Learn about the speakers who will take part in the November 11, 2026 Executive Briefing on the AI-Energy-Compute Nexus. Speakers are listed in order of appearance.
President, Northwestern University
As president of Purdue University, Mung Chiang grew sponsored research past $1 billion a year and helped bring a $3.9 billion AI memory-chip investment to the university. He served as Science and Technology Adviser to the U.S. Secretary of State, the first engineer in that role, and helped shape and implement the CHIPS and Science Act.
Kellogg Chair of Technology
Professor, Kellogg School of Management, McCormick School of Engineering
Founding Director, Northwestern Innovation Institute
Founding Co-Director, The Ryan Institute on Complexity
Founding Director, Center for Science of Science and Innovation
Northwestern University
Dashun Wang is the Kellogg chair of technology and a professor at the Kellogg School of Management and the McCormick School of Engineering at Northwestern University. At Kellogg, he is the founding director of the Northwestern Innovation Institute, founding co-director of the Ryan Institute on Complexity, and the founding director of the Center for Science of Science and Innovation (CSSI). He is also a core faculty member at the Northwestern Institute on Complex Systems (NICO).
Frank C. Engelhart Professor of Materials Science & Engineering
Chair of the AI4Energy Platform
Northwestern University
Chris Wolverton’s research group focuses on computational materials science, using first-principles quantum mechanical simulations and machine learning to design and discover new materials virtually before experiments. These tools enable materials informatics approaches to explore large datasets and predict new compounds, similar to the recommendation systems used by Netflix or Amazon. Wolverton leads the AI4Energy initiative, a cross-cutting research and talent-cultivation program of the Trienens Institute for Sustainability and Energy and the International Institute for Nanotechnology (IIN), partnered with the Northwestern Network for Collaborative Intelligence (NNCI).
President, Bioengineering
Head of Biohub, Chicago
Chan Zuckerberg Biohub, Chicago
Neena B. Schwartz Professor of Chemistry and Biomedical Engineering
Northwestern University
Chief Scientist and Innovation Officer
UL Solutions
Robert Slone guides UL Solutions in advancing scientific innovation and technical expertise across sustainability, safety, and security. Recognized as a Corporate Fellow in the William Henry Merrill Society, Slone holds UL Solutions' highest technical honor, reflecting his significant contributions to our mission to work for a safer world.
AI for Good Program Manager
Meta
At Meta, Madeline Hinkamp works at the intersection of frontier AI, public policy, and social impact. She helps policymakers, researchers, nonprofits, and community organizations understand how open-source AI works and where it can create value, with a special focus on health, emergency response, conservation, accessibility, and other public interest domains. Previously, Hinkamp spent over a decade in state and local government consulting at Accenture and in healthcare technology at early-stage startups. Hinkamp holds an MBA and a Master of Public Policy (MPP) from the University of Chicago and a BA from Northwestern University.
Senior Research Engineer, FAIR Chemistry team
Meta
Muhammed Shuaibi is a senior research engineer on the FAIR Chemistry team at Meta. His work focuses on deep learning models and datasets for atomic simulation across molecules, materials, and catalysts—from building the data pipelines that underpin the team's models to applying them in experimental discovery workflows. Muhammed received his PhD in chemical Engineering from Carnegie Mellon University, where he co-developed the original Open Catalyst 2020 (OC20) dataset with FAIR.
Senior Strategic Alliance Manager
NVIDIA
Steven Torrisi is a physicist at NVIDIA, working on AI for computational materials science and chemistry. He works on scientific software development, clean energy applications, and interpretable machine learning to accelerate the discovery of critically needed energy-related materials.
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