Organizers

Nikita Kazeev

Research Fellow in the National University of Singapore. In 2020 he got a double PhD degree in Computer Science from HSE University & in Physics from Sapienza University of Rome. Nikita has worked on a diverse range of subjects in the area of machine learning for science, from particle physics and materials to spacecraft evasive maneuvers; mentored students and interns; promoted science in lectures and articles for the general public. He reviewed for AI4Mat @ NeurIPS 2024 & 2023, Vienna 2024.

Eléonore Vissol-Gaudin

Research Fellow in the Department of Materials Science and Engineering at Nanyang Technological University and member of the Hip lab. She got her PhD from Durham University in the UK, working on the development of unconventional computing devices. Her current research focuses on data-driven modelling of dynamical systems and integrating machine learning into experimental workflows. She has worked on the organisation of the AI for Science and Nobel Turing Challenge Conference 2024, along with 14 domain-specific AI for Science workshops in Singapore. She has been associate editor and reviewer for the IEEE Nanotechnology Council flagship conference IEEE NANO 2024 and chaired sessions at IEEE NANO2024 and the Singapore MRS' ICMAT2023.

Mengyi Chen

PhD student in the Department of Mathematics, National University of Singapore. Her research interests focus on leveraging machine learning to explore the dynamics of complex systems. Mengyi has published in top machine learning venues such as the Conference on Neural Information Processing Systems (NeurIPS).

Isabelle Guyon

Director, Research Scientist, Google DeepMind. Chaired Professor of Artificial Intelligence (PR EX1) and INRIA researcher, University Paris-Saclay. Co-program chair of NeurIPS 2016 and co-general chair of NeurIPS 2017; then NeurIPS board member. AMIA and an ELLIS fellow. Action editor at JMLR, and CiML Springer series editor. BBVA award recipient (2020). Since 2003 machine learning challenge organizer as a means of directing research in domains including causality, computer vision, automatic machine learning, and high energy physics.

Bingjia Yang

Postdoctoral Researcher at Merck with a Ph.D. in Computational Chemistry from Princeton University. Her doctoral research focuses on developing quantum–accuracy neural networks models for molecular dynamics simulation. At Merck, she works on advancing AI applications in drug discovery. She served as a reviewer for AI4D3 at NeurIPS.

Andrey Ustyuzhanin

Director of AI/ML Research at Acronis, and an Adjunct Professor at Constructor University Bremen. Founder of the Omniscale Intelligence Initiative. His main motivation is to achieve a better understanding of how the universe and consciousness work together. His main area of expertise includes applying artificial intelligence techniques and methods to various scientific domains. With over 20 years of active research and an impressive h-index above 130, Andrey is driven to push the boundaries of computational science through innovative simulation techniques, the design of generative models, the application of optimization algorithms, and the use of interpretable machine learning processes to accelerate scientific discovery and promote human evolution. Andrey has extensive organizational experience, including 7 years of organizing the international summer school "Machine Learning for High Energy Physics", data challenges, ALEPH Workshop @ NIPS 2015, TrackML Competition @ NeurIPS 2018, and multiple local workshops on AI and Physics.