My research focuses on understanding and addressing extreme heat vulnerability through
physiological heat stress modeling, climate data analysis, and development of accessible
decision-support tools. I work at the intersection of climate science, public health, and
adaptation policy to protect vulnerable populations from rising heat extremes.
India Energy & Climate Center (IECC), UC Berkeley
Standard heat indices treat everyone the same—someone sitting indoors and a construction
worker in direct sun get the same "feels like" temperature. The EHI-N* changes that. Built
on the JPL/NASA physiological model, it accounts for metabolic heat production (100–350 W/m²),
solar radiation, and the body's actual thermoregulatory limits—sweating capacity, vasodilation,
and core temperature thresholds.
The framework classifies conditions into five risk zones from Safe (<32°C)
through Extreme Danger (>42°C), where the body can no longer shed heat
fast enough to prevent organ damage. Applied across 700+ Indian districts, it reveals that
heavy outdoor labor pushes workers into danger zones at temperatures traditional indices
still call "moderate."
Key finding
At 350 W/m² (heavy work), danger threshold drops by ~8°C compared to resting conditions
Method
Solves coupled heat-balance equations across physiological regions (core, skin, environment)
Impact
Real-time district-level heat alerts now operational for Regions 4/5/6 across India
Future Heat Stress Projections (ML Ensemble)
India Energy & Climate Center (IECC), UC Berkeley
Climate models tell us the planet is warming—but what does that mean for the person
working a rice paddy in Bihar at 2 PM? This project bridges that gap using stacked
machine learning ensembles trained on CMIP6 climate model outputs to project
physiological heat stress decades into the future.
The approach targets the upper tail of the distribution—95th and 99.7th percentile
events—because it's the extremes that kill. By combining multiple ML architectures
(gradient boosting, neural nets, quantile regression) into a probabilistic ensemble,
the model captures uncertainty while identifying which districts face the steepest
increases in unworkable days per year.
Status
Active development — ensemble architecture validated, scaling to national coverage
Patent
HEAT RADAR — ML system for future heat projections (listing pending)
Indoor Thermal Resilience of Cooling Shelters
UC Berkeley
Not everyone can escape the heat outdoors. This study investigates how well
designated cooling shelters actually perform during extreme heat events—measuring
indoor thermal conditions, occupancy patterns, and the gap between intended
and actual cooling capacity. The goal is to inform building standards and
emergency preparedness for heat-vulnerable urban populations.
Under review — submitted for peer review