Fall 2025 Lecture in Climate Data Science: HAMIDREZA NOROUZI

LEAP’s primary research strategy is to improve near-term climate projections by merging physical modeling with machine learning across a continuum from expertise in climate science and climate modeling to cutting-edge machine learning algorithms. The benefits will be significant for both the climate and data sciences communities. Climate scientists and modelers struggle to fully integrate the wealth of existing datasets into their models, while machine learning algorithms have been good at emulating and interpolating but have difficulties extrapolating or predicting extremes. By combining both approaches, LEAP will trigger a significant advancement for data science algorithms applied to physical problems. LEAP will incorporate physics and causal mechanisms into machine learning algorithms for better generalization and extrapolation, while optimally using the wealth of data available to climate science, in order to better predict the future.

LEAP Center

JOIN THE LEARNING THE EARTH WITH ARTIFICIAL INTELLIGENCE + PHYSICS (LEAP) CENTER AT COLUMBIA FOR A SEMINAR ON CLIMATE DATA SCIENCE.

Columbia Innovation Hub - Tang Family Hall

<h3><strong><strong>TITLE: "REMOTE SENSING OF LAKES, URBAN CLIMATE, AND HEAT RESILIENCE FROM LOCAL TO GLOBAL "</strong></strong></h3><h3><strong><strong>SPEAKER: HAMIDREZA NOROUZI (New York City College of Technology)</strong></strong></h3><p><br></p><h3>Date: December 11, 2025</h3><h3>Time: 12:00 p.m.</h3><h3>Format: Hybrid</h3><h3>Virtual: Zoom link provided upon registration</h3><h3>In-person: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 202, New York, NY 10027</h3><p><br></p><p>*Please note that in-person space is limited.*</p><p>__________________________________________________________________________________</p><p><strong><strong>Abstract</strong></strong>: This talk presents an integrated overview of our remote sensing and GeoAI research spanning lake ecosystems, urban heat, and subsurface transit environments. Using multi-sensor satellite data and in-situ measurements, we quantify trends in lake surface temperature, ice phenology, and harmful algal blooms across Adirondack and global lakes, producing high-resolution datasets that support long-term ecological monitoring.</p><p>In urban settings, we combine downscaled land surface temperatures, micro-meteorological observations, and machine-learning models to map neighborhood-scale heat exposure and develop Cool Routing pathways that reduce pedestrian thermal stress.</p><p>Findings from a comprehensive NYC subway climate study are additionally presented, drawing on platform sensors, infrared imaging, and forecasting models to estimate station-level temperature and heat index up to ten days in advance.</p><p>Together, these efforts demonstrate how remote sensing and AI-driven analytics can create actionable climate services that enhance environmental resilience, public health, and community decision-making.</p><p><strong><strong>Bio</strong></strong>: Hamid Norouzi is a Professor of Civil Engineering Technology and Director of Undergraduate Research at CUNY’s New York City College of Technology, where he also serves as Co-Director of the Center for Remote Sensing &amp; Earth System Sciences. His work centers on remote sensing, GeoAI, and climate resilience, with a focus on linking environmental observations to community-relevant applications. He is deeply engaged in mentoring the next generation of researchers and advancing geospatial approaches that improve understanding of natural and urban climate systems.</p><p><br></p><p>Learn More: <a href="https://leap.columbia.edu" title="https://leap.columbia.edu" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"><strong><strong>LEAP</strong></strong></a></p>

<p><strong><strong>HAMIDREZA NOROUZI</strong></strong></p><p>New York City College of Technology </p>

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Fall 2025 Lecture in Climate Data Science: HAMIDREZA NOROUZI

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Fall 2025 Lecture in Climate Data Science: HAMIDREZA NOROUZI

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