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Paolo FavaroView profile

Professor

• Computer Vision
• Computational Photography
• Machine Learning
• +4 more

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.

• Computational Imaging
• X-ray Imaging
• Optical Physics
• +4 more

Manuel Guizar-Sicairos is an Associate Professor of Physics at École Polytechnique Fédérale de Lausanne (EPF Lausanne), affiliated with the School of Basic Sciences (SB) and the Institute of Physics (IPHYS). He leads the Computational X-ray Imaging group at the Paul Scherrer Institute (PSI) in a joint position with EPF Lausanne since January 2023. His work bridges advanced X-ray imaging techniques and algorithm development for synchrotron sources. Education B.Sc. in Physics Engineering (2002), Tecnológico de Monterrey, Mexico M.Sc. in Electronic Systems (2005), Tecnológico de Monterrey, Mexico M.Sc. in Optics (2008), Ph.D. in Optics (2010), Institute of Optics, University of Rochester, NY His research focuses on computational X-ray imaging , particularly phase retrieval, holography, and ptychography. He has pioneered 3D nanoscale imaging techniques like SASTT and magnetization vector tomography, enabling applications in biomedical imaging (synaptic-resolution brain imaging), energy storage (3D operando batteries), and materials science (catalysts, bone structures). Selected publications since 2015 highlight his expertise in ptychography , nanotomography , and scattering techniques , with a recurring emphasis on synchrotron-based imaging and reconstruction algorithms . His work often intersects with materials science and semiconductor analysis . Scientific Awards Innovation Award on Synchrotron Radiation (2014, 2021) ICO Prize (2019) Fellow of The Optical Society (2021) and SPIE (2022) SPIE Senior Member Multiple Optics & Photonics Education Scholarships (2004–2009) As a confidential advisor for the Respect@PSI campaign since 2022, he advocates for diversity and inclusion in research. His group at PSI develops open-source algorithms for X-ray imaging, including quasi-discrete Hankel transforms and subpixel registration tools.

Yohei SatoView profile

Lecturer

• Computational Fluid Dynamics
• Multiphase Flow
• Heat Transfer
• +3 more

Yohei Sato is a Senior Scientist at the Paul Scherrer Institute (PSI) and a Lecturer in Computational Multiphase Thermal Fluid Dynamics at ETH Zurich. He holds a B.S. (1995), M.S. (1999), and Ph.D. (2006) from the University of Tokyo. His research focuses on Computational Fluid Dynamics (CFD), particularly free-surface flows and phase-change phenomena, with applications in boiling heat transfer, multiphase flows, and thermal-fluid systems. He leads projects like BRAVA (CFD code development) and PSI-BOIL (unstructured CFD code), and has contributed to the T-Flows and DEBORA benchmarks. His work bridges academic research (ETHZ lectures) and industrial applications (PSI innovations). Notable contributions include numerical methods for quenching processes, heat transfer modeling in film boiling, and microgravity-based protein crystallography using acoustic levitation. Education: B.S., University of Tokyo, 1991–1995 M.S., University of Tokyo, 1997–1999 Ph.D., University of Tokyo, 2003–2006 (part-time) Research Interests: Multiphase flow dynamics, boiling heat transfer mechanisms, CFD model validation, and advanced numerical methods for phase-change phenomena. His work emphasizes practical applications in energy systems (e.g., nuclear thermal-hydraulics) and material processing (e.g., quenching). Recent Contributions: Published widely on film boiling heat transfer, transient conjugate heat transfer, and CFD simulations of high-pressure boiling flows. Collaborates internationally on benchmarks like DEBORA and develops open-source tools like PSI-BOIL for industrial and academic use.

• Human Motion Prediction
• Computer Vision
• Multi-person Interaction Analysis
• +3 more

Dr. Wen Guo is a Researcher at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich, affiliated with the Professorship for Sensory-Motor Systems. Their work focuses on advancing computer vision, robotics, and biomedical engineering through innovative machine learning techniques. Key research interests include human motion prediction, multi-person pose estimation, and gesture generation in complex interactions. Wen Guo has contributed to high-impact projects such as the UVO Challenge 2021, securing first place in both video-based and image-based open-world segmentation categories. Their research emphasizes real-world applications in safety-critical systems, crowd behavior modeling, and human-robot interaction. Notable methodologies include hierarchical transformer dynamical VAEs for motion generation and pose-interacting networks for 3D pose estimation. Academic contributions span from foundational work in motion prediction to applied solutions in computer vision challenges. Their lab integrates interdisciplinary approaches, combining deep learning with biomechanical insights to tackle challenges in healthcare and robotics.

Benjamin BlackView profile

Lecturer

• Socio-ecological Systems
• Land Use Change Modeling
• Protected Area Effectiveness
• +3 more

Benjamin Black is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich, focusing on simulation modeling of socio-ecological systems and land use change. Research explores: Land use change modeling calibration techniques Protected area effectiveness assessment Participatory methods for environmental decision-making Uncertainty quantification in predictive models Ecological infrastructure valuation Key publications develop novel approaches for: Validating transition potential predictions in cellular automata models Counterfactual assessment of protected area impacts using propensity score matching Participatory Bayesian networks for policy intervention design Research contributes to the VALPAR.CH project assessing ecological infrastructure values in Swiss parks.

Arnor ElvarssonView profile

Lecturer

• Infrastructure planning
• Uncertainty modelling
• Strategic decision-making
• +4 more

Arnór Elvarsson is a Lecturer at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Institute of Construction and Infrastructure Management (IBI). He holds a PhD from ETH Zurich (2021) and an MSc in Spatial Development & Infrastructure Systems (2018), for which he received the Culmann Award. His research focuses on infrastructure planning under uncertainty, strategic decision-making, and adaptive planning methods to enhance societal responsiveness. He has professional experience as a Project Engineer at Infrastructure Management Consultants and a Specialist at EFLA Engineers. Education: BSc in Civil & Environmental Engineering from the University of Iceland (2014), MSc from ETH Zurich (2018), and ongoing PhD at ETH Zurich. His work integrates digital tools and uncertainty modeling to improve long-term infrastructure planning. Key projects include exploring adaptive mobility infrastructure, responsiveness in planning processes, and automated vehicle impacts. Research Interests: Infrastructure planning methodologies, transport demand modeling, decision-making under uncertainty, and the interplay between land-use and mobility. He teaches courses on infrastructure optimization tools and interdisciplinary project activities. Awards include the 2018 Culmann Award for his Master's thesis. Publications span topics like probabilistic pavement forecasts, automated vehicle deployment strategies, and infrastructure responsiveness. He advises students on Master's and Bachelor's theses and contributes to grants focused on transport infrastructure development and uncertainty modeling. Labs/Teams: Active in ETH Zurich's Infrastructure Management group, focusing on adaptive planning and uncertainty analysis. Collaborates on interdisciplinary projects with industry partners like BIM4AMS and MAPFalke.

Robert J. FlattView profile

Professor

• Digital Fabrication
• Concrete Technology
• Material Science
• +5 more

Prof. Robert J. Flatt is a Full Professor at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, and Deputy Head of the Institute for Building Materials. His research focuses on digital fabrication with concrete (Digital Concrete), extended reality (XR) for built heritage preservation, and concrete admixture science. He leads Design++, a center integrating AI, ML, and XR to reduce the environmental footprint of architecture, engineering, and construction (AEC). Flatt earned his PhD from EPFL (1999), conducted postdoctoral research at Princeton University, and previously led inorganic materials research at Sika Technology AG. Research interests span material science for heritage preservation, digital casting systems (e.g., 3D extrusion printing), and low-carbon cement development. His work emphasizes sustainability, interdisciplinary collaboration, and practical applications in construction. Notable contributions include the Mesh Mould technology and international conferences on digital concrete. Key awards include the Stephen Brunauer Award (twice), the RILEM Robert L Hermite Medal, and multiple Swiss and international technology awards. Flatt’s projects often involve industry partnerships, such as the Design++ initiative with 27 professorships and seven departments. Advising and grants: Leads interdisciplinary teams in ETH Zurich and collaborates with global industry partners. Current projects include demonstrators for digital fabrication at architectural scales and AI-driven design optimization. His labs include the Institute for Building Materials and the Design++ Center.

• Food Science
• Soft Materials Science
• Colloids and Interfaces
• +4 more

Jiaqi Su is affiliated with ETH Zürich as part of the Professorship for Food and Soft Materials Science. Their research focuses on the development of advanced materials for food systems, including gels, emulsions, and packaging solutions. The position emphasizes applied and fundamental studies in biomaterials and soft matter. Research interests include: Polysaccharide-protein complexation mechanisms Encapsulation strategies for bioactive compounds (e.g., curcumin, DHA) Thermal and structural stability of food systems Catalytic materials derived from amyloid fibrils Biodegradable food packaging alternatives High-pressure processing effects on emulsions Recent publications (2025–2023) highlight work in food science and materials science, particularly in emulsion stabilization, gel formation, and catalytic systems for CO2 conversion. Their studies often combine experimental methods with theoretical modeling to optimize material performance. No scientific awards or grants are explicitly mentioned. They currently have no listed advisees/PhD/Master’s students.

• Neurorehabilitation
• Movement Disorders
• Neuroscience
• +3 more

Lena Salzmann is a Lecturer at the Department of Health Sciences and Technology at ETH Zürich, Switzerland. Her primary affiliation is within the Rehabilitation Engineering group. She specializes in neurorehabilitation, movement disorders, and biomedical engineering. Her research focuses on integrating neurophysiological insights with clinical applications, particularly in Parkinson’s disease, gait analysis, and neurofeedback systems. Her academic work addresses interdisciplinary challenges in neuroscience and engineering, including electrocortical activity during movement adaptability, neuro-cognitive effects of sensory degradation, and precision rehabilitation strategies. She explores cutting-edge technologies such as functional near-infrared spectroscopy and implanted deep brain stimulation systems. No scientific awards are explicitly mentioned in the provided text. Her research has been published in high-impact journals, with recent studies focusing on Parkinson’s disease pathophysiology and neurotechnology applications. Lena Salzmann’s professional activities involve advancing rehabilitation engineering through collaborative projects in motor control, sensory perception, and clinical neurotechnology. She contributes to ETH Zürich’s interdisciplinary research ecosystem, bridging gaps between basic neuroscience and clinical interventions.

Maliki MoustaphaView profile

Lecturer

• Structural Engineering
• Uncertainty Quantification
• Reliability Analysis
• +3 more

Dr. Maliki Moustapha is a Lecturer at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Chair of Risk, Safety and Uncertainty Quantification. He holds a master's degree in structural engineering from the French Institute for Advanced Mechanics (IFMA) and a PhD from Clermont Université and ETH Zurich, focusing on reliable lightweight automotive design under impact conditions. His current work involves advancing reliability-based design optimization techniques for the UQLab software. His research interests span uncertainty quantification, reliability analysis, surrogate modeling, and sustainable building renovation strategies. He has contributed to methodologies addressing stochastic simulators, noise-aware algorithms, and robust design under budget constraints. His work bridges engineering systems and environmental sustainability, with a focus on automotive structures and civil infrastructure resilience. Dr. Moustapha has published extensively on topics including active learning for reliability analysis, stochastic emulators, and cost-effective renovation strategies for Swiss residential buildings. His recent articles emphasize probabilistic modeling, surrogate-based optimization, and the integration of environmental and economic performance metrics in design decisions. Though no scientific awards are explicitly mentioned, his contributions to UQLab's reliability modules reflect his impactful technical leadership. He collaborates on projects involving automotive industry partners (e.g., PSA Peugeot Citroën) and contributes to software development for uncertainty quantification tools.

• History of Computing
• Digital Society
• Technology Governance
• +2 more

David Gugerli is Full Professor for History of Technology at ETH Zurich's Institute for History, examining how technologies shape social orders and knowledge regimes. His research pioneers critical studies of computing infrastructures and digital societies. Research analyzes database epistemologies, supercomputing politics, and surveillance cultures. Major works include 'How the World Got Into the Computer' exploring digital reality emergence and studies of Swiss banking computerization. Methodological innovations combine archival research with science studies principles. Recognized through Rudolf Kellerman Prize and Berlin Brandenburg Academy Prize for electrification studies. International fellowships include Wissenschaftskolleg zu Berlin and Stanford University. Teaches courses on technology governance, historical methodology, and infrastructure studies within ETH's humanities curriculum.

Falk WittelView profile

Lecturer

• Materials Science
• Civil Engineering
• Geomatics
• +3 more

Dr. Falk Wittel is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich, affiliated with the Institute of Construction Materials (IfB). His research focuses on advanced materials science, structural engineering, and geomatics applications. Key areas include wood mechanics, hygroresponsive materials, and granular systems. He explores topics such as creep behavior in orthotropic materials, micro-mechanical testing of biomaterials, and environmental degradation effects on construction materials. His work spans experimental and computational analyses of material properties, including tensile/shear performance of wood tissues, residual stress in adhesives, and rheological modeling of hygroscopic systems. He investigates biomimetic systems like the Venus flytrap’s mechanical response and develops cost-effective testing methodologies for orthotropic materials. Notable contributions include studies on wood densification, ice lensing in sandstone, and fragmentation processes in granular materials. His research bridges fundamental mechanics with practical applications in civil engineering and sustainable construction.

• Computational Social Science
• Democracy Studies
• Sociotechnical Systems
• +5 more

Dr. Elisabeth Stockinger is a Lecturer at ETH Zurich's Department of Humanities, Social and Political Sciences (D-GESS), specializing in Computational Social Science. Her research focuses on sociotechnical systems, democracy dynamics, and misinformation propagation, particularly analyzing how time-of-day patterns and digital tools influence societal behaviors. She has contributed to studies on voting advice applications, pandemic misinformation, and industrial control frameworks like OPC-UA integration in CERN. Her work bridges social science and technology, examining how digital systems shape democratic processes and public discourse. Recent projects include exploring diurnal user activity impacts on misinformation spread and designing trustworthy digital tools for participatory governance. Key Areas: Democracy Studies, Sociotechnical Systems, Misinformation Dynamics Technical Expertise: Data acquisition protocols (OPC-UA), social media analytics Geographic Focus: Europe, with case studies in Italy and broader digital ecosystems

Virginia LangView profile

Lecturer

• Human Factors Engineering
• Usability Engineering
• Medical Device Design
• +3 more

Virginia Lang is a Lecturer at EPFL, affiliated with the EDEE-ENS unit under the VPA-AVP-DLE administrative structure. Her career spans medical research, cognitive psychology, and Human Factors engineering. She holds a PhD in Cognitive-Experimental Psychology from SUNY Binghamton and a Master's in Applied Technology. Her research focuses on human-centered design, usability in medical devices, and product lifecycle management. She founded the HirLan Institute of Human Factors, providing consulting and testing services to pharmaceutical and medical device companies globally. The institute operates labs in Carlsbad, California, and a UK consortium. Virginia's expertise includes user-centric design processes, FDA/CE compliance, and cognitive factors in visual display systems. She has collaborated with NASA and the U.S. Air Force on human-system integration.

Andreas SchlaginhaufenView profile

Researcher

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +3 more

Andreas Schlaginhaufen is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the SYCAMORE Lab under the Institute of Microengineering (IGM) within the School of Engineering (STI). His research focuses on reinforcement learning, safe AI systems, and convex analysis, with a particular emphasis on aligning AI models with human preferences and expert demonstrations. Education: He holds a Bachelor's and Master’s in Electrical Engineering and Information Technology from ETH Zurich, with an exchange semester at the University of Toronto, Canada. He has interned with EWZ Zurich's power grid team and ETH Zurich’s Learning & Adaptive Systems group. Bachelor's in Electrical Engineering (ETH Zurich) Master’s in Electrical Engineering and Information Technology (ETH Zurich) Exchange: University of Toronto, Canada Research Interests: His work spans reinforcement learning, safe reinforcement learning, convex analysis, and learning in games. He explores theoretical and practical aspects of reward shaping, transferability of learned models, and ensuring policy safety in dynamic environments. Awards: He has been awarded the Swiss Data Science Center PhD Fellowship, providing full funding and mentorship during his doctoral studies. Advising and Grants: While currently focused on his doctoral research, his fellowship supports his exploration of inverse reinforcement learning and its applications. He contributes to the SYCAMORE Lab, which investigates multi-agent optimization and systems control. Labs/Teams: He is part of the SYCAMORE Lab at EPFL, which focuses on systems control and multi-agent optimization, advancing theoretical and applied research in AI-driven systems.