Attila Cangi is a German scientist and the Head of the Department at the Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf. He holds a Ph.D. in Chemistry and an M.Sc. in Physics, with his education spanning the University of California, Irvine, Rutgers, The State University of New Jersey, and the University of Würzburg. Cangi has supervised numerous students and postdocs, and has received several awards, including the R&D 100 Award and SPOT Awards from Sandia National Laboratories. His research focuses on multiscale materials modeling, machine learning in electronic structure theory, and improving predictive capabilities in high-energy density science simulations. He has led and participated in several collaborative projects funded by the United States Department of Energy. Cangi has organized and contributed to numerous scientific meetings and workshops, and has delivered invited talks at various institutions. His publications span a wide range of topics, including machine learning in density functional theory, electronic structure calculations, and the study of warm dense matter. He has also published technical reports and book chapters, and developed software tools such as *atoMEC* and *Materials Learning Algorithms (MALA)*.Attila Cangi is a German scientist and the Head of the Department at the Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf. He holds a Ph.D. in Chemistry and an M.Sc. in Physics, with his education spanning the University of California, Irvine, Rutgers, The State University of New Jersey, and the University of Würzburg. Cangi has supervised numerous students and postdocs, and has received several awards, including the R&D 100 Award and SPOT Awards from Sandia National Laboratories. His research focuses on multiscale materials modeling, machine learning in electronic structure theory, and improving predictive capabilities in high-energy density science simulations. He has led and participated in several collaborative projects funded by the United States Department of Energy. Cangi has organized and contributed to numerous scientific meetings and workshops, and has delivered invited talks at various institutions. His publications span a wide range of topics, including machine learning in density functional theory, electronic structure calculations, and the study of warm dense matter. He has also published technical reports and book chapters, and developed software tools such as *atoMEC* and *Materials Learning Algorithms (MALA)*.