I’m a Data Scientist with 8+ years of experience at the intersection of climate, energy, insurtech, and AI. I hold a PhD in Geophysics from Penn State, where I studied climate change impacts on Antarctic glaciers using remote sensing — work that shaped how I think about complex systems and messy, real-world data.

Since then, I’ve applied that scientific foundation to problems that matter: modeling flood risk for cities like NYC, optimizing Oil & Gas energy operations through ML, building neural weather models, and quantifying climate transition risk for insurers and energy companies. I’m most energized when I can take a tangled dataset and turn it into something a decision-maker can actually act on.

I work across the full ML lifecycle, from research and prototyping through cloud deployment and production and I’ve led multidisciplinary teams through that entire arc. I care as much about scientific rigor as I do about shipping something that creates real business value.

Recent highlights:

• Climate Transition Risk Assessment — Modeled emissions pathways, carbon price projections, and abatement cost trade-offs for 50 companies across 10 sectors and 9 regions through 2050, quantifying when investing in abatement technologies becomes more cost-effective than paying carbon taxes.
• Wildfire Risk Reduction — Led spatial modeling to optimize cloud seeding for lightning suppression across North America.
• Flood Hazard Modeling — Directed a team integrating socio-economic, infrastructure, and meteorological data to map high-risk zones in NYC.
• Energy Optimization — Built predictive and reinforcement learning systems for drilling and fracking operations, achieving >5% cost savings and emissions reduction.
• Geocoding Accuracy — Developed neural networks improving location accuracy by 10%+ across 100k+ addresses.