We are proud to present the following speakers for our summer school:
George Em Karniadakis is a full professor at Brown University in applied mathematics and has a joint appointment with Pacific Northwest National Laboratory, where he is the director of the Physics-Informed Learning Machines Collaboratory on physics-informed learning. With an h-index of 120, he is currently one of the most influential mathematicians. His current research interests are in machine learning for scientific computing (Scientific Machine Learning), that is, how to solve and discover new PDEs via deep learning methods.
Khemraj Shukla is an assistant professor of applied mathematics at Brown University, working in the group of Prof. Karniadakis. He received his Ph.D. degree in computational geophysics. In his Ph.D., he studied high-order numerical methods for hyperbolic systems and finished his research works with the GMIG Group of Rice University. His research focuses on the development of scalable codes on heterogeneous computing architectures.
Tilman Plehn is a tenured professor at the Institute of Theoretical Physics, University of Heidelberg, where he leads the group for Particle Phenomenology and Machine Learning. His research focuses on simulations of particle systems as well as applying statistical and machine learning methods to Large Hadron Collider (LHC) data, with a particular focus on uncertainty quantification.
Anja Butter is a postdoctoral Research at the Particle Phenomenology and Machine Learning group, where she works on particle physics in the standard model and beyond, as well as on the development of machine learning techniques tailored for application in these fields.
Pietro Faccioli is an associate professor at the Physics Department of the University of Trento in the Statistical and Biological Physics Group and affiliated member of the Trento Institute for Fundamental Physics and Applications (INFN-TIFPA). His research interests range from quantum chromodynamics and hadron physics over quantum and statistical field theory to statistical mechanics of complex systems, including enhanced sampling techniques and their application to biophysics.
Pascal Friederich is a tenure-track professor at the Karlsruhe Institute of Technology, leading the AiMat research group (https://aimat.science). His research focuses on developing and applying machine learning methods for molecular design, materials simulations, and materials acceleration platforms.
Marlen Neubert is a PhD student in the AiMat group, working in the HIDSS4Health graduate school on developing neural network potentials for the simulation of chemical reactions in biomaterials.