About

Dr. Omar Isaac Asensio is an Associate Professor in the Jimmy and Rosalynn Carter School of Public Policy at Georgia Tech and serves as the Director of the Data Science & Policy Lab. His research focuses on climate and electrification strategies at the intersection of technology, artificial intelligence, and sustainability. He employs large-scale data, field experiments, and human-in-the-loop AI systems to address innovation challenges in energy systems, transportation, and human mobility, with particular emphasis on resource conservation and environmental sustainability. His work has been published in leading journals such as Nature Energy, Nature Sustainability, and the Proceedings of the National Academy of Sciences, and has 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. Dr. Asensio's research has been featured in policy advisory communications by organizations including the U.S. National Academy of Sciences, the UK Government, the European Commission, the United Nations, the Brookings Institution, the World Bank, and the IndiaAI initiative. He contributed to policy guidance for COP26 and the Glasgow Climate Pact. He holds a doctorate in environmental science and engineering with specialties in economics from UCLA and has received multiple awards for his research, including the NSF CAREER Award and the ARCS Emerging Sustainability Scholar Award. His teaching interests include quantitative methods, research design, data science, machine learning, and impact evaluation, with a focus on experiential learning and professional education in data science for public policy.

Research topics

• Computer Science
• Engineering
• Data Mining
• Artificial Intelligence
• Business
• Physics
• Environmental economics
• Electrical engineering
• Transport engineering
• Data science
• Automotive engineering
• Reliability engineering
• Economics

Selected publications

• Replication Data for: "Why is EV Charging So Unreliable? The Effects of Competition on Supplementary Service Quality"

  Harvard Dataverse · 2026-04-29

  datasetOpen access1st authorCorresponding

  This repository contains the anonymized dataset and replication code associated with the paper titled: "Why is EV Charging So Unreliable? The Effects of Competition on Supplementary Service Quality." The data includes EV charging station performance data and measures of core service competition and charging competition for U.S. EV charging infrastructure from 2011-2024.

  PublisherDOI

• Promoting sustainable travel decisions through health and active lifestyle messaging

  Research Square · 2026-02-03

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Data technologies and analytics for policy and governance: a landscape review

  Data & Policy · 2025-01-01 · 2 citations

  reviewOpen access1st authorCorresponding

  Abstract Data for Policy ( dataforpolicy.org ), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.

  PublisherOA PDFDOI

• Enabling Informed Decisions on Pyrolysis: A Key to Turn the Tide on Plastics Recycling

  ACS Sustainable Chemistry & Engineering · 2025-06-02 · 9 citations

  reviewOpen access

  The rapid expansion of the plastic industry has led to significant environmental challenges, prompting the exploration of alternative recycling methods. While mechanical recycling has limitations, chemical recycling, particularly pyrolysis, presents a promising solution. However, it faces contention regarding its environmental impacts and economic feasibility. In this perspective, we analyze both supporting and opposing viewpoints of plastic pyrolysis, highlighting the need for transparent, comprehensive, and measurement-informed life cycle assessments (LCAs) of pyrolysis systems to inform decisions. We also present a case study of literature-reported greenhouse gas (GHG) emissions from pyrolysis-derived ultralow sulfur diesel (ULSD) in the United States, showing that depending on plant capacity and co-product allocation methods, emissions can range from 28% lower to 30% higher than fossil-derived ULSD. Similarly, when viewed as a waste management strategy, net GHG emissions from plastic pyrolysis can range from 220% lower to 60% higher than those from current U.S. plastic waste management practices, depending on system conditions. These findings underscore the variability of results and the need for currently missing, robust, and contextualized LCAs. Finally, we discuss regulatory and social challenges and opportunities for the wider adoption of chemical recycling, emphasizing the critical role of public support in realizing the potential of pyrolysis for a circular economy.

  PublisherDOI

• Charging Uncertainty: Real-Time Charging Data and Electric Vehicle Adoption

  National Bureau of Economic Research · 2025-01-01 · 4 citations

  reportOpen access1st authorCorresponding

  Charging infrastructure is critical to electric vehicle (EV) adoption, but for chargers to be most useful, EV drivers need to know in real time where they are and whether they are working and available. We investigate the availability of real-time data from DC fast chargers on six major US Interstates and model the impacts of expanding access to real-time data to all DC fast chargers near highways. On average, between March and August 2024, 32.9% of DC fast charging stations adjacent to those six Interstates provided their real-time status on PlugShare, a major charge-finding app, with gaps of up to 1, 308 miles without real-time data. Further, we survey potential car buyers and EV owners and find low credibility of currently-available real-time data. We incorporate this data into a two-sided model of consumer vehicle choice and charging station build-out adapted from Cole et al. (2023). If universal real-time data is accompanied by improved charger uptime and driver confidence in the accuracy of the real-time data, we predict that the EV share of new vehicle sales would grow by 8.0 percentage points in 2030, expanding the EV fleet by 13.2%, and reducing 2030 carbon emissions by 22.5 mmt, versus baseline projections for 2030.

  PublisherDOI

• Charging Uncertainty: Real-Time Charging Data and Electric Vehicle Adoption

  SSRN Electronic Journal · 2025-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Catalyzing sustainable development: insights from the international workshop on STI policies and innovation systems in Central America

  Frontiers in Research Metrics and Analytics · 2024-12-11 · 2 citations

  articleOpen access

  This article examines the landscape of Science, Technology, and Innovation policies in Central America, focusing on Nicaragua, Guatemala, Honduras, and El Salvador. These nations face significant challenges in leveraging STI for sustainable development, including financial constraints and limited resources. Additionally, Central America struggles with systemic issues such as corruption, violence, and high levels of emigration, further complicating efforts to advance STI. A workshop organized by Georgetown University's Science Technology and International Affairs program brought together scholars to discuss STI policies, resulting in key recommendations. The article highlights critical challenges, including over-reliance on state funding, stagnant researcher numbers, and the pressing need for research diversification. It emphasizes the importance of youth engagement, leadership, and resilience in shaping effective STI policies. Recommendations include investing in science education, establishing governmental scientific advisory bodies, promoting research diversity, and addressing climate change through STI strategies. The findings provide valuable insights for scholars, policymakers, and international organizations working with less developed nations globally.

  PublisherOA PDFDOI

• A Generative AI Approach to Pricing Mechanisms and Consumer Behavior in the Electric Vehicle Charging Market

  Proceedings of the AAAI Symposium Series · 2024-01-22 · 5 citations

  articleOpen accessSenior author

  The electrification of transportation is a growing strategy to reduce mobile source emissions and air pollution globally. To encourage adoption of electric vehicles, there is a need for reliable evidence about pricing in pub-lic charging stations that can serve a greater number of communities. However, user-entered pricing information by thousands of charge point operators (CPOs) has created ambiguity for large-scale aggregation, increasing both the cost of analysis for researchers and search costs for consumers. In this paper, we use large language models to address standing challenges with price discovery in distributed digital data. We show that generative AI models can effectively extract pricing mechanisms from unstructured text with high accuracy, and at substantially lower cost of three to four orders of magnitude lower than human curation (USD 0.006 pennies per observation). We exploit the few-shot learning capabilities of GPT-4 with human-in-the-loop feedback—beating prior classification performance benchmarks with fewer training data. The most common pricing models include free, energy-based (per kWh), and time-based (per unit time), with tiered pricing (variable pricing based on usage) being the most prevalent among paid stations. Behavioral insights from a US nationally representative sample of 13,008 stations suggest that EV users are commonly frustrated with the slower than expected charging rates and the total cost of charging. This study uncovers additional consumer barriers to charging services concerning the need for better price standardization.

  PublisherOA PDFDOI

• Replication Data for: Housing Policies and Energy Efficiency Spillovers in Low- and Moderate-Income Communities

  Harvard Dataverse · 2024-01-30 · 1 citations

  datasetOpen access1st authorCorresponding

  Human and machine readable replication dataset for "Housing Policies and Energy Efficiency Spillovers in Low- and Moderate-Income Communities" Omar I. Asensio, Olga Churkina, Becky Rafter, Kira E. O'Hare

  PublisherDOI

• Generative AI and Discovery of Preferences for Single-Use Plastics Regulations

  Proceedings of the AAAI Symposium Series · 2024-01-22 · 2 citations

  articleOpen accessSenior author

  Given the heightened global awareness and attention to the negative externalities of plastics use, many state and local governments are considering legislation that will limit single-use plastics for consumers and retailers under extended producer responsibility laws. Considering the growing momentum of these single-use plastics regulations globally, there is a need for reliable and cost-effective measures of the public response to this rulemaking for inference and prediction. Automated computational approaches such as generative AI could enable real-time discovery of consumer preferences for regulations but have yet to see broad adoption in this domain due to concerns about evaluation costs and reliability across large-scale social data. In this study, we leveraged the zero and few-shot learning capabilities of GPT-4 to classify public sentiment towards regulations with increasing complexity in expert prompting. With a zero-shot approach, we achieved a 92% F1 score (s.d. 1%) and 91% accuracy (s.d. 1%), which resulted in three orders of magnitude lower research evaluation cost at 0.138 pennies per observation. We then use this model to analyze 5,132 tweets related to the policy process of the California SB-54 bill, which mandates user fees and limits plastic packaging. The policy study reveals a 12.4% increase in opposing public sentiment immediately after the bill was enacted with no significant changes earlier in the policy process. These findings shed light on the dynamics of public engagement with lower cost models for research evaluation. We find that public opposition to single-use plastics regulations becomes evident in social data only when a bill is effectively enacted.

  PublisherOA PDFDOI

Recent grants

• CAREER: Behavioral Analytics and Field Experiments in Sustainable Innovation Policies

  NSF · $600k · 2020–2026

Frequent coauthors

• Magali A. Delmas

  University of California, Los Angeles

  15 shared

• Darshan M.A. Karwat

  Arizona State University

  12 shared

• S. J. Diem

  University of Wisconsin–Madison

  9 shared

• Elena M. Krieger

  Healthy Start

  9 shared

• Hussam Mahmoud

  Colorado State University

  9 shared

• Tamara J. Zelikova

  Carbon Solutions (United States)

  9 shared

• Clare C. Rittschof

  University of Kentucky

  9 shared

• Sooji Ha

  Georgia Institute of Technology

  7 shared

Labs

• Data Science and Policy LabPI

Awards & honors

• National Science Foundation CAREER Award
• ARCS Emerging Sustainability Scholar Award, Alliance for Res…
• APPAM 40-for-40 fellowship
• Research Impact on Practice Award, Academy of Management ONE…
• U.S. Department of Energy Jump into STEM Team Advisor Award…