Björn Jensen is a Professor and Co-Head of the AI Robotics Research Lab at Lucerne University of Applied Sciences and Arts (HSLU), specifically within the Lucerne School of Computer Science and Information Technology. He also teaches medical robotics at the University of Bern's Biomedical Engineering Program. His professional background includes roles at the Autonomous Systems Lab at EPFL, Switzerland, and founding the startup Singleton 3D focusing on 3D laser measurement technology. Educational background: MSc in Electrical Engineering (Automation & Image Processing) from TU Darmstadt (1998), followed by a Master's in Industrial Management from the same institution. PhD in human-robot interaction from EPFL (2005), with research stints at Tokyo University (2005) and involvement in projects like Robox@Expo.02 and Smarter-Elrob. Research interests span robotics, human-robot interaction, autonomous systems, medical robotics, and sensor-based navigation. Notable projects include the 'Smart Ennoblement Factory', 'NaviMow' autonomous lawnmower, and 'Bagger Assistenzsysteme'. His work emphasizes real-world applications of robotics in dynamic environments and human-centric systems. Lab leadership includes co-directing the AI Robotics Research Lab, focusing on advancing robotics technologies for practical scenarios. No scientific awards explicitly listed, but contributions to industry-academia collaborations are highlighted through startup ventures and applied research projects.
Katharina O. E. Müller is a researcher at the University of Zurich's Institute of Computer Science, affiliated with the Communication Systems Group (CSG). She joined the group in December 2021 to pursue her Doctoral Degree under the supervision of Prof. Dr. Burkhard Stiller. Her research focuses on the Internet of Things (IoT) with an emphasis on Security & Privacy in wireless protocols (UWB, BLE, ZigBee) and Localization techniques. She investigates vulnerabilities through penetration testing and develops frameworks for privacy preservation in emerging IoT technologies. Recent publications highlight her work on Privacy ontologies for Ultra-Wideband networks Integrated data tracking systems for IoT environments These studies address security challenges in protocol stacks and real-time data management.
Dr. Clotaire Michel serves as a Lecturer in Risk Assessment and Risk Management at ZHAW School of Engineering, where he also holds roles as Deputy Programme Director for MAS/DAS/CAS Integrated Risk Management (IRM), Module Manager for Risk Management (Bachelor), and Coordinator for the CAS Risk Assessment program. His academic work spans Technology Assessment in the Master Circular Economy Management program and MSE module coordination. His educational background includes a PhD in Earth Sciences from Université Grenoble Alpes (2004-2007), an Ingénieur Civil des Mines (Engineering Geology) from Ecole Nationale Supérieure des Mines de Nancy (2001-2004), and a Master in Earth Sciences from Université Grenoble Alpes (2003-2004). Continuing education includes CAS Earthquake Engineering (HSLU, 2023) and Habilitation à diriger les recherches (Université Grenoble Alpes, 2017). Michel's research focuses on interdisciplinary risk management, integrating seismic risk assessment, technology evaluation, and climate resilience. His work bridges engineering geology with practical safety applications, particularly in structural safety, fire protection systems, and earthquake engineering. Current projects address circular economy risk frameworks and regulatory compliance in European safety standards. His 15 most recent publications (2020-2025) reveal evolving expertise from traditional seismic site characterization toward broader risk domains including climate adaptation and fire safety. Key trends include methodological advances in ambient vibration analysis for structural monitoring and practical applications of risk assessment frameworks in European regulatory contexts. Professional networks include Verein Risiko und Sicherheit, Netzwerk Risikomanagement, Institut pour la maîtrise des risques (ImdR), Association Française du génie ParaSismique (AFPS), and Schweizer Gesellschaft für Erdbebeningenieurwesen und Baudynamik (SGEB). His industry role as Project Manager/Section Leader at Risk&Safety AG complements his academic work in hazard and risk analysis. As Deputy Programme Director for IRM, Michel oversees curriculum development and program coordination for professional risk management education. His research group within ZHAW's Technology Assessment focus conducts field studies on structural safety systems and develops risk assessment protocols adopted by Swiss safety agencies.
Mauro Prevostini is the Program Manager of the Faculty of Informatics at the Università della Svizzera italiana (USI) since 2004 and holds the academic rank of Lecturer. He previously managed the creation of the Faculty of Informatics from 2001 to 2004 and has been a staff member of the ALaRI institute until 2018. His roles include coordinating academic-industry collaborations and promoting computer science education in local schools. Education: MSc in Electrical Engineering (ETH Zürich, 1994), Thesis: "On-Line Recognition of Masticatory Muscles Activity with Long-Time EMG Recorder" Secondary Education: Liceo Cantonale Lugano 1 (1984–1988) Research Interests: Focuses on Wireless Sensor Networks applied to Precision Agriculture, particularly in pest monitoring systems like PreDiVine DSS. His work integrates UML-based design methodologies for embedded systems and cyber-physical systems. Collaborations include ALaRI and Agroscope research center. Academic Contributions: Over 15 key publications from 2003–2022 emphasize system-level design, hardware/software co-design, and sensor network optimization. Recent articles address deep learning strategies for pest detection and adaptive decision support systems. Professional Activities: Co-founder of Dolphin Engineering Sagl (2012–present), a Precision Agriculture startup Member of the academic senate (2017–2019; 2023–present) Former coordinator of the Ticino branch for the informatica08 initiative (2008) Labs & Projects: Leads projects on wireless sensor networks for agricultural monitoring and collaborates with ALaRI on embedded system design tools and methodologies.
Xu Chen is a doctoral researcher at ETH Zurich specializing in 3D generative models and neural implicit shape animation . His work focuses on creating photo-realistic simulations of human activity for applications in human-centric perception tasks .
Siqi Zhang is a Tenure-Track Assistant Professor in the Department of Industrial Engineering and Operations Management (IEOM) at the School of Management and Engineering (SME) of Nanjing University (NJU). Holding a Ph.D. in Operations Research from the University of Illinois at Urbana-Champaign (UIUC) and postdoctoral experience at Johns Hopkins University (JHU), their research bridges optimization theory with machine learning applications. Ph.D. from UIUC's Industrial and Enterprise Systems Engineering (ISE) Department (2017-2022) Postdoctoral Fellow at JHU's Applied Mathematics and Statistics Department (2022-2024) Visiting Ph.D. at ETH Zurich's Optimization & Decision Intelligence Group Research focuses on stochastic optimization , nonconvex optimization , and minimax optimization with applications to federated learning and the intersection of machine learning and operations research . Their recent work establishes theoretical bounds for optimization algorithms in minimax settings. Publication trends reveal expertise in distributed optimization , communication efficiency , and algorithm stability . Key venues include AISTATS, ICLR, NeurIPS, and UAI conferences, with technical reports on arXiv and PMLR proceedings. 2025 ICLR Blogpost on optimization bounds 2024 ICLR/ICML algorithm stability analysis 2022 UAI complexity bounds 2020 NeurIPS series on optimization frameworks Awarded the Acheson J. Duncan Fund (2024) and "Yu Qian" Scholarship (2016), Siqi also serves as reviewer for top venues like NeurIPS, AAAI, and SIOPT journal. Teaching portfolio includes optimization theory, probability, and linear algebra applications at both JHU and ETH Zurich.
Fangjinhua Wang is a Researcher affiliated with the Department of Computer Science at ETH Zurich, working within the Professorship for Computer Science. Their role involves contributing to cutting-edge research in fields such as 3D reconstruction, computer vision, and neural networks. The research focuses on advancing methodologies like scene graph manipulation, multi-view stereo techniques, and holistic human-scene reconstruction. Collaborations and projects emphasize practical applications in robotics, computer graphics, and AI-driven systems. While no explicit education details are provided, the research trajectory reflects a deep engagement with computational methods for 3D modeling and vision-based systems. The work bridges theoretical advancements with real-world applications, addressing challenges in scene understanding, object interaction, and human-robot collaboration. Research interests are centered on interdisciplinary topics including neural representation learning, robust visual localization, and the integration of geometric priors for high-fidelity reconstructions. The output demonstrates a commitment to both foundational research and applied solutions in computer science and robotics.
PD Dr. Daniel Werner Meyer-Massetti is a Privatdozent (Part-Time Lecturer) at the Department of Mechanical and Process Engineering , ETH Zürich. His research focuses on stochastic methods for fluid dynamics and multiphase transport problems in complex systems. Primary Affiliation: ETH Zürich, Department of Mechanical and Process Engineering Email: meyerda@ethz.ch His work bridges theoretical and applied research in turbulence, porous media, and combustion. Key contributions include: Stochastic particle-based frameworks for fractured subsurface flows Turbulence modulation in droplet-laden flows Uncertainty quantification in heterogeneous reservoirs Computational tools like the Netflow Python library His recent publications demonstrate methodological advancements in: Modeling inertial particle clustering in turbulence Simulating evaporation dynamics in reactive flows Developing non-local transport formulations Quantifying dispersion mechanisms in porous media Validating kinematic turbulence models Creating adaptive simulation strategies He collaborates with research groups including the Coletti Group , Jenny Group , and Supponen Group , while maintaining connections to the Haller Group and Noiray People as a former member.
Michaël Unser is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering , leading the Biomedical Imaging Laboratory . He serves as Academic Director for Imaging at EPFL and contributes to cross-departmental teaching in Microengineering , Mathematics , and Life Sciences Engineering . His research spans Image Processing , Medical Imaging , Wavelets , and Spline-based Modeling , with a focus on multiresolution analysis and single-molecule localization microscopy . He has mentored over 30 PhD students and supervised numerous research projects. Recent publications highlight advancements in super-resolution microscopy , deep learning integration , and inverse problem solving for biomedical imaging. His work emphasizes mathematical rigor and open-source software development for accessible bioimaging tools. IEEE Technical Achievement Award (2008) IEEE EMBS Career Achievement Award (2020) Three ERC Advanced Grants (FUNSP, GlobalBioIm, FunLearn) As Academic Director for Imaging , he leads EPFL's cross-disciplinary imaging initiatives. His teaching includes Fundamentals of Image Analysis and Signals and Systems courses.
Ángel García-Fernández is an Associate Professor at the Polytechnic University of Madrid , specializing in Bayesian inference , multi-target tracking , and nonlinear filtering with applications in signal processing, robotics, and underwater mapping. His work includes the development of Poisson multi-Bernoulli mixture (PMBM) filters, iterated posterior linearization algorithms, and direction-of-arrival (DOA) measurement models.
Ursula Sury is a Swiss law professor, Vice Dean at Lucerne School of Computer Science and Information Technology, and founder of Advokatur Sury AG. Specializing in IT law, data protection, and legal risk management, she combines academic leadership with practical legal consultancy. Her career spans academic governance, postgraduate teaching, and interdisciplinary research. University of Zurich (1986) - Law Harvard Business School (2013) - Executive Education Columbia University (2008) - Legal Mediation Her research focuses on data protection law , blockchain governance , and digital transformation across sectors. Recent work examines AI compliance , cross-border data flows , and cybersecurity liability . She leads EU Commission research projects on regional innovation strategies. Key article trends include: Comparative analysis of Swiss and GDPR frameworks Legal challenges in blockchain/DLT systems Corporate responsibility for AI decisions Cloud computing liabilities Smart home privacy issues Digital identity regulations As founder of Advokatur Sury AG since 1993, she advises on Data protection IT contract management Digital compliance Her academic leadership includes directing the Management & Law master's program and serving on the CRUS rector conference. She represents Switzerland in international digital law forums.
Marcel Zbinden is a Senior Lecturer in Business Psychology at the Lucerne School of Business (HSLU), part of the Institute of Communication and Marketing (IKM). He specializes in sustainable consumer behavior, sharing economy, and market research. With over 18 years in academia and industry, he previously held roles such as Global Category Head at Emmi AG and Head of Market Research at TransferPlus Market Research AG. Education: lic.phil. in Social Psychology (University of Zurich), MBA in Marketing (University of Educatis). Research and Teaching Focus: Business Psychology, Market Research, Sustainable Consumption. He teaches in BSc and MSc programs and co-leads the CAS Customer Psychology course. His work explores sustainable consumer behavior dynamics, crisis impacts on consumption, and sharing economy engagement. Key Projects: Sharing Monitor Schweiz: Analyzing sharing economy trends in Switzerland Long-term studies on post-pandemic sustainable behavior Cycling adoption strategies for winter mobility Professional Contributions: Leads workshops, collaborates on behavioral coaching, and presents at conferences like Frontiers in Service (Maastricht, 2023).
Lénaïc Chizat is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) within the School of Basic Sciences and Institute of Mathematics. He chairs the Dynamics of Learning Algorithms (DOLA) laboratory and teaches advanced courses in machine learning and computational optimal transport, focusing on mathematical analysis of neural networks and measure transportation theory. His research centers on optimal transport theory and its applications to deep learning, with emphasis on Wasserstein geometry, entropic regularization, and gradient flow dynamics. He investigates implicit regularization in neural networks, convergence properties of learning algorithms, and the infinite-width limits of deep architectures. His work bridges theoretical mathematics with practical machine learning challenges, particularly in computational aspects of modern supervised learning. Analysis of his 15 most recent publications (2023-2025) reveals a strong thematic focus on entropic optimal transport, where he has made fundamental contributions to Sinkhorn algorithm convergence in continuous settings and Wasserstein barycenter computation. His research consistently explores the mathematical foundations of deep learning, especially training dynamics, min-max optimization, and the role of initialization in neural network scaling. Chizat currently advises PhD student Wang Guillaume Yitian and leads the DOLA laboratory, which develops theoretical frameworks for understanding learning algorithm dynamics through the lens of optimal transport and measure-valued optimization.
Professor Susan Mango is a leading researcher in developmental biology at the Biozentrum, University of Basel, where she serves as Professor of Cell and Developmental Biology since 2019. Her laboratory investigates fundamental questions about how complex organs develop from embryonic cells, with a focus on the nematode Caenorhabditis elegans as a model organism. She has made significant contributions to understanding transcriptional regulation, chromatin organization, and epigenetic inheritance during development. PhD, Princeton University (1984-1990) Undergraduate Researcher, MIT (1982-1983) Post-doctoral Fellow, University of Wisconsin (1990-1995) Professor Mango's research spans several interconnected areas of developmental biology. Her laboratory examines how chromosomes are organized in 3D within the cell nucleus and how this organization affects development, with particular focus on heterochromatin formation during embryogenesis. She has pioneered work on pioneer transcription factors, especially PHA-4/FoxA, which determines cell identity in the developing gut. Her research also investigates how environmental conditions experienced by parents can influence embryonic development in subsequent generations, exploring mechanisms of epigenetic inheritance. Her laboratory employs molecular genetics, live imaging, genomics, and innovative chromosome tracing techniques to address these fundamental questions. Professor Mango's publications reveal a consistent focus on nuclear architecture, transcriptional regulation, and developmental mechanisms using C. elegans . Her recent work has advanced chromosome tracing methodologies, explored translational control of mRNA localization, and investigated how neurons can modulate signaling to offspring. Her research shows a progression from foundational studies on organ development to more complex investigations of nuclear organization and transgenerational inheritance. Excellence in Teaching Award, University of Basel and Credit Suisse (2021) Elected to the European Molecular Biology Organization (EMBO) (2019) NIH NIGMS MERIT Award (2011-2020) MacArthur Foundation Fellowship (2009-2013) Harland Winfield Mossman Developmental Biologists' Award (2005) Professor Mango has led a productive research group investigating fundamental developmental processes for over two decades. Her laboratory has been supported by prestigious funding including the MacArthur Fellowship and NIH MERIT Award. She has mentored numerous students and postdoctoral researchers who have gone on to establish independent research careers. Her collaborative approach is evident in her extensive publication record spanning multiple disciplines within developmental biology. Professor Mango leads the Mango Research Group at the Biozentrum, University of Basel, where her team employs advanced imaging techniques including Chromosome Tracing and multiplex FISH to study nuclear organization during embryogenesis. Her laboratory investigates multiple projects simultaneously including Chromosomes in 3D, Epigenetic Inheritance, Pioneer transcription factors, and The Skin I Live in (focusing on epidermal development).
Dr. Scott Keating is a Lecturer at ETH Zurich’s Department of Earth and Planetary Sciences (D-EAPS), affiliated with the Institute of Geophysics. His research focuses on seismic inverse problem methodologies, particularly uncertainty quantification, numerical optimization, and the development of automated workflows for regional inversion updates. Current projects include full-waveform inversion of ambient noise measurements and CO2 storage monitoring using advanced sensor technologies like distributed acoustic sensing (DAS) and accelerometers. His work integrates cutting-edge computational methods, such as probabilistic inversion frameworks and adjoint-based optimization, to address challenges in subsurface imaging and parameter estimation. Applications span environmental seismology, carbon sequestration, and reservoir characterization, with a strong emphasis on practical, cost-effective solutions for real-world problems such as CO2 storage validation. Publications highlight contributions to elastic full-waveform inversion (FWI), including multiparameter analysis using combined geophone and DAS data, and innovative techniques like targeted nullspace shuttling to enhance inversion reliability. His research also addresses data sparsity and modeling uncertainties, advancing methodologies for robust subsurface monitoring and decision-making.
