the U.S. State Department Diplomacy Lab, Microsoft and ESRI. Professor Asensio's research also has been featured in policy advisory communications by the U.S. National Academy of Sciences, the European Commission, NSF Public Affairs, the World Bank, and national governments—including the U.K., and the IndiaAI initiative. He contributed to the Zero Emissions Vehicles (ZEV) policy guidance for COP26 and the Glasgow Climate Pact.
Dr. Asensio is a member of the National Academies of Sciences, Engineering and Medicine (NASEM), New Voices 2021 cohort. He has received multiple awards for his research including the National Science Foundation CAREER award, the Alliance for Research on Corporate Sustainability (ARCS) Emerging Sustainability Scholar Award, the Association for Public Policy Analysis and Management (APPAM) 40-for-40 fellowship, and the NBS Research Impact on Practice award by the Academy of Management ONE Division. Professor Asensio serves as an Associate Editor of the Data and Policy journal, published by Cambridge University Press. He holds a doctorate in environmental science & engineering with specialties in economics from UCLA. Professor Asensio is a faculty participant in the Research University Alliance (RUA) Research Exchange and is engaged in multiple activities to increase the representation of women and under-represented students and professionals in STEM fields. For more information, visit https://datasciencepolicy.gatech.edu
Distinctions:
- National Science Foundation CAREER Award
- Member, National Academy of Sciences, New Voices 2021 cohort
- Associate Editor, Data and Policy Journal, Cambridge University Press
- ARCS Emerging Sustainability Scholar Award, Alliance for Research on Corporate Sustainability
- APPAM 40-for-40 Fellowship for early career contributions to public policy
- ONE-NBS Research Impact on Practice Award, Academy of Management ONE Division
Interests
- Economic Development and Smart Cities
- Energy Efficiency
- Energy Markets
- Energy, Climate and Environmental Policy
- Information Programs
- Information and Communications Technology Policy
- Innovation and Diffusion
- Market-based Incentives
- Science, Technology, and Innovation Policy
- Smart Grid
- Transportation
- Voluntary Programs
- Energy
- Sustainability
- Technology Management and Policy
- Transportation
Courses
- ECON-8803: Special Topics: Big Data and Policy
- PUBP-3042: Data Science for Policy
- PUBP-4803: Special Topics: Data Science for Public Policy
- PUBP-6112: Research Dsgn-Polcy Sci
- PUBP-8751: Big Data and Policy
- PUBP-8813: Special Topics
Publications
Selected Publications
Journal Articles
- Impacts of micromobility on car displacement with evidence from a natural experiment and geofencing policy
In: Nature Energy [Peer Reviewed]
Date: November 2022
- Widespread use of National Academies consensus reports by the American public
In: Proceedings of the National Academy of Sciences [Peer Reviewed]
Date: February 2022
- A Field Experiment on Workplace Norms and Electric Vehicle Charging Etiquette
In: Journal of Industrial Ecology [Peer Reviewed]
Date: 2021
- Electric Vehicle Charging Stations in the Workplace with High-Resolution Data from Casual and Habitual Users
In: Scientific Data [Peer Reviewed]
Date: 2021
- Topic Classification of Electric Vehicle Consumer Experiences with Transformer-based Deep Learning
In: Patterns - Cell Press [Peer Reviewed]
Date: 2021
- Real-time Data From Mobile Platforms To Evaluate Sustainable Transportation Infrastructure
In: Nature Sustainability [Peer Reviewed]
Date: 2020
- Using Machine Learning Techniques to Aid Environmental Policy Analysis: A Teaching Case Regarding Big Data and Electric Vehicle Charging Infrastructure
In: Case Studies in the Environment [Peer Reviewed]
Date: 2020
- Correcting Consumer Misperception
In: Nature Energy [Peer Reviewed]
Date: 2019
- The Effectiveness of US Energy Efficiency Building Labels
In: Nature Energy [Peer Reviewed]
Date: 2017
- The Dynamics of Behavior Change: Evidence from Energy Conservation
In: Journal of Economic Behavior and Organization [Peer Reviewed]
Date: 2016
- Nonprice Incentives and Energy Conservation
In: Proceedings of the National Academy of Sciences [Peer Reviewed]
Date: 2015
- Information Strategies and Energy Conservation Behavior: A Meta-Analysis of Field Experiments 1975-2012
In: Energy Policy [Peer Reviewed]
Date: 2013
All Publications
Journal Articles
- Impacts of micromobility on car displacement with evidence from a natural experiment and geofencing policy
In: Nature Energy [Peer Reviewed]
Date: November 2022
- Widespread use of National Academies consensus reports by the American public
In: Proceedings of the National Academy of Sciences [Peer Reviewed]
Date: February 2022
- A Field Experiment on Workplace Norms and Electric Vehicle Charging Etiquette
In: Journal of Industrial Ecology [Peer Reviewed]
Date: 2021
- Electric Vehicle Charging Stations in the Workplace with High-Resolution Data from Casual and Habitual Users
In: Scientific Data [Peer Reviewed]
Date: 2021
- Topic Classification of Electric Vehicle Consumer Experiences with Transformer-based Deep Learning
In: Patterns - Cell Press [Peer Reviewed]
Date: 2021
- Real-time Data From Mobile Platforms To Evaluate Sustainable Transportation Infrastructure
In: Nature Sustainability [Peer Reviewed]
Date: 2020
- Using Machine Learning Techniques to Aid Environmental Policy Analysis: A Teaching Case Regarding Big Data and Electric Vehicle Charging Infrastructure
In: Case Studies in the Environment [Peer Reviewed]
Date: 2020
- Correcting Consumer Misperception
In: Nature Energy [Peer Reviewed]
Date: 2019
- The Effectiveness of US Energy Efficiency Building Labels
In: Nature Energy [Peer Reviewed]
Date: 2017
- The Dynamics of Behavior Change: Evidence from Energy Conservation
In: Journal of Economic Behavior and Organization [Peer Reviewed]
Date: 2016
- Nonprice Incentives and Energy Conservation
In: Proceedings of the National Academy of Sciences [Peer Reviewed]
Date: 2015
- Information Strategies and Energy Conservation Behavior: A Meta-Analysis of Field Experiments 1975-2012
In: Energy Policy [Peer Reviewed]
Date: 2013
Conferences
- EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
In: CHI 2021 ACM Conference on Human Factors in Computing Systems [Peer Reviewed]
Date: 2021
- Detecting Behavioral Failures in Emerging Electric Vehicle Infrastructure Using Supervised Text Classification Algorithms
In: Transportation Research Board (TRB) annual conference
Date: 2020
- Extracting User Behavior at Electric Vehicle Charging Stations with Transformer Deep Learning Models
In: CARMA 2020, Third International Conference on Advanced Research Methods and Analytics, Valencia [Peer Reviewed]
Date: 2020
- Mobile Apps for Workplace Charging: A Big Data Field Experiment in Electric Vehicles
In: Academy of Management Global Proceedings [Peer Reviewed]
Date: 2018