NISS Ai, Statistics & Data Science Webinar: Advances in Climate Analytics for Resilience and Risk Management

Tuesday, October 20, 2026 - 12:00pm to 1:30pm ET

Speaker

Steve Sain, Senior Director, Geospatial and Data Sciences, Jupiter Intelligence

Moderator

Whitney Huang, Associate Professor of Statistics, Clemson University
 
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Abstract

Title: Advances in climate analytics for resilience and risk management  

Climate risk analytics draws on climate science, statistical modeling, and financial/operational risk assessment to quantify how acute weather events and long-term climate change affect organizations, and to translate that understanding into actionable decisions. This talk walks through the analytics pipeline from hazard modeling and downscaling, through exposure and impact assessment, to adaptation evaluation and includes a focus on where applied statistics and machine learning drive methodological advances at each stage. I'll highlight specific applications in tropical cyclone risk, extreme heat, and wildfire, and where AI-enabled climate products are beginning to play a key role. 

About the Speaker

Dr. Steve Sain is a Senior Principal Data Scientist and Senior Director at Jupiter Intelligence, where he heads the data sciences team, directs R&D projects leveraging applied statistics, generative AI and machine learning, and helps shape Jupiter’s overarching AI strategy. Jupiter provides critical data and analytics to predict and manage risks from severe weather and sea level rise, storm intensification and changing temperatures. As an applied statistician and data science leader, Steve specializes in the intersection of climate research, spatial methods for large datasets, extremes, uncertainty quantification, and climate risk analytics. Previously, Steve was a scientist and head of the Geophysical Statistics Project at the National Center for Atmospheric Research (NCAR) from 2006 to 2014. He is a Fellow of the American Statistical Association (ASA) and a past recipient of the Distinguished Achievement Award from the ASA’s Section on Statistics and the Environment. Steve serves on the steering committee for the ASA’s Caucus of Industry Representatives, the board of the National Institute of Statistical Sciences (NISS), and the advisory board for the Institute for Mathematical and Statistical Innovation (IMSI) at the University of Chicago. He is an affiliate faculty member at the University of Colorado and a member of the National Academies Roundtable on AI and Climate Change, where he chaired the organizing committee for a recent workshop on accelerating climate progress with AI. See Profile

About the Moderator

Dr. Whitney Huang is an Associate Professor of Statistics at Clemson University, where he has served since August 2019. Prior to joining Clemson, he was a Canadian Statistical Sciences Institute (CANSSI) and Statistical and Applied Mathematical Sciences Institute (SAMSI) postdoctoral fellow at the University of Victoria (UVic), affiliated with the Pacific Climate Impacts Consortium and the School of Earth and Ocean Sciences, working with Dr. Francis Zwiers and Prof. Adam Monahan. Before his time at UVic, he held a SAMSI/University of North Carolina postdoctoral position under the supervision of Prof. Richard Smith. He received his Ph.D. in Statistics from Purdue University in August 2017, advised by Prof. Hao Zhang. During his doctoral studies, he was actively involved in the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences (STATMOS) and the Center for Robust Decision Making on Climate and Energy Policy (RDCEP), collaborating with Michael Stein and Elisabeth Moyer at the University of Chicago and Doug Nychka at the National Center for Atmospheric Research. Before pursuing his doctorate at Purdue, he earned a Master’s degree in Statistics from the University of Akron and a Bachelor’s degree in Mechanical Engineering from National Cheng Kung University in Taiwan. His research interests include statistics of extremes, spatio-temporal statistics, surrogate modeling for computer experiments, time-frequency analysis, multiscale statistical modeling, spatial point processes, environmental applications, and high-frequency physiological data analysis. See Profile

About AI, StAtIstics and Data Science in Practice 

The NISS AI, Statistics and Data Science in Practice is a monthly event series will bring together leading experts from industry and academia to discuss the latest advances and practical applications in AI, data science, and statistics. Each session will feature a keynote presentation on cutting-edge topics, where attendees can engage with speakers on the challenges and opportunities in applying these technologies in real-world scenarios. This series is intended for professionals, researchers, and students interested in the intersection of AI, data science, and statistics, offering insights into how these fields are shaping various industries. The series is designed to provide participants with exposure to and understanding of how modern data analytic methods are being applied in real-world scenarios across various industries, offering both theoretical insights, practical examples, and discussion of issues.

Featured Topics:

Event Type

  • NISS Hosted

Cost

Free Webinar

Location

Free Zoom Webinar
United States

Policy