Speaker

Weijie Su, UPenn

Moderator

Overview

Coming Soon!

About the Speaker

Weijie Su, UPenn - Coming Soon! See Profile

About the Moderator

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:

• Veridical Data Science - Speaker: Bin Yu, October 15,2024 
• Random Forests: Why they Work and Why that’s a Problem - Speaker: Lucas Mentch, November 19, 2024
• Causal AI in Business Practices - Speakers: Victor Lo, and Victor Chen, January 24, 2025
• Large Language Models: Transforming AI Architectures and Operational Paradigms - Speaker: Frank Wei, February 18, 2025
• Machine Learning for Airborne Biological Hazard Detection - Speaker: Jared Schuetter, March 11, 2025
• Trustworthy AI in Weather, Climate, and Coastal Oceanography - Speaker: Dr. Amy McGovern, May 13, 2025
• Sequential Causal Inference in Experimental or Observational Settings - Speaker: Aaditya Ramdas, August 26, 2025 
• POSTPONED: AI, Statistics & Data Science in Practice Webinar: Reinventing Operations Management’s Research and Practice with Data Science - Speaker: David Simchi-Levi (DATE TBD)
• Covariate Adjustment, Intro to Resampling, and Surprises - Speaker: Tim Hesterberg, October 3, 2025
• Bayesian Geospatial Approaches for Prediction of Opioid Overdose Deaths Utilizing the Real-Time Urine Drug Test - Speaker: Joanne Kim, November 18, 2025
• COVID-19 Focused Cost-benefit Analysis of Public Health Emergency Preparedness and Crisis Response Programs - Speaker: Nancy McMillan, December 11, 2025