Hamid Dashti

About Me

I am a computational and geospatial scientist working at the intersection of remote sensing, Geospatial Science, Earth system modeling, and data science. I am currently a Computational Scientist at the Research Computing Center, University of Chicago, where I collaborate with research groups on environmental science, GeoAI, and scalable geospatial computing. Previously, I held postdoctoral positions at the University of Wisconsin-Madison and the University of Arizona, and earned my Ph.D. at Boise State University.

My work spans from leaf-level photosynthesis to global carbon and food systems, always asking how ecosystems are connected with Earth and human systems. I have contributed to various NASA programs such as Surface Biology and Geology mission, PACE, Drylands and have secured and contributed to over $1M in funded research.

My current research at UChicaogo focuses on developing scalable methods in geosptial analyses, Earth system modeling, and application of AI in Earth and human ssytems. I am passionate about open science, reproducibility, and mentoring the next generation of scientists in environemental, computational and geospatial sciences.

Research

Vegetation, Disturbance & Ecosystem Recovery
I study how ecosystems respond to disturbance — fire, land cover change, climate variability — and what "recovery" means when ecosystems are non-stationary. My recent work introduced the concept of counterfactual recovery, asking not just whether an ecosystem recovered, but what it could have become in the absence of disturbance (GRL, 2024).

Earth System Modeling & Data Assimilation
I integrate Earth observations into process-based models (CESM/CLM, ED2, TECs, GCAM) to improve our understanding of the carbon cycle, surface energy balance, and land–climate feedbacks. I have worked extensively with data assimilation (DART) and model–data fusion across the Arctic–Boreal region and global drylands.

Remote Sensing & Vegetation Physiology
From hyperspectral leaf trait retrieval to global Solar-Induced Fluorescence (SIF) from OCO-2, I develop and apply remote sensing methods to track vegetation function at scale. I work with the full observational stack: ground-based spectrometers, TLS, UAVs, airborne campaigns, and spaceborne sensors including GEDI, PRISMA, and NEON AOP. In a recent study (Ecology Letters, 2025), I combined data from over 400 eddy covariance sites with satellite observations to shed light on how canopy structure regulates maximum light use efficiency — a fundamental, intrinsic measure of a plant's capacity to convert light into carbon. The work revealed that this relationship is nonlinear and varies systematically across global biomes.

Vegetation, Food Systems & Climate Policy
A growing thread of my work connects Earth system processes to human systems. I recently examined how climate-driven vegetation change restructures food production and what that means for climate mitigation policy (submitted to One Earth, 2026).

Skills

Earth System & Ecological Modeling

Remote Sensing & Environmental Sensing

Programming & Scalable Computing

AI & Machine Learning