Events

Xavier Blase holds his PhD in theoretical Physics from the University of California at Berkeley,followed by a postdoctoral experience at EPFL, Switzerland. He is presently CNRS Research Director at Institut Néel, Grenoble, France. His research focuses on the electronic and optical properties of systems relevant to condensed matter physics, materials sciences and physical chemistry, with much emphasis on methodological developments in the field of ab initio modelling based on the first principles of quantum mechanics. He is the initiator of the FIESTA code (Bull-Fourier prize 2014) that implements the many-body GW and Bethe-Salpeter formalisms with Gaussian basis sets, implementing embedded techniques for the study of disordered organic systems in large environments. He received the 2008 CNRS silver medal.

Kazuhiro Yabana obtained his PhD from Kyoto University in 1987. After serving as a postdoctoral researcher at Kyoto University, he joined Niigata University as an assistant professor in 1988. He moved to University of Tsukuba in 1999, and has been a professor, Center for Computational Sciences, University of Tsukuba, from 2004 to 2025. He retired from his professorship in 2025 and is currently Professor Emeritus at the University of Tsukuba and Visiting Professor at Graz University of Technology in Austria.
During his doctoral studies, he worked in the field of theoretical nuclear physics. In the mid-1990s, he pioneered real-time first-principles calculation for electronic dynamics in nanostructures and solids, applying a method developed in nuclear theory to solid-state density functional Hamiltonian. He currently works on cutting-edge topics in light-matter interaction, such as extreme nonlinear and ultrafast optics. He leads the development of an open-source software SALMON (Scalable Ab initio Light-Matter simulator for Optics and Nanoscience).

Ai Shinobu obtained her PhD in physical chemistry from the Hebrew University of Jerusalem, Israel. She held postdoctoral positions at the University of Tokyo, the Tokyo Institute of Technology, and RIKEN, before joining the University of Osaka, where she is currently a specially appointed associate professor at the Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe). Her research focuses on large-scale molecular dynamics simulations of biological systems, with an emphasis on developing simulation protocols to explore molecular processes over long timescales. She is particularly interested in understanding how mutations and other perturbations influence molecular structure, dynamics, and function. She collaborates closely with experimental and clinical researchers, integrating simulations with data from biophysical, biochemical, and clinical studies. Her work also contributes to interdisciplinary efforts aimed at connecting molecular-level insights to cellular processes and disease mechanisms.

Dr.-Ing. Mario Rüttgers is a visiting professor and postdoctoral researcher at the Data-Driven Fluid Engineering (DDFE) Laboratory, Inha University, Incheon, South Korea. He is supported by the Walter Benjamin Fellowship of the German Research Foundation (DFG). He received his doctoral degree from RWTH Aachen University in 2024, after conducting research at both the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, and the Institute of Aerodynamics (AIA), RWTH Aachen University. Following his doctorate, he was a postdoctoral researcher at JSC funded by the HANAMI project, where he collaborated closely with researchers from RIKEN, Kobe, Japan. He continues to work with partners from RIKEN and JSC on the project Deep Neural Networks for CFD Simulations within the Joint Laboratory for Extreme Scale Computing (JLESC).
His research focuses on the intersection of machine learning, numerical methods, and High-Performance Computing (HPC) for bio-medical and smart city applications. These applications range from patient-specific diagnosis and treatment of respiratory and hemodynamic diseases, to drag-based route planning for unmanned aerial vehicles (UAVs) in urban environments, and the optimal placement and operation of urban wind turbines for sustainable energy production. Beyond applications, he (co-)develops computational methods and tools, including HydroGym, an open-source framework that couples reinforcement learning with CFD for flow control, and urbanFlowGen, a tool that automates the setup of CFD simulations in urban environments on large-scale HPC systems.