Prof. Dr. Roland Büchi is a Professor at the School of Engineering, Zurich University of Applied Sciences (ZHAW), specializing in control systems engineering and applied AI. His affiliation includes leadership roles in research projects such as the ongoing 'Digital Bridge to Computer Science' initiative. With a career spanning decades, he maintains active research output and industrial collaborations. Research Interests: Büchi focuses on control systems optimization , particularly PID controller tuning using AI methods, system identification, and applications in robotics and drone technology. His work bridges theoretical control theory with practical implementations in mechatronics. Recent efforts explore machine learning for hysteresis modeling and swarm optimization for control systems. Publication Trends: His 15 most recent works (2018-2024) emphasize AI-driven control optimization , drone technology advancements, and engineering education. Dominant themes include PID parameter tuning for time-delayed systems, drone telemetry, and adaptive learning algorithms. A notable shift toward AI/ML applications in control engineering emerged post-2020. Labs and Teams: Büchi collaborates with researchers like Lukas Gruber and has historical ties to ETH Zurich’s robotics projects. His work involves experimental validation at ZHAW’s engineering facilities, with patents in turbocharger magnetic bearing systems.
Didier Trono is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Laboratory of Virology and Genetics (LVG) within the School of Life Sciences. Formerly, from 2004 to 2012, he served as the founding dean of EPFL's Faculty of Life Sciences, orchestrating its development and growth during this formative period. Trono received his medical education at the University of Geneva, followed by clinical training in pathology, internal medicine, and infectious diseases in Geneva and at Massachusetts General Hospital in Boston. His scientific career began at the Whitehead Institute of MIT, and in 1990 he was recruited by the Salk Institute of San Diego to launch an AIDS research center. After seven years in the United States, he returned to Europe and eventually joined EPFL. Dr. Trono's research has evolved significantly over his career. Initially focusing on virus-host interactions, he studied pathogens like HIV and Hepatitis B virus, creating HIV-derived genetic transfer tools that are now successfully used in gene therapy. For approximately the last fifteen years, his research has centered on epigenetics, particularly exploring the impact of retroelements and their control mechanisms on development and physiology of higher organisms, including humans. His laboratory investigates how transposable elements and KRAB zinc finger proteins regulate gene expression, with important implications for understanding cancer biology and developing new diagnostic and therapeutic approaches. Analysis of his recent publications (2022-2024) reveals a strong focus on transposable elements, KRAB zinc finger proteins, and their roles in gene regulation and cancer. His work combines molecular biology, genomics, and bioinformatics approaches to understand how these ancient viral remnants have been co-opted by the host genome to regulate development and cellular functions. The research spans basic molecular mechanisms to potential clinical applications in cancer diagnosis and therapy, with some recent work also addressing SARS-CoV-2 and immune responses. Throughout his career, Professor Trono has mentored numerous PhD students, including Bojkowska Karolina, Brandão Sanches Vong Martins Filipe Amândio, Bulliard Yannick, Coluccio Andrea, Corsinotti Andrea, Coudray Alexandre, De Tribolet-Hardy Jonas Caspar, and Dorschel Iris Arianna. His laboratory has received significant funding to support research at the intersection of virology, genetics, and epigenetics, contributing to EPFL's reputation as a leading institution in life sciences research. The Trono Laboratory continues to be at the forefront of research on retroelements and their regulatory mechanisms, maintaining a vibrant research environment that bridges fundamental biological questions with potential medical applications, particularly in cancer research and precision medicine.
Mathieu Salzmann is a Senior Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) in the School of Computer and Communication Sciences (IC). He also holds a courtesy appointment with the EPFL College of Humanities and serves as Deputy Chief Data Scientist at the Swiss Data Science Center (SDSC). He has held concurrent roles in teaching units including SIN, SODH, and SSC, reflecting his interdisciplinary engagement. His research focuses on the intersection of machine learning and computer vision, particularly in deep learning for 2D and 3D visual scene understanding, efficient and robust models, domain adaptation, and interpretable AI. These interests are evident across his extensive publication record in top-tier venues. His recent publications (2023–2024) show a consistent trend in advancing deep learning methods for visual recognition, with strong representation at CVPR, ICCV, ECCV, ICML, ICLR, and NeurIPS. Topics include domain generalization, 3D understanding, model robustness, and multimodal learning, often with applications in real-world systems. His editorial roles as Associate Editor for IEEE TPAMI and Action Editor for TMLR further highlight his leadership in the field. Area Chair: ICML 2023, CVPR 2023, ICCV 2023, NeurIPS 2023, AAAI 2024, ECCV 2024 Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Action Editor: Transactions on Machine Learning Research (TMLR) Mathieu Salzmann has supervised numerous PhD students at EPFL, both current and past, including Bouquet Yann Yanis, Javed Saqib, Li Shuangqi, and others. He has also been involved in research grants and collaborative projects, such as his work with S. Süsstrunk and R. Baroni on comics reconfiguration. His part-time role as Senior GNC Engineer at ClearSpace (2020–2024) illustrates his applied research engagement in aerospace systems. He is actively involved in EPFL’s data science and AI research ecosystem through SDSC and multiple labs.
Silvestro Micera is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) and holds the Bertarelli Foundation Chair in Translational Neuroengineering. He directs the Translational Neural Engineering Laboratory and teaches courses including Neural signals and signal processing and Translational neuroengineering . His research bridges neural interfaces, robotics, and neuroprosthetics to restore motor functions in spinal cord injuries, stroke, and amputations. Micera's research integrates implantable neural interfaces, robotic rehabilitation, and hybrid neuro-prosthetic systems. Key focus areas include: Robotic neurorehabilitation for mobility restoration Neural control mechanisms in movement CNS/PNS neural interface development Bioelectronic modulation for sensory feedback His recent publications emphasize machine learning-driven motor recovery prediction, closed-loop sensory feedback systems, and minimally invasive neuroprosthetics. Trends include AI-optimized stimulation protocols, multimodal data fusion for rehabilitation, and clinical translation of neural bypass technologies. Awards: IEEE EMBS Early Career Achievement Award (2009) IEEE EMBS Technical Achievement Award (2021) Micera leads EU-funded projects such as TIME, CLONS, and NeuWalk, focusing on neural prostheses. He advises 8 current and 18 former PhD students in neuroengineering. His lab collaborates with MIT, Harvard, and industry partners (e.g., Plexon) to advance translational neurotechnologies.
Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.
John Lygeros is a Full Professor and Head of the Institute for Automatic Control at ETH Zurich's Department of Information Technology and Electrical Engineering. He received his B.Eng. (1990) and M.Sc. (1991) from Imperial College London, and Ph.D. (1996) from UC Berkeley. Before joining ETH Zurich in 2006, he held academic positions at the University of Cambridge and University of Patras. His research focuses on: Modeling and control of hierarchical hybrid systems Large-scale dynamical systems applied to biochemical networks Control of automated transportation systems via wireless networks Energy systems and advanced manufacturing control His research demonstrates strong emphasis on optimization methods, distributed control architectures, and machine learning applications in control systems, with significant contributions to real-time optimization algorithms and data-driven control methodologies. Major scientific awards include: HSCC Test of Time Award (2024) ERC Advanced Grant (2018) O. Hugo Schuck Best Paper Award (2018) IEEE George S. Axelby Paper Award (2016) Three Golden Owl teaching awards from ETH Zurich He leads the Automatic Control Laboratory at ETH Zurich and serves as Director of the National Centre of Competence in Research 'Dependable Ubiquitous Automation'. He has advised over 60 doctoral candidates since 2017, with research spanning optimization algorithms, power systems, autonomous systems, and machine learning applications.
Prof. Raffaello D'Andrea is a Full Professor at ETH Zürich's Department of Mechanical and Process Engineering, affiliated with the Institute for Dynamic Systems and Control. His research focuses on bridging digital and physical worlds through robotics, control systems, and autonomous systems. He has pioneered work in aerial robotics, swarm systems, tactile sensing, and soft robotics. His philosophy emphasizes solving 'easy' problems with scalable, robust solutions, prioritizing simplicity and replicability. Key research areas include UAV navigation, distributed control, tactile sensor design, and fault-tolerant systems. He has founded multiple organizations and led roles as CTO/CEO, emphasizing cross-disciplinary innovation. His work has commercial applications in logistics, healthcare, and automation, driven by a belief in technology's role in improving human life. Notable projects include the Cubli robotic cube, aerial vehicle swarms, and optical tactile sensors for robotics. His lab emphasizes collaboration and team leadership, aiming to translate theoretical insights into practical, scalable technologies. Scientific awards: None explicitly listed in the provided texts. Advising and grants: No students listed in the provided texts; grants information not detailed. Labs/Teams: Leads research at ETH Zurich's Institute for Dynamic Systems and Control, collaborating with industry and academic partners globally.
Verena Zimmermann serves as Assistant Professor (Tenure Track) for Security, Privacy and Society at ETH Zurich within the Department of Humanities, Social and Political Sciences. She leads the Security, Privacy and Society research group, focusing on human-centered approaches to safety, IT security, and privacy challenges in emerging technologies. Her academic foundation includes a PhD in Usable Security from TU Darmstadt, where her dissertation From the Quest to Replace Passwords towards Supporting Secure and Usable Password Creation earned dual awards from the German Association for Data Protection and Data Security (GDD) and the Ernst-Ludwigs-Hochschulgesellschaft. Zimmermann's research pioneers the shift from viewing humans as security vulnerabilities to recognizing them as integral solutions. Her work spans password security, phishing interventions, smart home privacy, and cybersecurity education, consistently applying human factors principles to design systems that align with natural user behavior. Current projects explore emotional dimensions of security engagement, quantum governance frameworks, and LLM-related privacy concerns through interdisciplinary lenses combining computer science, psychology, and safety science. Recent publications reveal strong thematic trends toward understanding user motivations in security behavior, developing personalized training through augmented reality and adaptive systems, and translating safety science principles to cybersecurity incident management. Her work increasingly addresses societal implications of emerging technologies like quantum computing and generative AI. Her distinguished recognitions include: GDD Dissertation Award for Usable Security research Ernst-Ludwigs-Hochschulgesellschaft Dissertation Award IANUS Award (2nd place) for Moving from a 'Human-as-Problem' to a 'Human-as-Solution' Cybersecurity Mindset Zimmermann actively mentors through ETH's Human-Centered Security and Privacy Lab while collaborating internationally with institutions including ATHENE (German National Center for Applied Cybersecurity), Abertay University's Cybersecurity Division, and the Safety Science Innovation Lab. Her research receives support from Swiss and European funding bodies focused on privacy-preserving technologies and organizational security behavior. At ETH Zurich, she directs the Security, Privacy and Society group which employs design thinking, LEGO Serious Play, and mixed-methods research to tackle real-world security challenges. The team partners with industry stakeholders to develop practical solutions for phishing resistance, smart home privacy, and cybersecurity education while maintaining strong ties to the Research Training Group 2050 Privacy and Trust for Mobile Users.
Dr. Robert Huber serves as a Lecturer at the Department of Environmental Systems Science at ETH Zürich, specializing in Agricultural Economics and Policy. His research focuses on the integration of interdisciplinary knowledge to analyze and evaluate management and policy options that promote sustainable development in the agricultural sector. Institution: ETH Zürich Department: Agricultural Economics and Policy Research Focus: Sustainable Agriculture and Policy Methodologies: Bio-economic and agent-based modeling Dr. Huber's research explores how multifunctional agricultural sectors can provide essential non-market goods and services while maintaining their primary economic function of food production. His work applies bio-economic and agent-based modeling techniques to understand farmers' decision-making processes regarding ecosystem services provision, coupled with economic valuation techniques to analyze societal demand for these services. His research aims to identify effective and efficient policy measures that align supply and demand for ecosystem services at landscape scale. His recent publications demonstrate strong expertise in agricultural policy analysis, climate change mitigation strategies, sustainable farming practices, and innovative modeling approaches. His work spans topics from herbicide-free agriculture using remote sensing technologies to biodiversity enhancement in agricultural landscapes and the cost-effectiveness of different climate change mitigation policies at various scales. Remote sensing applications in sustainable agriculture Behavioral aspects of farmer decision-making Policy evaluation methodologies Climate change adaptation in farming systems Economic valuation of ecosystem services Digitalization impacts on agricultural policy Dr. Huber has published extensively in high-impact journals including Journal of Agricultural Economics, People and Nature, and Q Open. His collaborative research often involves interdisciplinary teams addressing complex agricultural sustainability challenges. His publications demonstrate a consistent focus on practical policy implications derived from rigorous empirical analysis.
Prof. Eleni Chatzi is a Full Professor and Chair of Structural Mechanics at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. She holds a PhD from Columbia University (2010) and has held roles from Assistant to Full Professor at ETH since 2010. Her research focuses on intelligent structural monitoring and data-driven asset management, emphasizing nonlinear dynamics and sensor integration. Affiliations : Institute of Structural Engineering, European Academy of Wind Energy (EAWE President), Swiss Community for Computational Methods (SWICCOMAS Chair) Research interests include Structural Health Monitoring (SHM), system identification, and advanced simulation tools. She pioneered work on data-driven diagnostics and self-aware infrastructure, supported by grants like the ERC Starting Grant (2015). Awards include the 2020 Walter L. Huber Prize and 2024 SHM Person of the Year Award. Her work spans wind energy infrastructure, metamaterials for vibration control, and AI-driven structural analytics. Over 600 publications and 200k+ citations highlight her impact. She teaches computational science and structural dynamics in ETH's programs and collaborates globally on sustainable infrastructure projects.
Alexander Mathis is an Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Brain Mind Institute (School of Life Sciences). His research bridges computational neuroscience and machine learning to decode sensorimotor behaviors and develop AI tools for behavioral analysis. Pure Mathematics MSc, Ludwig Maximilians University Munich PhD in Computational Neuroscience, Ludwig Maximilians University Munich His work focuses on understanding how the brain generates behavior through computational models and algorithms. Key contributions include the DeepLabCut toolbox, hBehaveMAE , and other frameworks for pose estimation, action segmentation, and brain-inspired AI. His group also explores proprioception, motor control, and neural coding theories. Recent publications include unsupervised hierarchical behavior modeling via masked autoencoders (ECCV 2024), synthetic basketball benchmarks (Shot7M2), and extensions of BABEL into hBABEL. These works highlight his interest in temporal hierarchies in behavior and scalable AI solutions for neuroscience. Scientific Honors Robert Bing Prize (2024) Eric Kandel Young Neuroscientists Prize (2023) Frontiers of Science Award (2023) Marie Skłodowska-Curie Postdoctoral Fellowship Studienstiftung des deutschen Volkes scholarship He advises PhD candidates in neuroscience and life sciences, leads courses on brain-like computation and software engineering for life sciences , and collaborates across AI4Science initiatives. His group actively participates in competitions, such as NeurIPS' MyoChallenge, where brain-inspired reinforcement learning algorithms have won awards.
Giacomo Indiveri is a dual Professor at the Faculty of Science of the University of Zurich and the Department of Information Technology and Electrical Engineering of ETH Zurich . He serves as the Director of the Institute of Neuroinformatics at both institutions. Indiveri holds an M.Sc. in Electrical Engineering (1992) from the University of Genoa and a Ph.D. in Computer Science (2004) from the same university. Primary Affiliation: University of Zurich (Faculty of Science, Institute of Neuroinformatics) Secondary Affiliation: ETH Zurich (Department of Information Technology and Electrical Engineering) Indiveri's research bridges neuroscience , computer science , and machine learning to develop neuromorphic cognitive systems . His work focuses on spike-based learning , recurrent neural networks , and analog/digital circuit design for real-time sensory-motor systems . He integrates emerging memory technologies into fault-tolerant event-based architectures, enabling brain-inspired computing paradigms in applications like robotics and medical monitoring. His recent publications emphasize neuromorphic hardware for epileptic seizure detection , spiking neural networks in robotic painting , and scalable processors with on-chip learning . These works explore biologically plausible neurons , delay lines , and memory arrays for temporal processing, with applications in healthcare , edge computing , and adaptive control . Scientific Awards & Recognitions: 2021 IEEE Biomedical Circuits and Systems Best Paper Award Senior Member of IEEE Society ERC Fellow with three European Research Council grants Indiveri's group at the Institute of Neuroinformatics develops event-based systems for real-world validation of brain-inspired computing. His work includes multi-core processors , feedback optimizers , and dynamic routing architectures , supported by grants for advancing neuromorphic technologies .
Josie Hughes is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) within the School of Engineering's Institute of Mechanical Engineering. She leads the Computer Robot Design and Fabrication Laboratory (CREATE Lab) and serves on the doctoral program committee for Robotics, Control and Intelligent Systems. Her roles include teaching courses in product development, engineering design, and data-driven manufacturing methods while maintaining active research and student supervision. Her research centers on soft robotics with emphasis on adaptive design, fabrication techniques, and real-world applications. Key areas include agricultural robotics (exemplified by the GraspBerry raspberry harvester), biomimetic materials like self-healing e-skins, and developmental robotics (BabyBot project). She pioneers approaches integrating morphological computation, variable stiffness mechanisms, and machine learning for robotic control, while championing open-source principles and diversity in robotics through accessible educational initiatives like balloon robot kits. Analysis of her recent publications reveals dominant trends in soft robotics adaptability, particularly in variable-stiffness structures, sensor integration, and task-specific optimization. Her work bridges fundamental material science with practical applications in agriculture, food science, and human-robot interaction, frequently employing computational methods like Bayesian optimization and neural networks for design and control. Scientific awards: No specific awards were mentioned in the source materials. Dr. Hughes actively supervises 16 doctoral students across diverse projects including soft grippers for agriculture, biomimetic locomotion, and robotic manipulation systems. Her advising portfolio shows strong alignment with her research themes, with current students working on topics like modular soft arms, agricultural automation, and developmental robotics. While grant details weren't specified, her high publication volume indicates robust research funding. The CREATE Lab under her leadership focuses on holistic co-design of hardware and software for soft robotic systems, with notable projects including the GraspBerry agricultural harvester and BabyBot developmental platform. The lab emphasizes open-source development and educational outreach, maintaining strong industry and academic collaborations while pushing boundaries in reconfigurable robotics and human-centered applications.
Bradley Nelson is a Full Professor of Robotics and Intelligent Systems at ETH Zürich since 2002, leading the Institute for Robotics and Intelligent Systems. He holds a Ph.D. in Robotics from Carnegie Mellon University (1995), with prior roles as Assistant Professor at the University of Illinois at Chicago (1995-1998) and Associate Professor at the University of Minnesota (1998-2002). His research focuses on microrobotics and nanorobotics for biomedical applications, including targeted drug delivery and medical robotics. He leads the Microrobotics Lab (MSRL) and chairs international workshops/conferences. Awards include IEEE and ASME Fellowships, and Best Paper accolades at major robotics venues. Research interests emphasize magnetic microrobot navigation, smart materials, and clinical translation of robotic systems. His work bridges engineering and medicine, addressing challenges like cerebral vasculature navigation for drug delivery. He has developed electromagnetically controllable catheters and telesurgery frameworks. Leadership roles include Head of the ETH Department of Mechanical and Process Engineering and Chairman of the ETH Electron Microscopy Center (EMEZ). Notable contributions include magnetically guided microcatheters, variable-stiffness catheters, and clinical-ready navigation systems. Ongoing projects explore magnetoelectric effects and biodegradable micromotors for environmental and biomedical uses. His lab collaborates with industry and academic partners globally to advance robotic solutions for healthcare challenges.
Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.
