Overview
Speaker
Tim Hesterberg, Staff Data Scientist at Instacart
Moderator
Abstract
About the Speaker
Dr. Tim Hesterberg is a Staff Data Scientist at Instacart, with previous experience as a Senior Data Scientist at Google. His career has spanned multiple domains, including positions in academia, the energy sector, and software development. He earned his Ph.D. in Statistics from Stanford University and his B.A. in Mathematics from St. Olaf College, and also spent two years studying in Germany. Dr. Hesterberg is co-author, with Laura Chihara of Carleton College, of Mathematical Statistics with Resampling and R (Wiley), a widely used text in statistics education. He has also contributed to instructional materials on teaching introductory statistics using resampling methods, in collaboration with colleagues including David Moore. His research and applied work cover a wide range of topics, including statistical methodology, electric demand forecasting, web traffic analysis, clinical trials, display advertising, computer vision, natural phenomena such as streams and earthquakes, and even modeling of biological and physical processes. He has served in leadership roles with the Canadian Statistical Sciences Institute and the National Institute of Statistical Sciences and was a contributing author to the American Statistical Association’s Guidelines for Undergraduate Statistics Programs.
About the Moderator
Moderator coming soon!
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
- Reinventing Operations Management’s Research and Practice with Data Science - Speaker: David Simchi-Levi, September 23, 2025
- ML and Bayesian geospatial approaches for prediction of opioid overdose deaths - Speaker: Soledad Fernández, November 18, 2025
Event Type
- NISS Hosted
Location
Policy
