Anima Anandkumar is a Bren Professor at Caltech. She previously was a Senior Director of AI Research at NVIDIA and Principal Scientist at Amazon Web Services. She received her B.Tech from the Indian Institute of Technology Madras, and her Ph.D. from Cornell University. She did her postdoctoral research at MIT and an assistant professorship at the University of California Irvine. She has received several honors such as the IEEE Fellowship, Alfred. P. Sloan Fellowship, NSF CAREER Award, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is also part of the World Economic Forum’s Expert Network.

Cristiano Malossi is a Principal Research Scientist and Manager of the Frontiers of Computing and Simulation group at the IBM Research Laboratory in Zurich. From 2025, Cristiano is leading research on AI for PDEs applied to Physics and Engineering. Previously, between 2020-2024, Cristiano has led IBM’s global research and innovation strategy around Enterprise Visual Inspection, with a focus on inspection of large-scale infrastructures. His team designs, develops, and productizes scalable AI cloud services for detection of small and rare defects in high-resolution data, bridging research with industrial deployment.

Rose Yu is an Associate Professor at UC San Diego in the Department of Computer Science and Engineering and an Amazon Scholar. She is a primary faculty member with the AI Group. Her research interests focus on machine learning for large-scale spatiotemporal data, and she is particularly excited about AI for scientific discovery. She has received the Presidential Early Career Award for Scientists and Engineers (PECASE), the DARPA Young Faculty Award, the ECASE Award, the NSF CAREER Award, the Hellman Fellowship, and multiple faculty awards from Sony, JP Morgan, Meta, Google, Amazon, and Adobe. She was named one of MIT Technology Review’s Innovators Under 35 in AI.

Clécio R. Bom is Professor and Technology Researcher at the Brazilian Center for Physics Research (CBPF), where he leads the Laboratory for Artificial Intelligence Applied to Physics (LAB-IA). His current work is focused on pushing the frontiers of AI in physics applications, including the inverse modelling with Neural Posterior Estimation, Physics-Informed Neural Networks and Agentic AI for physics in multiple fields including Astrophysics and Geophysics.

Maximilian Herde is a doctoral researcher at the Computational and Applied Mathematics Laboratory (CAMLab) group, ETH Zürich, working under the supervision of Prof. Siddhartha Mishra. His research focuses on scientific machine learning, operator learning, and foundation models for partial differential equations. He is a coauthor of RIGNO and Poseidon, frameworks for robust and generalizable PDE operator learning. Maximilian received the ETH Medal in 2025 for his master’s thesis``On Foundation Models for Partial Differential Equations''.

Jingmin Sun is a postdoctoral fellow in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), working with Prof. Mauro Maggioni. She obtained her Ph.D. in Mathematical Science from Carnegie Mellon University (CMU), working with Prof. Hayden Schaeffer (UCLA), and a B.S. degree in Mathematical Science from Rensselaer Polytechnic Institute (RPI). Her research interests lie in Mathematical machine learning, differential equations, and optimization.

Dr. John R. Smith is IBM Fellow and Head of AI for Math and Science at IBM T. J. Watson Research Center. Dr. Smith received Ph.D. in Electrical Engineering, Columbia University, 1997, where he was awarded the Eliahu I. Jury prize for outstanding thesis in signal processing. Dr. Smith has led research at IBM in diverse areas spanning computer vision, speech, language, multimedia, and scientific discovery in domains such as chemistry and materials, healthcare and life sciences, and climate and sustainability. 

Dr. Pablo J. Blanco received his PhD in Computational Modeling from the LNCC (2008, Brazil). He is a Senior Permanent Researcher at the National Laboratory for Scientific Computing (LNCC/MCTI, Brazil) and Head of the HeMoLab (Hemodynamics Modeling Laboratory) group at the LNCC. Dr. Blanco was an Affiliated Member of the Brazilian Academy of Sciences (2014-2018), and is currently a member of the World Council of Biomechanics and co-PI of the National Institute of Science and Technology in Medicine Assisted by Scientific Computing (INCT-MACC). 

Lucas Nissenbaum is a Project Scientist at IMPA’s Center for Projects and Innovation and the institution’s Manager of Technological Projects. He holds a Master’s and PhD in Electrical Engineering and Computer Science from MIT. His work focuses on developing academic–industrial collaborations in applied mathematics, with a particular emphasis on data science and machine learning. Currently, he leads a collaboration with Petrobras that aims to use physics-informed neural networks to solve partial differential equations, with a specific focus on seismic inversion applied to geophysical data.

Prof. Alvaro L. G. A. Coutinho is a CNPq 1A researcher and FAPERJ Scientist of Our State. He holds bachelor's, master's, and doctoral degrees in Civil Engineering from UFRJ/COPPE and was a visiting professor at the Oden Institute, University of Texas at Austin (2004). A full professor since 2001, he has served as COPPE’s financial director and deputy director of technology and innovation, and currently coordinates its Interdisciplinary Area of Computational Engineering and Science and directs the Advanced Center for High-Performance Computing. He sits on editorial boards of leading journals, consults for the COPPETEC Foundation, and is a founding member of the Brazilian Association of Computational Methods in Engineering. He has received major distinctions including the IBM Faculty Award (2001), COPPE Academic Merit Award (2007), Fellowship of the International Association of Computational Mechanics (2012), and the InRio Personalities of the Year Award (2015).

More than fifteen years of experience in applied research, innovation, and leadership of multidisciplinary teams at the intersection of Artificial Intelligence, Applied Mathematics, and Industry. Served as a Senior Research Scientist and manager at IBM Research Brazil, leading groups focused on AI-assisted decision making, risk modeling, materials discovery, and visual analytics, with direct impact across sectors such as energy, finance, and chemistry. Led strategic global projects involving seismic analysis, reservoir modeling, and the integration of scientific knowledge into industrial decision-making processes. 

Renato Cerqueira is the Director of the Behring Institute for Artificial Intelligence at PUC-Rio, whose mission is to align research and the advancement of AI technologies with topics of great societal impact. Before taking on this role, he was a Senior Research Manager at IBM Research Brazil, where he led the Knowledge-centric Systems group. Renato and his team explored the application of these research efforts in areas such as Materials Design, Geosciences, and Finance.

Eduardo Soares is a Senior Research Scientist specializing in the design and pre-training of large multimodal foundation models for scientific applications. His research focuses on integrating data-driven learning to model complex dynamical behaviors across scientific domains. His broader interests include representation learning, scientific simulation, and large-scale AI architectures for advancing scientific discovery.

Daniel Yukimura is a Project Scientist at the Center for Projects and Innovation at IMPA (Brazil), where he develops academic–industrial collaborations that apply advanced mathematics and AI to real-world challenges. His research focuses on simulation methods and machine learning for scientific and engineering problems, with contributions spanning particle filtering techniques and the theoretical analysis of neural networks. 

Nara Bobko is the Academic Manager at IMPA Tech, the undergraduate program of IMPA (Brazil), where she also serves as a professor and coordinator of the Bachelor’s in Mathematics of Technology and Innovation. Her research interests focus on mathematical modeling applied to biological phenomena, particularly population dynamics and infectious disease modeling.

Arthur Bizzi is a postdoctoral Research Associate at the École Polytechnique Fédérale de Lausanne (EPFL), at the Intelligent Maintenance and Operations Systems (IMOS) laboratory. His research interests lie in the application of numerical and analytical methods from the theory of dynamical systems to the development of Physics-Informed architectures for scientific machine learning.

Siddhartha Mishra is a chair Professor of Applied Mathematics at ETH Zurich, where he heads the Computational and Applied Mathematics Laboratory (CAMLab) and the Seminar for Applied Mathematics. He is also the Director of Computational Science Zurich, a core faculty member of the ETH AI Center and a steering committee member of the Swiss National AI institute. His research interests lie in the fields of numerical analysis, scientific computing and machine learning/AI with applications to different fields of science and engineering including fluid dynamics, astrophysics, climate science, geophysics and biology. 

Elisa Serioli is the Head of CFD Methodology at Dallara, where she leads the development and implementation of advanced Computational Fluid Dynamics techniques. Her work focuses on optimizing aerodynamic performance and ensuring the high-fidelity application of CFD tools in cutting-edge motorsport and engineering projects. She is a recognized leader in leveraging simulation to drive innovation and performance gains within the automotive industry.

Ana Paula Muller has worked in the Geophysical Technology Department of Petrobras Exploration since 2014, developing algorithms for migration velocity analysis and FWI, fundamental tools that help ensure a high-quality seismic image and reduce uncertainty. Her research integrates deep learning methods into the velocity model-building flow, developing artificial intelligence technologies for Petrobras's seismic processing algorithms.