SPEAKERS

Artem Mishchenko works at the intersection of machine learning and scientific discovery, with interests spanning physics- and materials-related applications.

Bartosz Grzybowski works on chemistry and automated synthesis, including data-driven approaches aligned with self-driving laboratories.

Bingqing Cheng develops computational and AI-enabled approaches for chemistry and materials science.

Carlo Vittorio Cannistraci is a Professor of Biomedical Cybernetics whose research spans AI for biology, algorithmic network science, and unconventional computing inspired by neural topologies.

Curtis Berlinguette is a global leader in self-driving labs, AI-enabled chemical discovery, and accelerated materials research for energy technologies.

Eun-Ah Kim is a theoretical physicist whose work includes quantum many-body systems and related computational approaches.

Giacomo Indiveri is a Professor of Neuromorphic Cognitive Systems at the University of Zurich and ETH Zurich. His work spans neuromorphic mixed-signal circuits, brain-inspired computing, and real-world intelligent systems.

Haizhao Yang develops mathematical and machine-learning methods with applications in scientific modelling and computational physics.

Jacqueline Cole applies computation and machine learning to chemistry and materials, including structure–property modelling and discovery workflows.

Jonathan Schmidt develops AI-driven materials science methods and data-centric approaches for physical modelling.

Jun Jiang works on AI agents, intelligent chemistry automation, and self-driving laboratory systems for accelerated molecular discovery.

Karsten Reuter directs the Theory Department at the Fritz Haber Institute, developing multiscale and AI-accelerated models for catalysis and energy technology.

Liu Chong works in electrochemistry and materials-related research, including directions connected to accelerated discovery and automation.

Luis Camuñas-Mesa works on neuromorphic systems, unconventional computing, and event-driven hardware architectures.

Marco Bernardi develops computational and AI-driven methods for electronic transport, excitons, and quantum phenomena in advanced materials.

Milad Abolhasani develops autonomous chemical discovery platforms combining AI, robotics, and flow chemistry.

Ray Meng Gao works on AI methods and their applications across scientific problems, including chemistry, materials science, and physics.

Simon J. L. Billinge is a Professor of Materials Science, Applied Physics and Applied Mathematics at Columbia University and a physicist at Brookhaven National Laboratory. He develops advanced diffraction and data-science methods, including AI- and ML-driven analysis, to reveal local structure in complex materials.

Tapio Schneider leads the Climate Modeling Alliance (CliMA), building next-generation Earth system models integrating physics and machine learning.

Tommaso Dorigo is a senior scientist at INFN specialising in particle physics and machine learning, contributing to major projects including CMS at CERN.

Torsten Hoefler works on high-performance computing and scalable machine-learning systems that enable next-generation scientific computing.

Ulrich S. Schubert is a full professor of Organic and Macromolecular Chemistry at Friedrich Schiller University Jena, head of the Laboratory of Organic and Macromolecular Chemistry and director of the Center for Energy and Environmental Chemistry Jena. His research focuses on functional and supramolecular polymers, polymer-based energy storage, and digitally assisted, high-throughput materials discovery.

Vivek Natarajan is a Research Scientist at Google DeepMind leading research at the intersection of AI, science and medicine. He is the lead researcher behind Med-PaLM (Nature, 2023) and Med-PaLM 2 (Nature Medicine, 2025), the first AI systems to obtain passing and expert level scores on US Medical License exam questions, respectively. Vivek also co-leads Project AMIE, a research program aiming to build and democratize medical superintelligence. Over the past year, AMIE has shown promising potential in controlled settings, including primary care, specialty care, and complex diagnostic challenges, as both a standalone (Nature, 2025) and assistive tool for clinicians (Nature 2025). Finally, Vivek recently co-led the development of the AI co-scientist - a virtual AI collaborator designed to augment scientists, help uncover new original knowledge and accelerate the clock speed of scientific discoveries. Prior to Google, Vivek worked on multimodal assistant systems at Facebook AI Research. He is also part of the faculty for executive education at Harvard T.H. Chan School of Public Health in a part-time capacity.