About
I’m a data scientist with over 10 years of experience applying predictive modeling, machine learning, and geospatial analysis to problems where decisions have real-world, long-term consequences.
My work sits at the intersection of data science, domain expertise, and decision-making, with a focus on energy systems, infrastructure planning, and climate-related impacts.
How I Approach Problems
I’m most interested in problems where forecasting, uncertainty, and spatial context matter more than optimizing a single metric.
Across projects, I emphasize:
- understanding system behavior rather than just fitting models
- combining machine learning with simulation and scenario analysis
- making uncertainty explicit and decision-relevant
- building models that stakeholders can trust and act on
Areas of Focus
- Predictive modeling & forecasting for energy, transportation, and public systems
- Large Language Models for analysis, interpretation, and communication
- Simulation and scenario analysis to explore future pathways under uncertainty
- Geospatial analytics across large spatial and temporal scales
- Uncertainty-aware modeling for decision support
Experience & Impact
I’ve worked across energy, climate, population, and public-health domains, collaborating with researchers, engineers, and policy-focused teams.
My work has:
- supported infrastructure planning and electrification analysis
- contributed to national-scale forecasting efforts
- informed climate vulnerability and adaptation research
- been published in Nature Communications, with an additional paper accepted for publication in Proceedings of the National Academy of Sciences (PNAS).
What I Value in Collaboration
I enjoy working on teams that value:
- rigorous thinking and open discussion
- cross-disciplinary collaboration
- clear communication between technical and non-technical audiences
- models as tools for insight and decision-making, not ends in themselves