Michele Guerra is a Doctoral Research Fellow at the Department of Mathematics and Statistics , UiT The Arctic University of Norway. His research focuses on graph neural networks (GNNs) , with emphasis on explainability , temporal modeling , and subgraph-based architectures . Recent work includes applications of Koopman theory for GNN interpretation and probabilistic load forecasting using reservoir computing techniques. Research Trends: His publications highlight innovative approaches to improving the expressivity and transparency of graph-based machine learning models, particularly through stochastic methods and subgraph decomposition. Key application domains include energy systems forecasting and dynamic graph analysis . Key Collaborations: Active collaborations with researchers such as Indro Spinelli , Simone Scardapane , and Filippo Maria Bianchi are evident in recent co-authored works presented at venues like the Northern Lights Deep Learning Workshop.
Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.
Frank Allgöwer is a Professor and Head of the Institute for Systems Theory and Control Engineering at the University of Stuttgart's Faculty of Engineering. With an extensive publication record through 2025, he leads a prominent research group specializing in advanced control theory methodologies. His research interests span multiple domains of modern control theory, with particular emphasis on Model Predictive Control (MPC), data-driven control approaches, nonlinear systems analysis, and event-triggered control strategies. His work bridges theoretical foundations with practical implementations, focusing on stability guarantees, performance optimization, and computational efficiency. Recent research shows a strong focus on Koopman operator theory applications to control systems, distributed multi-agent coordination, and the integration of machine learning techniques with traditional control frameworks. Analysis of his 15 most recent publications reveals a consistent research trajectory centered around developing theoretically sound control methodologies with practical applicability. His work demonstrates increasing integration of data-driven approaches with traditional model-based control, particularly in the areas of nonlinear system control and distributed multi-agent systems. The publications span top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters. Professor Allgöwer actively mentors numerous junior researchers, with frequent collaborations suggesting a strong supervisory role for PhD students and postdoctoral researchers. His group maintains productive international collaborations while being firmly rooted at the University of Stuttgart.
Robin Strässer is a Research Associate at the Institute for Systems Theory and Control Engineering at the University of Stuttgart, where he has been working since October 2020. His research focuses on data-based control methods, particularly using the Koopman operator to derive closed-loop control guarantees for nonlinear systems based on measured data. He is also actively involved in developing an autonomous e-scooter project aimed at reducing the university's CO2 emissions and promoting sustainable urban mobility. His research interests span across data-driven control theory, nonlinear systems analysis, and autonomous vehicle systems. Strässer has made significant contributions to the application of Koopman operator theory in control systems, developing methods that provide stability guarantees for nonlinear systems. His work bridges theoretical control concepts with practical applications in sustainable transportation. He has also explored novel applications of control theory in unexpected domains, such as the analysis of cryptosystems using Koopman operator methods. Analysis of his publications reveals a strong focus on the Koopman operator framework for data-driven control, with particular emphasis on stability guarantees and error bounds. His work demonstrates a progression from theoretical foundations to practical implementations, especially in autonomous vehicle systems. The research shows increasing interdisciplinary connections, linking control theory with sustainable transportation, chemical engineering, and even cryptography. Among his notable achievements are the Best Paper Award at the European Robotics Forum (ERF2025) and the Best Poster Award at the International Conference on Data-Integrated Simulation Science (SimTech2023). Strässer has been actively involved in teaching, having organized and supervised practical courses in control engineering and taught courses on system dynamics fundamentals and data-driven control. He has also completed a research stay at EPFL's Data-Driven Modelling and Control Group, further expanding his expertise in data-driven control methods. His current work includes leading the autonomous e-scooter project at the University of Stuttgart, which serves as both a research platform for advanced control algorithms and a practical solution for sustainable campus transportation.
