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.
Prof. Roland Siegwart is a Full Professor of Autonomous Systems at ETH Zurich's Department of Mechanical and Process Engineering since 2006. He leads the Wyss Zurich Translational Center and previously served as Vice President for Research and Corporate Relations (2010–2014). His academic background includes a Diploma (1983) and PhD (1989) in Mechanical Engineering from ETH Zurich, followed by a decade as a professor at EPFL Lausanne (1996–2006) and visiting roles at Stanford University and NASA Ames. His research focuses on robot systems operating in complex environments, emphasizing adaptive autonomy for applications like aerial robotics, driver assistance, and service robots. He has coordinated EU projects, co-founded multiple spin-offs, and holds IEEE Fellow status. Awards include the IEEE RAS Inaba Technical Award and leadership roles in IFRR and robotics journals. Key contributions span reactive navigation, multi-modal localization (e.g., CompSLAM), and UAV path optimization. His work bridges theoretical robotics with real-world applications like corrosion inspection drones and Mars exploration path planning. Current projects emphasize open-source space systems (ATMOS) and neural radiance fields for perception.
Alcherio Martinoli is a Full Professor at the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL), leading the Distributed Intelligent Systems and Algorithms Laboratory (DISAL). He holds additional roles as Director of the ENAC School’s SSIE-GE department and is involved in administrative committees such as the ENAC School Direction and CDS. His expertise spans autonomous robotics, distributed systems, and environmental monitoring. Education: He earned his Diploma in Electrical Engineering from ETH Zurich and a Ph.D. in Computer Science from EPFL. His research focuses on designing, controlling, and optimizing distributed systems, including multi-robot teams and sensor networks, with applications in environmental and civil engineering. Research Interests: His work integrates theoretical models with physical experiments, emphasizing mixed societies of natural and artificial systems. Key areas include swarm robotics, intelligent vehicles, and fluid-mediated self-assembly. He emphasizes closing the theory-experiment loop through computational methods. Publications: His recent work addresses multi-robot system architectures, gas sensing with nano aerial vehicles, and distributed control algorithms. These contributions advance environmental monitoring, autonomous navigation, and swarm coordination. Awards: Recognitions include the Best Teacher Award (2016), Best Poster Award (2016), and SNSF Professorships (2003, 2007). Grants & Advising: Supervises numerous PhD students and has advised over 20 graduates. His grants include projects on distributed robotics and environmental systems, though specific grant details are not explicitly listed in the provided texts. Labs & Teams: Directs the DISAL lab, fostering interdisciplinary research in intelligent systems and their applications. Collaborates on projects involving multi-robot coordination and autonomous systems.
Prof. Dr. Sven Seuken is an Associate Professor at the University of Zurich's Department of Informatics, leading the Computation and Economics Research Group. He is affiliated with ETH Zurich's AI Center and co-directs the Zurich Center for Market Design. His roles include Chief Economist at Worldcoin and founder of Market Design Consulting GmbH. He holds a Ph.D. from Harvard University and has been recognized with awards such as the ERC Starting Grant and Google Faculty Award. His research bridges AI, game theory, and market design, focusing on practical applications like auction mechanisms and resource allocation. Education: Ph.D. in Computer Science, Harvard University (Advisor: David C. Parkes) Research Interests: Market Design Artificial Intelligence Economics and Computation Computational Mechanism Design Algorithmic Game Theory Machine Learning Recent Work Trends: Articles emphasize AI-driven market mechanisms, dynamic auctions, mobile network optimization, and scalable multi-agent systems. Key themes include truthful aggregation, cross-optimization, and reinforcement learning in strategic environments. Awards: ERC Starting Grant (2020) Google Faculty Research Award "Top 40 under 40" by Capital magazine (2017) Grants & Roles: Secured major grants including the ERC MIAMI project. Advises on market design for companies and serves as a mentor for the German Academic Scholarship Foundation. Active in organizing conferences like EC'22. Labs/Teams: Leads the Computation and Economics Research Group and collaborates with the Zurich Center for Market Design to advance applied market solutions.
Prof. Maryam Kamgarpour is a faculty member at ETH Zurich's Automatic Control Laboratory within the Department of Information Technology and Electrical Engineering. Her research focuses on control systems, optimization, and machine learning, with applications in robotics, power systems, and multi-agent systems. She has contributed significantly to safe reinforcement learning, stochastic control, and distributed optimization frameworks. Her work bridges theoretical foundations and practical implementations in areas like safe trajectory planning, market mechanisms for energy systems, and algorithmic guarantees for greedy methods. Key technical areas include model-based multi-agent reinforcement learning, robust control policies under uncertainty, and parameterization techniques in control theory. Collaborations with institutions like ETH Zurich's Automatic Control Lab and researchers such as Andreas Krause highlight her interdisciplinary approach. Her research has been supported by grants from the Swiss National Science Foundation (NCCR Automation) and the European Union (Reliable Data-Driven Decision Making in Cyber-Physical Systems). Publications span top journals and conferences in control systems, machine learning, and robotics, emphasizing rigorous analysis and practical scalability. Recent work addresses challenges in safe black-box optimization, stochastic hazard management in robotics, and market-based resource allocation in power grids.
Prof. Mirko Kovac is a leading academic in robotics, holding professorial and leadership roles at Imperial College London, École Polytechnique Fédérale de Lausanne (EPFL), and the Swiss Federal Laboratories for Materials Science and Technology (Empa). He heads the Laboratory of Sustainability Robotics at Empa and EPFL, focusing on innovative mobile robotic systems for environmental applications. His educational background includes a PhD from EPFL and a Mechanical Engineering undergraduate degree from ETH Zurich (2005). Prior to his current appointments, he was a post-doctoral researcher at Harvard University. Prof. Kovac's research centers on robotics for sustainability, particularly in robot design , hardware development , and multi-modal sensor mobility . His work enables autonomous systems to operate in complex natural environments for distributed sensing and manufacturing. He is recognized for advancing the integration of robotics into ecological monitoring and sustainable infrastructure. His publications and presentations reflect strong activity in the robotics community, with over 100 peer-reviewed papers and more than 100 invited and keynote lectures. The body of work indicates deep engagement in mobile robotics, environmental robotics, and autonomous system design, forming a cohesive research trajectory in sustainability-focused AI and robotics. Best Paper Awards Prof. Kovac advises government, investment funds, and industry on robotics opportunities, indicating significant impact beyond academia. He has not advised any named students in the provided text, nor are specific grants mentioned, but his leadership of a major robotics laboratory suggests active supervision and funding acquisition. He leads the Laboratory of Sustainability Robotics, a multidisciplinary team focused on developing novel mobile robots for real-world environmental challenges, operating across Empa and EPFL.
Prof. Jamie Paik is a Full Professor at the Swiss Federal Institute of Technology (EPFL), where she serves as Director of the Reconfigurable Robotics Lab (RRL) and is a core member of the Swiss NCCR robotics group. She holds multiple academic appointments across EPFL's School of Engineering, including positions in the Institute of Mechanical Engineering (IGM), STI-SMT SMT-ENS, and STI-SGM SGM-ENS. Additionally, she serves as a PhD program committee member for the Doctoral Program in Robotics, Control and Intelligent Systems. Her research focuses primarily on soft robotics , origami-inspired robotics , and wearable technologies . Prof. Paik's work leverages multi-material fabrication and smart material actuation to develop novel robotic designs that push the physical limits of materials and mechanisms. Her current research includes self-morphing Robogami (robotic origami) that transforms from planar shapes to 2D or 3D structures through predefined folding patterns, similar to traditional paper origami. Prof. Paik has published extensively on robotic origami, haptic feedback systems, and soft actuators, with her most recent work (2024-2025) focusing on vibration of soft twisted beams for locomotion, semi-autonomous surgical assistance, plug-and-play pneumatic systems, and data-driven kinematic modeling. Her research demonstrates a clear trajectory toward more adaptive, reconfigurable robotic systems with applications in surgery, human-robot interaction, and wearable technologies. Among her notable achievements, she developed a 7-DoF humanoid arm during her PhD at Seoul National University (sponsored by Samsung Electronics), which was the lightest in literature at that time (3.7kg including the 8-DoF hand). During postdoctoral work at Pierre Marie Curie University, she developed the internationally patented JAiMY laparoscopic tools now commercialized by Endocontrol-medical.com. PhD: Seoul National University (Humanoid Arm and Hand Design) Postdoc: Institut des Systems Intelligents et de Robotic, Université Pierre Marie Curie Postdoc: Harvard University's Microrobotics Laboratory Prof. Paik has supervised numerous PhD students, both current and past, including Bakir Alihan, Demirtas Serhat, Jiang Shaopeng, Kanno Ryo, Schüssler Alexander Michael, and Wang Ziqiao. Her teaching includes Topics in Autonomous Robotics, Mechanical Product Design and Development, and Advanced Design for Sustainable Future.
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.
Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.
Giovanni Iacca is an Associate Professor at the Department of Information Engineering and Computer Science (DISI) of the University of Trento, Italy, where he leads the Distributed Intelligence and Optimization Lab (DIOL). He serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. Dr. Iacca has over 15 years of industrial experience in mechatronics and optimization applied to engineering, logistics, and scheduling. Dr. Iacca received his PhD in 2011 from the University of Jyväskylä, Finland, and his MSc in 2006 from the Technical University of Bari, Italy. His academic career includes: 2021-present: Associate Professor, University of Trento 2018-2021: Tenure-track Assistant Professor, University of Trento 2017-2018: Postdoc, RWTH Aachen University, Germany 2013-2016: Postdoc, EPFL and University of Lausanne, Switzerland 2012-2016: Postdoc, INCAS³, The Netherlands Dr. Iacca's research bridges fundamental and applied aspects of artificial intelligence with particular emphasis on evolutionary computation and explainable AI. His work spans machine learning, optimization techniques, distributed systems, and their practical implementations. Recent research directions include federated learning, interpretable reinforcement learning, neural architecture search, and optimization for resource-constrained environments. He teaches courses on Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, Optimization Techniques, and AI in Medicine. His publication record demonstrates a strong trend toward developing transparent and efficient AI systems. Recent papers focus on making complex AI models more interpretable while maintaining performance across diverse domains from healthcare to supply chain management. His work on evolutionary approaches to explainable AI has gained significant recognition in the computational intelligence community. Scientific Awards and Editorial Roles EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) IEEE Senior Member (2023) Associate Editor, Evolutionary Intelligence (2024) Editorial Board Member, Memetic Computing (2024) Associate Editor, IEEE Transactions on Evolutionary Computation (2023) Dr. Iacca has successfully supervised multiple PhD students including Andrea Ferigo, Hyunho Mo, and Leonardo Lucio Custode. His research is supported by various grants and collaborations with industry partners like MyAv. He serves as chair for PPSN 2026 and has organized workshops including the Workshop on Awareness and Consciousness in Artificial Intelligence (ACAI). As leader of the Distributed Intelligence and Optimization Lab (DIOL), Dr. Iacca oversees a research team working at the intersection of evolutionary computation, machine learning, and distributed systems. The lab focuses on developing novel algorithms that balance computational efficiency with interpretability, with applications spanning from embedded systems to large-scale distributed computing environments. Current projects include interpretable reinforcement learning, federated neuroevolution, and optimization for edge computing.
Tomasz Tadeusz Gorecki is a former academic researcher at École Polytechnique Fédérale de Lausanne (EPFL). His research focuses on control systems, power engineering, aerospace automation, and energy efficiency. He contributed to interdisciplinary projects involving smart grids, renewable energy integration, and aerospace propulsion systems. He has published widely in conferences such as IEEE Control Applications (CCA 2015) and IEEE Manchester Powertech (2017), and journals like Applied Sciences-Basel. His work spans technical areas including adaptive control systems, power grid stability, and sustainable building technologies. Gorecki has been affiliated with EPFL units including La3 (12 publications), La, Desl, and Igm. No specific awards or grants are listed in the provided records.
Andrei Atanov is a Researcher at the Laboratoire d'intelligence et d'apprentissage visuels (VILAB) within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) . His research focuses on advanced topics in artificial intelligence, computer vision, and machine learning, with particular emphasis on multimodal learning, vision-language models, and robust generalization. He is affiliated with the Department of Computer Science and contributes to projects exploring innovative solutions for vision tasks using computationally designed sensors and diffusion models. His work often bridges theoretical advancements with practical applications in robotics and generative systems. Key research areas include developing large vision-language models, optimizing vision algorithms for low-sensor environments, and enhancing model robustness through diversification strategies. His publications span topics from 3D data augmentation to uncertainty estimation in deep learning, reflecting a broad yet technically deep expertise in AI fundamentals and applications.
Dr. Tommaso Polonelli is a Lecturer and Postdoctoral Researcher at ETH Zürich's Department of Information Technology and Electrical Engineering, affiliated with the Center for Project-Based Learning (PBL). He leads teaching initiatives and research in IoT systems, energy-efficient electronics, and autonomous technologies. His research focuses on energy-efficient systems, smart sensing, and ultra-low power computing, with applications in UAVs, wind turbine monitoring, and wearable devices. He also serves as a Scientific Advisor at RTDT Laboratories AG, an ETH spin-off. Polonelli holds a PhD (2020) and Master’s degree (2017) in Electronic Engineering from the University of Bologna. He has co-authored over 60 publications and received awards such as the Spark Award (2021), KITE Award finalist (2024), and the Certificate of Achievement for National Scientific Qualification (2023). He is an active IEEE member and contributes to both academia and industry through innovations in sensor technology and predictive maintenance. His work emphasizes interdisciplinary collaboration, with projects ranging from structural health monitoring to AI-enhanced systems. Recent efforts include Aerosense—a MEMS-based monitoring system for wind turbines—and ElectraSight, a smart eyewear platform with non-invasive eye tracking.
Dr. Dimitar Petrov is an Associate Professor in Computer Science at Ca’ Foscari University of Venice, specializing in static analysis and cybersecurity. He is affiliated with ETH Zürich's Institute of Pharmaceutical Sciences (IPW) as staff under Tit.-Prof. Jörg Scheuermann. His research focuses on applying abstract interpretation-based methods to detect security vulnerabilities in systems ranging from blockchain smart contracts to IoT devices. Education details are not explicitly provided in the text, but his extensive publication history indicates advanced expertise in formal methods. His research interests include software verification, privacy enforcement, and the application of static analysis tools like LiSA across diverse domains such as robotics, microservices, and mobile applications. Key research trends in his articles emphasize blockchain security (smart contract vulnerabilities, consensus protocols), IoT/IoMT security (device interactions, privacy policies), and the integration of machine learning with program analysis. His work bridges academic research with industry challenges, addressing compliance with regulations like GDPR and the EU Data Act. Prior to his current roles, he has contributed to open-source frameworks like LiSA and collaborated on projects involving automated policy extraction, cross-language analysis, and vulnerability detection in automotive systems. His research group at Ca’ Foscari actively engages in both theoretical advancements and practical tool development. Labs/Teams: Part of the Software and System Verification group at Ca’ Foscari, and collaborates with ETH Zürich's Institute of Pharmaceutical Sciences on interdisciplinary projects combining formal methods with healthcare technology.
Joyce Chai is a prominent academic researcher in computational linguistics and AI with extensive contributions to grounded language learning, embodied agents, and human-machine collaboration. Her work spans vision-language models, theory of mind implementation, and task guidance systems. Key research themes: Language grounding in physical/social contexts Embodied AI and situated reasoning Interactive learning frameworks Zero-shot and continual concept acquisition Recent publications demonstrate her leadership in: Developing TRAVER for coding tutoring agents Creating W2W grounded language model Advancing theory of mind evaluation in LLMs Establishing HAR reasoning strategies for coherent physical reasoning She has mentored numerous students including Ziqiao Ma, Shane Storks, and Yuwei Bao. Her work appears in top venues like ACL, EMNLP, and NAACL with focus on practical applications like cake-making guidance systems (WTaG) and autonomous driving dialogue (DOROTHIE). Technical contributions include: MetaReVision retrieval-enhanced meta-learning EpiCA network for compositional concept recognition Neuro-symbolic DANLI agent architecture Pragmatic Rational Speaker framework
