George Karniadakis received his S.M. (1984) and Ph.D. (1987) from Massachusetts Institute of Technology. He was appointed Lecturer in the Department of Mechanical Engineering at MIT in 1987 and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech (1993) in the Aeronautics Department. He joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics on January 1, 1994. He became a full professor on July 1, 1996. He has been a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT since September 1, 2000. He was Visiting Professor at Peking University (Fall 2007 & 2013). He is a Fellow of AAAS (2019), the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the SIAM/ACM Prize in Computational Science & Engineering (2021), the SIAM Ralf Kleinman Award (2015), CFD award (2007) and the inaugoral J Tinsley Oden Medal (2013) by the US Association in Computational Mechanics. His h-index is 141 and he has been cited over 100000 times.

Steven L. Brunton received the B.S. degree in mathematics from the California Institute of Technology, Pasadena, CA, USA, in 2006, with a focus on control and dynamical systems, and the Ph.D. degree in mechanical and aerospace engineering from Princeton University, Princeton, NJ, USA, in 2012. He is currently a Professor of mechanical engineering and a Data Science Fellow with the eScience Institute, University of Washington. Dr. Brunton’s research focuses on combining techniques in dimensionality reduction, sparse sensing, and machine learning for the data-driven discovery and control of complex dynamical systems. He is also interested in how low-rank coherent patterns that underlie high-dimensional data facilitate sparse measurements and optimal sensor and actuator placement for control. He is developing adaptive controllers in an equation-free context using machine learning. Specific applications in fluid dynamics include closed-loop turbulence control for mixing enhancement, bio-locomotion, and renewable energy. Other applications include neuroscience, medical data analysis, networked dynamical systems, and optical systems.

Thomas Beckers is an Assistant Professor of Computer Science and the Institute for Software Integrated Systems at Vanderbilt University. Before joining Vanderbilt, he was a postdoctoral researcher at the Department of Electrical and Systems Engineering, University of Pennsylvania, where he was member of the GRASP Lab, PRECISE Center and ASSET Center. In 2020, he earned his doctorate in Electrical Engineering at the Technical University of Munich (TUM), Germany. He received the B.Sc. and M.Sc. degree in Electrical Engineering in 2010 and 2013, respectively, from the Technical University of Braunschweig, Germany. In 2018, he was a visiting researcher at the University of California, Berkeley. He is a DAAD AInet fellow and was awarded with the Rhode & Schwarz Outstanding Dissertation prize. His research interests include physics-enhanced learning, nonparametric models, and safe learning-based control.

Ankush Chakrabarty received the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, IN, USA, in 2016. He was a Postdoctoral Fellow with Harvard University, Cambridge, MA, USA, from 2016 to 2018, where he worked on the conceptualization and development of an embedded artificial pancreas system. He has been with Mitsubishi Electric Research Laboratories, Cambridge, MA, USA, since 2018, where he is currently a Principal Research Scientist. His research lies in the intersection of machine learning and control engineering, focusing on Bayesian optimization, meta-learning, and state/parameter estimation using simulation environments and digital twins of building energy systems. Dr. Chakrabarty serves as the Outreach Coordinator with the IEEE CSS Electronic Information Committee, and as Associate Editor for CSS and SMC conferences. He has an Erd˝os number of 4. He was a Ross Fellow with Purdue University.

Rajiv Singh is a senior team lead in the Controls and Identification team at MathWorks, focusing on system identification, controls, and design optimization products for MATLAB® and Simulink®. Rajiv received his Master’s in Mechanical Engineering from Purdue University, and PhD in Electrical and Computer Engineering from Northeastern University. His research focuses on development of tractable system identification algorithms using rational interpolation and machine/deep learning techniques.

Dario Piga received his Ph.D. in Systems Engineering from the Politecnico di Torino (Italy) in 2012. He was Assistant Professor at the IMT School for Advanced Studies Lucca (Italy) and since 2017 he has been Senior Researcher at the SUPSI-IDSIA Dalle Molle Institute for Artificial Intelligence in Lugano (Switzerland), founder and head of the “LEarning for Optimization and control (LEON)” at the Dalle Molle Institute for Artificial Intelligence (IDSIA). He has co-authored more than 150 peer-reviewed scientific papers in leading international journals and conferences in the fields of system identification, control theory, machine learning, and nonlinear optimization. He has collaborated with international companies and coordinated several research projects for the development of innovative AI-based systems in the manufacturing, transportation, biomedical and chemical industry. He is Associate Editor of the IFAC journal Automatica and the IEEE-CSS Conference Editorial Board.

Yuhan Liu received the B.S., M.S., and Ph.D. degrees in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2015, 2017 and 2022, respectively. From 2019 to 2021, she was a visiting researcher in the Control Systems Group, Eindhoven University of Technology (TU/e), Eindhoven, the Netherlands, where she currently holds a position as a Postdoctoral Researcher. Her research interests focus on nonlinear system identification, data-driven and machine learning methods for modeling and control. She is a member of the IEEE CSS Technical Committee on System Identification and Adaptive Control, as well as the IFAC TC 2.1 on Adaptive and Learning Systems.