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Colin JonesView profile

Associate Professor

• Optimal Control
• Model Predictive Control
• Optimization
• +6 more

Colin Jones is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Automatic Control Laboratory, School of Engineering. He earned his BASc and MASc in Electrical Engineering and Mathematics from the University of British Columbia (1994-2002) and a PhD in Control Theory from the University of Cambridge (2002-2005). Prior to EPFL, he was an assistant professor there and a senior researcher at ETH Zürich. Current role: Director of the Robotics, Control, and Intelligent Systems Doctoral Program at EPFL Research focus: Optimization-based and model predictive control (MPC) for renewable energy systems, green energy management, and data-driven control methods His recent work (2023-2025) spans high-speed predictive control , smart grid optimization , and physically consistent neural networks , with applications to buildings, hovercrafts, and power systems. He has secured an ERC Starting Grant for his research on optimal control of building networks. Publications include over 200 papers in journals like Automatica , IEEE Transactions , and Energy and Buildings . Notable article trends include distributed optimization , data privacy in energy systems , and nonlinear MPC for autonomous vehicles . Scientific Awards : ERC Starting Grant for optimal control of building networks Advising : Supervises 10 current PhD students and has advised 19 past PhD students, including Alessandretti Andrea and Diwale Sanket Sanjay. Grants and projects emphasize smart energy systems , predictive demand response , and nonlinear control .

Melanie ZeilingerView profile

Associate Professor

• Learning-based Control
• Distributed Control and Optimization
• Robotics
• +4 more

Prof. Dr. Melanie Zeilinger is an Associate Professor at the Department of Mechanical and Process Engineering at ETH Zurich, leading the Intelligent Control Systems group at the Institute for Dynamic Systems and Control. She holds a diploma in Engineering Cybernetics from the University of Stuttgart (2006) and a Ph.D. in Electrical Engineering from ETH Zurich (2011). Her postdoctoral research included stints at EPFL (2011–2012), a Marie Curie fellowship at UC Berkeley and the Max Planck Institute (2012–2015), and a professorship at the University of Freiburg (2018–2019). Her research focuses on learning-based control, distributed control systems, and robotics , with applications to medical devices (e.g., hydrocephalus shunts) and human-in-the-loop systems. She organizes the Conference on Learning for Dynamics and Control (L4DC) and contributes to initiatives like the "Algorithm on My Team" project. Her awards include the ETH Medal for her PhD thesis, a Marie-Curie IO Fellowship , and an SNF Assistant Professorship grant . She serves as an Associate Editor for IEEE Control Systems Letters and actively reviews for top journals/conferences like IEEE TAC, Automatica, and NeurIPS. Key projects include: VIEshunt: A smart ventricular shunt for hydrocephalus treatment, combining control systems and medical engineering. Autonomous Racing: Contextual tuning and safety-certified learning-based MPC for real-time obstacle avoidance. Data-Driven Control: Integrating Gaussian processes and state-space models into MPC frameworks for uncertain systems. Her work bridges control theory, machine learning, and robotics, addressing societal challenges such as healthcare and energy efficiency.

Andrea MartinelliView profile

Lecturer

• Optimal Control
• Reinforcement Learning
• Decentralized Control
• +4 more

Dr. Andrea Martinelli is a Lecturer and Postdoctoral Researcher at the Automatic Control Laboratory (IfA), ETH Zurich. He holds a PhD in Automatic Control from ETH Zurich (2024) under Prof. John Lygeros, an MSc in Control Engineering (2017) from Politecnico di Milano, and a BSc in Management Engineering (2015). His research focuses on optimal control, reinforcement learning, and decentralized control strategies for large-scale systems, emphasizing scalability and applicability to renewable energy systems. He received the ETH Medal for his doctoral thesis on data-driven control methods. Education: BSc in Management Engineering, Politecnico di Milano (2015) MSc in Control Engineering with Honours, Politechnico di Milano (2017) PhD in Automatic Control, ETH Zurich (2024) Research Interests: Optimal control and reinforcement learning Data-driven methods for control systems Decentralized control of interconnected systems Dissipativity theory and passivity-based approaches Applications in renewable energy systems (DC microgrids) Teaching & Outreach: Program Manager for the CAS ETH in Automation Teaching a post-graduate course on automation in 2025 Professional Activities: Worked at Laboratoire d'Automatique (EPFL) during MSc thesis (2017) Research Assistant with Prof. R. Scattolini, Politecnico di Milano (2018)

Yuning JiangView profile

Visiting Professor

• Optimal Control
• Model Predictive Control
• Power Systems
• +4 more

Yuning Jiang is a Visiting Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Automatic Control Laboratory (LA3) within the School of Engineering (STI). He teaches the doctoral course Optimal Control for Dynamic Systems and contributes to research in distributed optimization, model predictive control (MPC), and smart grid technologies. His work bridges theoretical advancements in control systems with practical applications in power networks and autonomous systems. Current research emphasizes scalable solutions for AC optimal power flow, real-time MPC for embedded systems, and robust optimization under uncertainty. His research interests span Optimal Control , Power Systems , Smart Grids , and Federated Learning . Notable contributions include distributed algorithms for large-scale power systems and privacy-preserving co-simulation frameworks. Recent publications focus on microservice deployment in satellite-terrestrial networks and real-time pricing mechanisms for vehicle-to-grid (V2G) integration. Yuning holds a position in the EDEE-ENS unit under EPFL’s Academic Affairs division (VPA-AVP-DLE), reflecting his role in academic administration and teaching infrastructure. His lab, the Automatic Control Laboratory, focuses on cutting-edge research in control theory and its interdisciplinary applications.

Andrea Del PreteView profile

Associate Professor

• Robot Control
• Reinforcement Learning
• Trajectory Optimization
• +7 more

Andrea Del Prete is an Associate Professor in the Industrial Engineering Department at the University of Trento (Italy) since 2022. His research focuses on robot control, reinforcement learning, trajectory optimization, and numerical algorithms for dynamic systems. He leads the Interdepartmental Robotics Lab (IDRA) and has previously held roles as a tenure-track assistant professor at the University of Trento (2019-2021), a research scientist at the Max-Planck Institute for Intelligent Systems (2018), and an associated researcher at LAAS-CNRS (2014-2017) working with the HRP-2 humanoid robot. Earlier, he conducted PhD and post-doc research at the Italian Institute of Technology (2010-2013) on iCub robot control. PhD in Robotics (2013) - Italian Institute of Technology MEng in Computer Engineering (2009) - University of Bologna BSc in Computer Engineering (2006) - University of Bologna Dr. Del Prete specializes in merging learning and model-based techniques for safe robot control, particularly in legged systems. His work bridges trajectory optimization (TO) with reinforcement learning (RL) to overcome local minima challenges (CACTO/CACTO-SL algorithms) and develops robust controllers for humanoid and quadrupedal robots in unstructured environments. He explores viability kernels in MPC, safety certificates, and bi-level optimization for co-designing hardware/control policies. Key application areas include mountain rescue robotics (ALPINE platform), aerial maneuver recovery, and energy-efficient legged locomotion. His recent publications (2023-2025) emphasize numerical optimization algorithms, multi-contact locomotion, and hybrid control frameworks. Topics span from analytical integral optimization (2025) to climbing robots for mountain operations (2025), demonstrating a trajectory from theoretical algorithm development to real-world robotic applications. Research keywords include robotics, numerical optimization, and machine learning, with sub-fields like MPC for dynamic systems, humanoid control, and terrain adaptation. As an educator, he teaches advanced courses on: Optimization and Learning for Robot Control (48-hour master's course) Optimization-based Control of Legged Robots (12-hour PhD course) Task-Space Inverse Dynamics (3-hour PhD course) Current PhD advisees include Mohammad Hasan Yeganegi (generalization bounds for imitation learning), Pietro Noah Crestaz (numerically-efficient RL), Veronica Campana (ergodic control for defect detection), Elisa Alboni (data-efficient model-based RL), and Gianni Lunardi (MPC for legged locomotion).

Tobias GeyerView profile

Research Professor

• Power Electronics
• Control Systems
• Model Predictive Control
• +3 more

Tobias Geyer is a Research Professor and Privatdozent at ETH Zurich's Department of Information Technology and Electrical Engineering, with an adjunct role as Extraordinary Professor at Stellenbosch University, South Africa. He leads ABB's R&D platform for medium-voltage drives (ACS6000/6080) and serves as a Corporate Executive Engineer for power converter control methods. His career spans roles at GE Research, the University of Auckland, and ABB's Corporate Research Centre. Education: Dipl.-Ing. (ETH Zurich, 2000), Ph.D. (ETH Zurich, 2005), Habilitation (ETH Zurich, 2017). Research focuses on model predictive control (MPC), high-power converters, and grid integration strategies. Key areas include optimized pulse patterns, thermal management, and control of medium-voltage drives. Notable achievements include the IEEE Fellow distinction (2022), Semikron Innovation Award (2021), and five prize paper awards. He authored a seminal book on MPC in power electronics and serves on editorial boards for top journals. Current projects emphasize digitalization, predictive maintenance, and special applications in oil/gas and offshore wind sectors.

Sossan FabrizioView profile

Associate Professor

• Power Systems
• Energy Storage Systems
• Renewable Energy Integration
• +4 more

Prof. Sossan Fabrizio is an Associate Professor of Power Systems at HES-SO Valais-Wallis, focusing on energy storage, renewable integration, and smart grid technologies. He holds a PhD from DTU (2014) and has held roles at EPFL, ETHZ, and Mines ParisTech. His research emphasizes optimizing distribution grids, hydropower flexibility, and EV charging infrastructure. He leads projects like STOR-HY (Hybrid Hydropower Control) and STORE (Swiss Renewable Energy Storage). Education: Bachelor's/Master's in Computer Engineering, University of Genova (2010) PhD in Electrical Engineering, Technical University of Denmark (2014) Research Interests: Planning/scheduling/control of distributed energy resources, energy storage systems, and grid dispatchability. Areas include hydropower penstock stress reduction, EV charging infrastructure optimization, and model predictive control. Articles Overview: Over 20 peer-reviewed publications since 2013, focusing on grid integration of storage, renewable curtailment, and frequency control. Recent work includes optimal EV charging station planning (2023) and stress-informed MPC for hydropower plants (2022). Advising/Grants: Supervised PhD students like Stefano Cassano (defended 2023) and Biswarup Mukherjee. Secured grants including Horizon 2020 STOR-HY (2024) and Swiss Innovation Agency projects. Leads the ResiNet initiative on grid resiliency. Labs/Teams: Co-founded ModBESS (2020) for microgrid monitoring systems. Active in experimental facilities like the CIGRE MV network and Waterloo Institute’s hydropower lab.

• International Security Policy
• Swiss Security Policy
• Conflict Analysis
• +3 more

Leo Eigner is a Researcher affiliated with the Department of Swiss and International Security Policy at ETH Zürich, Switzerland. He is part of the Professorship for International and Swiss Security Policy. His work focuses on strategic security analysis, conflict resolution mechanisms, and policy frameworks related to national and international security dynamics. His research interests include the application of quantitative methods to security studies, policy evaluation frameworks, and interdisciplinary approaches to addressing contemporary security challenges. He collaborates on projects involving geopolitical risk assessment and institutional resilience strategies. Eigner has contributed to over 20 peer-reviewed publications since 2011, with notable work in control theory and optimization applied to security contexts. His articles explore stability verification of control systems, region-of-attraction computations, and optimization-based methods for nonlinear systems. Key contributions include advancements in sum-of-squares programming and semidefinite programming techniques for complex system analysis. No specific academic awards or grants are explicitly mentioned in the provided materials. His professional activities are centered on maintaining rigorous research output within the security policy domain.

Charbel ToumiehView profile

Research Fellow

• Autonomous Systems
• Robotics
• Aerial Robotics
• +3 more

Charbel Toumieh is a Research Fellow at the École Polytechnique Fédérale de Lausanne (EPFL), based in the Intelligent Systems Laboratory (LIS) under the School of Engineering (STI). His research focuses on advanced robotics, particularly in aerial systems, motion planning, and autonomous systems. He holds a postdoctoral position and contributes to projects involving multi-agent coordination, high-speed navigation, and energy-efficient drone designs. Key research areas include teleoperation of aerial swarms, adaptive morphing for avian-inspired drones, and decentralized multi-agent planning. His work addresses challenges in cluttered environments, dynamic obstacle avoidance, and real-time trajectory optimization. The LIS lab, part of the Institute of Microengineering (IGM), emphasizes innovative solutions in intelligent systems and robotics. Recent publications highlight advancements in motion planning algorithms, safe corridor generation using voxel grids, and GPU-accelerated exploration techniques. His research bridges theoretical control systems with practical applications in autonomous robotics, aiming to enhance efficiency and resilience in robotic systems.

Andrea CarronView profile

Senior Lecturer

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +7 more

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

• Model Predictive Control
• Nonlinear Systems Control
• Robust Control
• +3 more

Dr. Johannes Köhler is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, Switzerland. His research focuses on advanced control systems, particularly model predictive control (MPC), robust control of nonlinear systems, and data-driven control strategies. He holds a Master's degree in Engineering Cybernetics from the University of Stuttgart (2017) and a Ph.D. in Mechanical Engineering (2021), for which he received the 2021 European Systems & Control PhD Award. His work emphasizes safety, optimization, and adaptability in control systems, with applications ranging from autonomous navigation to energy-efficient building systems. Key academic achievements include the 2022 ETH Zurich Career Seed Award for his work on data-driven predictive control, and the 2020 Best Publication Award at the University of Stuttgart for applying MPC to combat the COVID-19 outbreak. He has authored over 60 peer-reviewed articles, with recent contributions in stochastic MPC, robust nonlinear control, and safety-aware exploration strategies. Education: Ph.D. in Mechanical Engineering, University of Stuttgart (2021) Master of Engineering Cybernetics, University of Stuttgart (2017) His research group at ETH Zürich develops algorithms for real-time control of complex systems, with a focus on embedded MPC for robotics and energy systems. Current projects include safe autonomous navigation, adaptive control under uncertainty, and data-driven methods for nonlinear systems.

Moreno ColomboView profile

Researcher

• Human-Computer Interaction
• Human-Building Interaction
• Smart Cities
• +12 more

Moreno Colombo is a postdoctoral researcher and doctoral assistant at the Department of Computer Science, University of Fribourg, affiliated with the Human-IST Institute. He holds a PhD in Phenotropic Interaction and is actively engaged in research and teaching within the Faculty of Mathematics, Natural Sciences and Medicine. His work bridges human-centered computing, smart cities, and sustainable technology design. His research interests focus on making human-technology interaction more natural and personalized. Key areas include Human-Computer Interaction (HCI), Human-Building Interaction, Smart Cities, Sustainability, Green Mobility, and the application of machine learning and fuzzy logic in perceptual computing. He specializes in Computing with Words and Phenotropic Interaction, aiming to reduce protocol dependency in interfaces. His recent publications (2020–2024) demonstrate a consistent focus on human-centered smart environments, including lighting systems, urban perception mapping, citizen engagement in smart cities, and semantic modeling for natural language understanding. These works reflect interdisciplinary collaboration and a strong commitment to user experience and environmental sustainability. PhD in Phenotropic Interaction, University of Fribourg He has supervised numerous Bachelor’s and Master’s theses on topics such as mobility visualization, smart city applications, and human-building interfaces. While no scientific awards are listed, his active publication record and involvement in international conferences indicate strong recognition in the research community. Moreno Colombo leads and contributes to projects involving crowdsourcing, machine learning, and fuzzy systems, often in collaboration with researchers across disciplines. His labs and research teams include the Human-IST Institute and collaborations within the Energy Informatics and Engineering departments.

Kimia Chavoshi BoroujeniView profile

Assistant Professor

• Nonlinear systems
• Cooperative control center networks
• Model Predictive Control (MPC)
• +4 more

Dr. Kimia Chavoshi Boroujeni is an Assistant at the Chair of Control and Computation under the Institute for Automatic Control at ETH Zurich. Her research focuses on advanced traffic management systems for autonomous and connected vehicles. PhD in Civil Engineering (Road Traffic Engineering), ETH Zurich (2019–2024) MSc in Electrical Engineering (Control Systems), Sharif University of Technology (2016–2018) BSc in Electrical Engineering (Control Systems), Sharif University of Technology (2011–2015) Her work addresses complex control challenges in nonlinear systems , model predictive control (MPC) , and autonomous vehicle coordination , particularly in lane-free and direction-free highway systems. She has developed innovative strategies like feedback linearization and Karma games to enhance fairness and efficiency in traffic networks. Notable projects include ASTRA BGT for automated driving in road tunnels and RECCE for real-time traffic estimation. Her publications span journals like IEEE Transactions on Intelligent Transportation Systems and Electronics, alongside conferences like TRB and STRC.

Thomas StastnyView profile

Researcher

• Unmanned Aerial Vehicles (UAVs)
• Autonomous Systems
• Control Theory
• +3 more

Dr. Thomas Stastny is affiliated with ETH Zürich's Autonomous Systems Department as a Researcher. His work focuses on advancing unmanned aerial vehicle (UAV) technologies, particularly in control systems, environmental monitoring, and autonomous navigation. Key research areas include fixed-wing UAV dynamics, solar-powered UAVs, wind-aware path optimization, and multi-agent robotic systems. His contributions span theoretical frameworks and experimental validations, with field applications in glacier monitoring and long-endurance aerial sensing. Research Highlights: Developed real-time wind prediction systems for UAVs (WindSeer project) Pioneered hybrid UAV designs like Soliro and Dipper for multimodal mobility Advanced control methodologies for high-angle-of-attack flight and VTOL transitions Explored solar-powered UAVs achieving multi-day endurance records Technical Expertise: Nonlinear model predictive control (MPC) Sensor fusion (radar/vision integration) Aerodynamic modeling and system identification Autonomous navigation algorithms His research integrates robotics, aerospace engineering, and environmental science to create robust airborne systems for challenging operational environments.

• Control Systems
• Model Predictive Control (MPC)
• System Identification
• +6 more

Prof. Roy Smith is a Professor at ETH Zurich specializing in control systems, with a focus on model predictive control (MPC), system identification, and robust control. His research spans energy systems, aerospace applications, and data-driven methods. He collaborates with leading institutions like the Swiss Federal Institute of Technology (ETH Zurich) and has co-authored numerous high-impact papers in top journals like IEEE Transactions on Automatic Control and Automatica. Key research interests include: distributed control of interconnected systems, kernel-based system identification, and real-time optimization for energy networks. His work bridges theoretical advancements with practical applications in buildings, renewable energy, and aerospace. Recent publications highlight contributions to MPC design under uncertainty, closed-loop identification techniques, and stochastic control systems. He has pioneered methods for energy hub networks and developed tools for data-driven predictive control in smart buildings. Academic collaborations include projects with Prof. John Lygeros (ETH Zurich) and Prof. Florian Dörfler (ETH Zurich), focusing on multi-agent coordination and energy systems. His research also extends to aerospace applications such as autonomous kite power systems and spacecraft pose estimation.