Sebastian Trimpe is a Full Professor (W3) at RWTH Aachen University, where he heads the newly founded Institute for Data Science in Mechanical Engineering (DSME) since May 2020. Research at DSME focuses on fundamental questions at the intersection of control, machine learning, networks, and robotics. Before moving to RWTH, Sebastian was a Max Planck and Cyber Valley Research Group Leader (W2) at the Max Planck Institute for Intelligent Systems in Tübingen/Stuttgart. Sebastian obtained his Ph.D. degree in 2013 from ETH Zurich with Raffaello D'Andrea at the Institute for Dynamic Systems and Control. Before, he received a B.Sc. degree in General Engineering Science in 2005, a M.Sc. degree (Dipl.-Ing.) in Electrical Engineering in 2007, and an MBA degree in Technology Management in 2007, all from Hamburg University of Technology. In 2007, he was a research scholar at the University of California at Berkeley. Sebastian is the recipient of the triennial IFAC World Congress Interactive Paper Prize (2011), the Klaus Tschira Award for achievements in public understanding of science (2014), the Best Paper Award of the International Conference on Cyber-Physical Systems (2019), and the Future Prize by the Ewald Marquardt Stiftung for innovations in control engineering (2020).

Colin N. Jones received bachelor’s and master’s degrees in electrical engineering and mathematics from The University of British Columbia, Vancouver, BC, Canada, in 1999 and 2001, respectively, and the Ph.D. degree, for his work on polyhedral computational methods for constrained control, from the University of Cambridge, Cambridge, U.K., in 2005. He was a Senior Researcher with the Automatic Control Laboratory, ETH Zürich, Zürich, Switzerland, until 2010. He has been an Assistant Professor with the Automatic Control Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, since 2011, where he has been an Associate Professor since 2017. He is the author or a coauthor of more than 200 publications. His current research interests include high-speed predictive control and optimization, and the control of green energy generation, distribution, and management. Dr. Jones has received the ERC Starting Grant to study the optimal control of building networks. He is a senior member of IEEE.

Ali Mesbah is Associate Professor of Chemical and Biomolecular Engineering at the University of California at Berkeley. Before joining UC Berkeley, Dr. Mesbah was a senior postdoctoral associate at MIT. He holds a Ph.D. degree in Systems and Control and a Master’s degree in Chemical Engineering, both from Delft University of Technology. Dr. Mesbah is a senior member of the IEEE and AIChE. He serves on the Editorial Boards of the IEEE Transactions on Control Systems Technology, IEEE Control Systems Letters, and IEEE Transactions on Radiation and Plasma Medical Sciences. Dr. Mesbah is recipient of the Alexander von Humboldt Research Fellowship in 2023, the Best Application Paper Award of the IFAC World Congress in 2020, the AIChE's 35 Under 35 Award in 2017, the IEEE Control Systems Outstanding Paper Award in 2017, and the AIChE CAST W. David Smith, Jr. Publication Award in 2015. His research interests lie at the intersection of optimal control, machine learning, and applied mathematics, with applications to learning-based analysis, optimization, and predictive control of materials processing and manufacturing systems.

Mehmet Mercangöz received the B.Sc. and M.Sc. degrees in chemical engineering from Boğaziçi University, İstanbul, Turkey, in 2000 and 2002, respectively, and the Ph.D. degree in chemical engineering from the University of California at Santa Barbara, Santa Barbara, CA, USA, in 2007. He joined ABB Corporate Research, Baden-Dättwil, Switzerland, after completing the Ph.D. degree and worked as a Scientist and Principal Scientist with the Control and Optimization Group eventually assuming the group leader role there, from 2012 to 2018. In 2018, he was promoted to a Senior Principal Scientist role with ABB and also started lecturing with the Department of Information Technology and Electrical Engineering, ETH Zürich, which he continued to do until 2022. In 2021, he joined the Chemical Engineering Department, Imperial College London, and the Sargent Centre for Process Systems Engineering, as a Reader/an Associate Professor, where he currently leads the Autonomous Industrial Systems Laboratory. He has authored more than 60 research articles and is listed as an inventor in more than 35 patents and patent applications. His research interests include the integration of artificial intelligence technologies into process monitoring, optimization, and control applications for increasing the autonomous operation capabilities of industrial processes.

Valentina Breschi received her M.Sc. degree in electrical and system engineering from the University of Florence, Italy, in 2014. She received her Ph.D. degree in control systems from IMT School for Advanced Studies in Lucca, Italy, in 2018. In 2017, she was a visiting scholar in the Department of Aerospace Engineering, University of Michigan, Ann Arbor, USA. From 2018 to 2020, she held a postdoctoral position at Politecnico di Milano, Italy, where she was a junior assistant professor from 2020 to 2023. Currently, she is an assistant professor at Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands. Her main research interests include hybrid system identification, data-driven control, data analysis, and policy design for mobility systems. She is a Member of IEEE.

Alisa Rupenyan is a professor at Zurich University for Applied Science. She was a senior scientist at the Automatic Control Lab at ETH Zurich and Head of the Advanced Control and IoT group at Inspire, the technology transfer organization at ETH Zurich. She led research projects in the intersection between process optimization, industrial control, and machine learning for applications strongly connected to manufacturing. Previously, she led the application development in the Zurich-based startup Qualysense AG for fast grain-sorting robots using machine learning techniques for high-dimensional data. Her Ph.D. is in the field of time-resolved laser spectroscopy from the Vrije Universiteit Amsterdam (2009) and her MSc degree is in Laser Physics from the University of Sofia. 

Dinesh Krishnamoorthy is an Assistant professor at the Department of Mechanical Engineering at TU Eindhoven, where he is a part of the Control Systems Technology group. He was a post-doctoral researcher at Harvard John A. Paulson School of Engineering and Applied Sciences. Dinesh received his PhD in Process Systems Engineering from the Norwegian University of Science and Technology (NTNU), MSc in Control Systems from Imperial College London, and B.Eng in Mechatronics from the University of Nottingham. Dinesh also has more than four years of industrial research experience. He was working as a Senior Researcher at Statoil Research Centre, Norway between 2012-2016, and was also a part-time Senior Data Science consultant for Novo Nordisk R&D (2021). Dinesh is the recipient of the Dimitris N. Chorafas Foundation Award (as one of 35 worldwide), Excellence in Computer-Aided Process Engineering (CAPE) PhD Award by the European Federation of Chemical Engineers (EFCE), NTNU Faculty of Natural Sciences Best PhD Thesis Award, as well as IFAC Young author award. His research interests include distributed optimization, numerical optimal control, real-time optimization, and Bayesian optimization, with applications to energy systems.

Yuning Jiang received his B.Sc. degree in electronic engineering from Shandong University, China, in 2014, and his Ph.D. degree in information engineering from the University of Chinese Academy of Sciences, China, in 2020. He was a Visiting Scholar with the University of California at Berkeley, Berkeley, University of Freiburg, and Technische Universität Ilmenau, during his Ph.D. study. He is currently a Postdoctoral Researcher with the Automatic Control Laboratory at EPFL. His research focuses on learning- and optimization-based policy for operating complex systems, such as nonlinear autonomous systems (e.g., autonomous vehicles, robotics, and smart buildings), and large-scale multiagent systems (e.g., power and energy systems, IoT, and traffic networks).

Wenjie Xu is currently a PhD student in Electrical Engineering at EPFL as part of NCCR Automation, working with Prof. Colin Jones. He is also working with Dr. Bratislav Svetozarevic from Empa on building control. He received his MPhil from the Department of Information Engineering at the Chinese University of Hong Kong, where he worked with Prof. Minghua Chen. Before that, he received a BEng in Electronic Engineering and (dual) BSc in Math, both from Tsinghua University in 2018. His research interest lies in the integration of optimization, control, and learning, with applications to building control and intelligent transportation. In particular, his recent interest focuses on Bayesian optimization and its applications to occupant-centric self-optimizing building thermal control. He received the ASME ESTC best paper award at ACC '22 for his work on violation-aware Bayesian optimization.