Speaker

Mengdi Wang, Associate Professor, Department of Electrical and Computer Engineering and Center for Statistics and Machine Learning, Princeton University

Moderator

Overview

Coming Soon!

About the Speaker

Mengdi Wang is an Associate professor at the Department of Electrical and Computer Engineering and Center for Statistics and Machine Learning at Princeton University. She is also affiliated with the Department of Computer Science, Princeton’s ML Theory Group. She was a visiting research scientist at DeepMind, IAS and Simons Institute on Theoretical Computer Science. Her research focuses on machine learning, reinforcement learning, generative AI, AI for science and intelligence system applications . Mengdi received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013, where she was affiliated with the Laboratory for Information and Decision Systems and advised by Dimitri P. Bertsekas. Before that, she got her bachelor degree from the Department of Automation, Tsinghua University. Mengdi received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years), the Princeton SEAS Innovation Award in 2016, the NSF Career Award in 2017, the Google Faculty Award in 2017,  and the MIT Tech Review 35-Under-35 Innovation Award (China region) in 2018, WAIC YunFan Award 2022, AACC’s Donald Eckman Award 2024. She serves as a Program Chair for ICLR 2023 and Senior AC for Neurips, ICML, COLT, associate editor for Harvard Data Science Review, Operations Research. Research supported by NSF, AFOSR, NIH, ONR, Google, Microsoft C3.ai, FinUP, RVAC Medicines. Mengdi’s research group studies machine learning theory, reinforcement learning, generative artificial intelligence, AI for science and intelligence system applications. See Profile

About the 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 
• 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