Find Academic Supervisors

• Power Electronics
• Internet of Things (IoT)
• Predictive Maintenance
• +3 more

Tino Gfrörer is a doctoral researcher at the Power Systems and High Voltage Laboratory within the Department of Information Technology and Electrical Engineering at ETH Zürich . His work focuses on power electronics, predictive maintenance, and IoT-enabled sensor platforms. His research spans Design and optimization of multilevel inverter topologies for motor drives Model Predictive Control (MPC) applications in grid-connected converters Edge-sensing systems for condition monitoring using MEMS sensors The articles he co-authored highlight advancements in GaN/Si inverter performance, vibration-based predictive maintenance techniques, and MPC-driven stability improvements for LCL filters. These works intersect with broader fields like renewable energy integration , smart grid technologies , and power semiconductor device engineering .

• Control Systems
• Optimization Algorithms
• Machine Learning
• +3 more

Roland Schwan is a researcher at the Automatic Control Laboratory (LA) within the School of Engineering at the École Polytechnique Fédérale de Lausanne (EPFL) . His work focuses on control systems, optimization algorithms, and the integration of machine learning with physical models. He is actively involved in projects involving model predictive control (MPC), stability verification of neural network controllers, and real-time optimization-based control for robotics applications. His research interests span physics-informed machine learning , quadratic programming solvers , and embedded systems . Key contributions include the development of the PIQP solver for convex quadratic programming and stability analysis frameworks for neural network controllers. He has also explored applications in hovercraft dynamics identification and rocket trajectory control using advanced MPC techniques. Schwan collaborates closely with institutions like the Swiss National Science Foundation and has published extensively in venues such as IEEE Transactions on Automatic Control and IEEE Conference on Decision and Control . His work bridges theoretical control methodologies with practical embedded system implementations.

• Renewable Energy Systems
• Control Engineering
• Solar Energy Optimization
• +5 more

Fariba Moghaddam is an Ordinary Professor and Head of the Power & Control Orientation at the University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis). She specializes in renewable energy systems, control engineering, and remote laboratory technologies. Her work focuses on optimizing solar energy systems, particularly photovoltaic and photovoltaic-thermal (PVT) setups, using advanced control algorithms like Model Predictive Control (MPC) and machine learning. She leads multiple international projects, including the Solar Energy Optimization project and Sustainable Laboratories initiative, promoting sustainable energy and resource sharing in the Global South. Her research integrates real-time data acquisition, IoT, and digital twins for remote experimentation and infrastructure collaboration. Notable projects include developing a remote research platform for solar tracking systems and creating Massive Open Online Laboratories (MOOLs) to enhance global access to engineering education. She has contributed to over 10 peer-reviewed publications, emphasizing solar panel modeling, control systems, and educational technology. Key collaborations include partnerships with institutions in Iran, Lebanon, Niger, and Djibouti, aiming to bridge the digital gap in education and research. Her work aligns with UN Sustainable Development Goals (SDGs) related to clean energy, innovation, and global equity.

Christophe SalzmannView profile

Lecturer

• Real-time Control
• Remote Laboratories
• Online Education Tools
• +3 more

Christophe Salzmann is a Lecturer and Research Associate at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the Automatic Control Laboratory (LA3). He holds multiple roles including Head of IT for SCI-STI-GFT and SCI-STI-AK groups, Head of Operations for DLL-STI-ENG and DLL-STI-GE, and Scientist in SGM-GE. His primary academic rank is Lecturer, teaching in Mechanical Engineering, Electrical and Electronics Engineering, and Microengineering. He completed his PhD in Remote Experimentation and Network at EPFL (2005) and MS in Computer Science at the University of Florida (1999). Salzmann's research focuses on real-time control systems, remote experimentation, and online learning technologies. He pioneered frameworks for Massive Open Online Labs (MOOLs) integrated into MOOC platforms, emphasizing scalable remote laboratory solutions. His work includes standardization efforts like the Smart Device Specification for Remote Labs and Laboratory as a Service (LaaS) paradigms. Key areas include real-time Internet services, quality of service optimization, and LabVIEW-based control solutions. His advising includes PhD students such as Bogdanov Evgeny, Halimi Wissam, and Perritaz Damien. He contributes to labs like the Automatic Control Laboratory (LA3), Decode Lab, and DDMAC Lab. Notable projects involve real-time optimization of solid-oxide fuel cells and adaptive control strategies for building climate systems. Salzmann is actively involved in educational technology innovations, bridging virtual and physical experimentation for global STEM education.

• Model Predictive Control
• Autonomous Systems
• Robotics
• +5 more

Petr Listov is a former researcher at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the School of Engineering. His work focuses on advanced control systems, particularly Model Predictive Control (MPC) for autonomous systems, robotics, and mechatronics. Listov contributed to developing the PolyMPC software framework, enhancing real-time embedded optimization for applications like rocket control and autonomous driving. His research emphasizes nonlinear control, stochastic systems, and optimization under uncertainty. Education: Completed a doctoral thesis titled Real-Time Nonlinear Model Predictive Control for Fast Mechatronic Systems under supervision at EPFL. Research Interests: Development of MPC algorithms for high-speed systems Applications in autonomous vehicles and robotics Stochastic control and uncertainty quantification Optimization-based control strategies Key Contributions: His publications address real-time MPC implementations, thrust vector control for rockets, and stochastic trajectory planning. Experimental validations include indoor/outdoor rocket testing and automotive hardware-in-the-loop systems. Labs/Teams: Collaborated with the Laboratoire d’Automatique (LA) and contributed to the PolyMPC open-source library.

• Smart Grids
• Energy Systems
• Control Systems
• +3 more

Luca Fabietti is a former researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (ENAC) and the Institute of Electrical Engineering (IEL). His work focuses on smart grids, energy systems, control systems, renewable energy integration, and building automation. He contributed to the development of advanced control strategies for distributed energy resources and the optimization of grid services through multi-time scale coordination and energy storage integration. His research includes experimental validation of frequency regulation services using commercial buildings and HVAC systems, as well as the application of model predictive control (MPC) techniques in energy management. Key areas of interest span heterogeneous energy resource coordination, stochastic prosumer dispatch, and demand-side management. His work is published in journals like Applied Energy and Ieee Transactions on Smart Grid , and his findings have been tested in real-world scenarios, including the Swiss energy market. Fabietti collaborated with units such as La3 (Laboratory of Automation 3), DESL (Distributed Electrical Systems Laboratory), and IGM (Intelligent Maintenance and Life Cycle Engineering). His contributions address challenges in grid stability, energy dispatchability, and the integration of renewable resources into smart grids.

Evren Mert TuranView profile

Lecturer

• Process Systems Engineering
• Machine Learning
• Optimization
• +3 more

Dr. Evren Mert Turan is a Lecturer at ETH Zürich's Department of Energy and Process Systems Technology. His academic background includes a Bachelor's and Master's in Chemical Engineering from the University of Cape Town, followed by a PhD in Process Systems Engineering at the Norwegian University of Science and Technology. His research focuses on integrating machine learning and optimization techniques to address decision-making challenges under uncertainty in energy systems and process engineering. Evren's expertise spans model predictive control, real-time optimization, and data-driven approaches for complex systems. He has contributed to advancements in semi-infinite programming, feedback control policies, and steady-state detection algorithms. His work emphasizes practical applications in sustainable energy systems and industrial process optimization. Key research trends include the development of neural network-based control strategies, convex optimization methods for reduced computational complexity, and experimental validation of novel algorithms. His publications highlight interdisciplinary approaches blending machine learning with traditional engineering methodologies. Evren currently teaches the course 'Introduction to Modeling and Optimization of Sustainable Energy Systems' and actively engages in collaborative research at ETH Zürich. His contributions to scientific machine learning aim to enhance robustness and reliability in dynamic systems analysis.

• Process Control
• Real-Time Optimization
• Chemical Engineering
• +3 more

Dominique Bonvin is a Professor at the School of Engineering (STI) of École Polytechnique Fédérale de Lausanne (EPFL), holding a position within the Automatic Control Laboratory (LA). His research focuses on advanced process control, real-time optimization, and model-based systems engineering. Key topics include modifier adaptation for uncertain systems, dynamic optimization of chemical processes, and control strategies for reaction systems and energy systems. He has published extensively in top-tier journals such as Computers & Chemical Engineering and Journal of Process Control , with over 450 scholarly works to date. His work addresses challenges in industrial processes like wastewater treatment plants, gas compressors, and solid-oxide fuel cells, emphasizing robustness against plant-model mismatches. Collaborations span global institutions, with notable contributions to bioprocess modeling and control of energy-harvesting systems such as kite power. Research Themes: Real-Time Optimization (RTO), Model Predictive Control (MPC), Process Automation, and Systems Engineering Key Technologies: Modifier Adaptation, Dynamic Optimization, Port-Hamiltonian Systems, and Extent-Based Reaction Modeling His recent efforts include accelerating optimization algorithms (e.g., gradient-directed modifier adaptation) and integrating transient measurements for improved plant modeling. He has also pioneered the use of vessel extents to simplify reaction system analysis, enabling decoupled state estimation without full kinetic knowledge.