Dr. Tobias Diehl is a Senior Scientist and Seismologist at the Swiss Seismological Service (SED) at ETH Zurich, leading the Seismotectonic Group since 2015. His research focuses on observational seismology, including seismic tomography, earthquake source analysis, and tectonic interpretation. He has held academic positions at ETH Zurich and international institutions, contributing to projects like the GANSSER seismic network in Bhutan and the AlpArray initiative. His work integrates advanced data analysis techniques with regional seismic monitoring to understand tectonic processes and earthquake hazards. Diehl teaches courses in crustal seismology and seismic tomography, and has authored over 50 peer-reviewed papers on topics ranging from Alpine tectonics to induced seismicity in geothermal systems.
Dr. Afifa Imtiaz is a scientific researcher at the Swiss Seismological Service (SED), ETH Zurich, since 2019. Her work focuses on earthquake hazard and risk assessment, particularly in Basel, Switzerland. She specializes in seismic ground motion analysis, site effects, and microzonation studies. Dr. Imtiaz holds a PhD in Engineering Seismology from Grenoble Alpes University (2015) and has extensive postdoctoral experience in France and Switzerland. Education: PhD in Engineering Seismology (2015), Grenoble Alpes University MSc in Engineering Seismology (2011), Grenoble Alpes University Research Interests: Dr. Imtiaz investigates spatial variability of ground motion in active seismic regions, focusing on basin effects and near-source dynamics. She develops numerical models to predict amplification and coherence patterns, with applications to urban risk assessment. Her work integrates geophysical data (e.g., shear-wave velocity profiles) with probabilistic risk frameworks. Key Projects: ARES PRD: Earthquake risk reduction in Haiti ANR EXAMIN: Ground motion variability for industrial infrastructure IMAGE: Geothermal exploration in sedimentary basins Basel Urban Seismic Risk Model: 3D geological-seismological integration Scientific Contributions: Her research bridges seismic hazard modeling with practical risk mitigation, particularly in urban environments. She has pioneered methods for combining ambient noise data with morphometric analyses to map resonance effects. Recent work focuses on scenario-based loss assessments and probabilistic amplification mapping.
Erich Walter Farkas is an Associate Professor of Quantitative Finance at the University of Zurich (UZH) and an Associate Faculty member at ETH Zurich's Department of Mathematics. He serves as Program Director for the joint UZH-ETH Zurich Master of Science in Quantitative Finance, established to bridge expertise in finance and mathematics. His research focuses on risk management, sustainable investments, and mathematical finance, emphasizing holistic approaches that integrate quantitative analysis with behavioral factors. Education: Farkas earned his doctorate and habilitation in Germany, later moving to Switzerland. He holds a Master's and Licentiate in Mathematics from the University of Bucharest, and a Certificate in Advanced Studies for Board Members from Bern-Rochester. Research Interests: Mathematical Finance, Quantitative Risk Management, Volatility Modeling, Sustainable Investment Impact, and Risk Measures. He leads projects like the Data-Driven Financial Risk (DaDFiR3) initiative and co-initiated the Finance Roundtable 2025 on AI and Big Data in finance. Advising & Grants: Supervises theses in quantitative finance and risk management. Active in organizing conferences such as ETH Risk Days and serves on boards like the Swiss Risk Association and Swiss Finance Institute. Professional Roles: Member of the Executive Education Board at UZH, Director of the Teaching Center at the Department of Finance, and founder of the Swiss Risk Association. His work bridges academia and industry, emphasizing practical applications of theoretical frameworks.
Ueli Schilt is a Research Associate and Doctoral Student at the Lucerne School of Engineering and Architecture, part of the Lucerne University of Applied Sciences and Arts (HSLU). His work focuses on thermal energy systems, renewable generation, and energy efficiency in Swiss urban and regional contexts. He is affiliated with the Institute of Mechanical Engineering and Energy Technology (IME), specifically within the Thermal Energy Storage research group. Role: Research Associate & Doctoral Student Institution: Lucerne University of Applied Sciences and Arts (HSLU) School: School of Engineering and Architecture Institute: Institute of Mechanical Engineering and Energy Technology (IME) Research Focus: Thermal energy storage, multi-energy system optimization, renewable integration Ueli Schilt’s research explores the integration of thermal energy storage in multi-energy systems, solar PV expansion, and heating system retrofits. His work emphasizes temperature considerations, load forecasting, and sensor technology validation. Key projects include decentralized renewable generation in Swiss regions and the SENSHOEK initiative for adaptive heating controls. Recent publications highlight advancements in air quality monitoring, heat pump consumption analysis, and communal energy planning tools. While no scientific awards are explicitly listed, his contributions to peer-reviewed journals and international conferences indicate active academic engagement. Collaborations with Philipp Schütz and other researchers underscore interdisciplinary teamwork in energy modeling and policy support.
Gaétan Raynaud is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Unsteady Flow Diagnostics Laboratory (UNFOLD) within the Institute of Mechanical Engineering (IGM) under the School of Engineering (STI) . His research focuses on fluid-structure interaction, fluid dynamics, and robotics, with applications in areas such as soft robotics, material design, and real-time sensing systems. He contributes to projects like the development of highly agile flat swimming robots and studies on flapping flag dynamics. His work integrates experimental fluid mechanics, computational modeling, and machine learning approaches, such as Physics-Informed Neural Networks (PINNs), to solve complex fluid-structure interaction problems and optimize robotic systems. Labs/Teams: UNFOLD Lab (EPFL), collaborating on interdisciplinary projects involving robotics, fluid dynamics, and soft materials. Technical Expertise: Event-based sensing, soft robotics actuation, data-driven modeling, and computational fluid dynamics.
Prof. Dr. Siegfried Handschuh is a Full Professor for Data Science and Natural Language Processing at the Institut für Informatik (ICS-HSG), University of St. Gallen. His research focuses on advanced NLP techniques, financial text analysis, and AI-driven solutions in cybersecurity and education. He leads projects like CS-AWARE-NEXT, enhancing cybersecurity awareness in public institutions. Prof. Handschuh has authored over 100 publications, with recent work emphasizing transformer optimization, generative AI applications, and educational tools for argumentative writing. His team collaborates with companies like Rheasoft and Peracton on AI-powered financial analytics and cybersecurity systems. Education: Doctorate in Computer Science, specialized in knowledge representation and semantic web technologies. Research Interests: Data science, machine learning, financial NLP, cybersecurity, and AI in education. His work bridges academia and industry, addressing real-world challenges in finance, cybersecurity, and educational technology through innovative AI frameworks.
Davide Martinenghi is a contract professor at the Università della Svizzera italiana (University of Italian Switzerland) and an Associate Professor at the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano, Italy. His academic career bridges theoretical and applied research in database systems and data science. Educational Background: M.Sc. in Computer Engineering from Politecnico di Milano Ph.D. in Computer Science from Roskilde University, Denmark His research focuses on advanced database topics such as ranking mechanisms, preference modeling, conceptual modeling of data, and fairness in data science and AI. He has contributed extensively to major journals and conferences including ACM Transactions on Database Systems, VLDB Journal, and IEEE Transactions on Knowledge & Data Engineering. Key domains of his work include: Database optimization and constraint handling Big data analytics and business intelligence Ranking algorithms and data analysis Ensuring fairness in AI-driven data systems Martinenghi actively engages in academic service as an Editorial Board member for the Data & Knowledge Engineering Journal and has participated in program committees for top-tier conferences like ACM-SIGMOD, ICDE, and PVLDB.
Rui Yao is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering (ENAC) and the Department of Civil Engineering. Additionally, he works as a Scientist in the Laboratory for Human-Oriented Mobility Eco-system (HOMES) within EPFL. His research focuses on large-scale equilibrium modeling in multi-modal transport systems, individual mobility choice modeling, and demand management strategies. Education: B.Sc. in Civil Engineering, Technion – Israel Institute of Technology Direct-track Ph.D. in Transportation Engineering, Technion – Israel Institute of Technology His research spans both theoretical and applied domains, including stochastic traffic equilibrium , multi-passenger ridesharing systems , perturbed utility models , and deep learning for choice analysis . He has contributed to advancements in data-driven route choice modeling , integrated equilibrium models for electrified logistics , and stable matching frameworks for mobility platforms . Rui Yao is affiliated with the Human-Oriented Mobility Eco-system (HOMES) lab at EPFL, which focuses on innovative transportation solutions. His work bridges theoretical modeling with real-world applications in smart mobility and transportation policy.
Matteo Favero is a Lecturer and Scientist at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC). He conducts research at the ETHOS Lab, focusing on data-driven interventions to enhance environmental and social sustainability in the built environment. His work integrates computational tools with human-centric approaches to optimize building performance and occupant well-being. Education: PhD in Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), 2022 MSc in Building Engineering, Politecnico di Milano, 2015 BSc in Building Engineering, Politecnico di Milano, 2012 Research Interests: Favero's research explores occupant behavior modeling, statistical analysis of thermal comfort, and human-building interactions. He emphasizes multi-domain studies to bridge gaps between energy efficiency, indoor environmental quality, and user satisfaction. His work supports the development of adaptive building controls and sustainable design guidelines through empirical data and interdisciplinary methodologies. Publication Trends: Recent articles (2020–2024) prioritize occupant-centric building operations, statistical rigor in comfort studies, and standardized documentation for behavior models. Themes include predictive algorithms for thermal preferences, validation of human-in-the-loop methods, and critical reviews of multi-domain research practices, reflecting a consistent focus on enhancing data reliability and practical applicability in sustainable building science. Awards: 2022 Best Paper Award: A guideline to document occupant behavior models for advanced building controls (Building and Environment) 2022 Best Paper Award: Quality criteria for multi-domain studies in the indoor environment (Building and Environment) Labs and Teams: He is a core member of the ETHOS Lab at EPFL, an interdisciplinary group advancing sustainability through computational and engineering solutions for human-oriented built environments.
Raffaella Buonsanti serves as an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL) within the School of Basic Sciences, holding multiple leadership roles including Director of SCGC Administration and Member of SB School Direction. She leads the Laboratory of Nanochemistry for Energy (LNCE) at EPFL Valais Wallis, focusing on cutting-edge nanomaterials research for energy applications. Her institutional affiliations span the Institute of Chemical Sciences and Engineering (ISIC), Swiss Center for Electronics and Microtechnology (SCGC), and Center for Digital Scholarship (CDS). Her research centers on nanomaterials synthesis for energy conversion , with particular expertise in colloidal nanocrystals, CO 2 electroreduction, and quantum dot applications. Key focus areas include: Designing tunable catalysts for CO 2 -to-fuel conversion Atomic-scale control of metal-oxide interfaces Stability mechanisms in electrocatalytic systems Data-driven nanocrystal shape prediction Hybrid quantum dot-molecular systems for energy transfer Current work emphasizes overcoming copper catalyst stability challenges through oxide coatings and liquid metal nanoparticle engineering. Analysis of her 15 most recent publications (2024-2025) reveals dominant research themes in electrocatalysis (73% of articles), nanomaterials synthesis (60%), and energy conversion (53%). Key technical advances include c-ALD-grown oxide shells for quantum dots, liquid gallium-based catalysts, and data-driven nanocrystal shape control. Her work consistently bridges fundamental surface science with industrial CO 2 electrolysis applications. She actively mentors 9 current PhD students and has supervised 11 graduates, with advisees researching copper nanocatalysts, CO 2 reduction mechanisms, and colloidal nanomaterials. Her teaching portfolio includes Introduction to Chemical Engineering Laboratory Works , Introduction to Transport Phenomena , and Nanomaterials for Chemical Engineering Applications . The LNCE laboratory under her direction develops colloidal synthesis methods for energy applications, with recent work focusing on solid-liquid electrocatalysts and quantum dot hybrid materials. Current projects emphasize scalable nanofoundry approaches and industrial implementation of CO 2 conversion technologies.
Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.
Prof. Dr. Maike Scherrer is a Professor at the Zurich University of Applied Sciences (ZHAW) School of Engineering, where she leads research in sustainable mobility and supply chain management. Her work focuses on developing innovative solutions for sustainable and circular supply chains, urban logistics, and resilient transportation systems. She directs multiple research projects including Sustainable and Circular Supply Chains for the MEM-industry and the Sustainable Mobility Lab, collaborating with industry partners to address critical challenges in sustainable logistics and mobility. Prof. Scherrer's research spans several key areas in sustainable operations and supply chain management. Her work in sustainable supply chains examines how to achieve net-zero emissions through circular economy principles and systemic approaches. In urban logistics, she develops innovative concepts like Smart Urban Multihub systems to reduce freight traffic and improve city livability. Her research on supply chain resilience investigates how to design networks that can withstand disruptions while maintaining mission-critical supplies. She also explores the electrification of freight fleets and temperature-controlled supply chains to reduce environmental impacts. Her recent publications demonstrate a strong focus on applying operations research and systems thinking to sustainability challenges. Key trends include using agent-based modeling to assess circular economy supply chains, developing optimization approaches for resilient networks, and examining the spatial aspects of pharmaceutical supply chains. Her work bridges theoretical frameworks with practical applications, particularly in the Swiss context but with implications for global supply chain sustainability. Prof. Scherrer leads multiple significant research projects including Sustainable and Circular Supply Chains for the MEM-industry (ongoing), Techno-Economic Grid Connection Optimization for Electric Freight Fleets (ongoing), and Designing and simulating resilient supply chain networks (ongoing). She serves as Deputy Project Leader for the Sustainable Mobility Lab and has completed notable projects such as User-based redistribution for free-floating car sharing and Predictive replenishment of urban distribution centres. Her research group operates within the Sustainable Mobility research focus at ZHAW's School of Engineering, collaborating with industry partners across multiple sectors including manufacturing, pharmaceuticals, and urban transportation. The team applies interdisciplinary approaches combining operations research, systems analysis, and sustainability science to develop practical solutions for complex mobility and logistics challenges.
David Brückner is an Assistant Professor of Theoretical Biophysics at the Biozentrum of the University of Basel, Switzerland, where he leads a research group investigating the physics of living systems. Appointed in April 2025, he develops theoretical frameworks to understand how cellular interactions govern multicellular organization during development. His work bridges statistical physics, soft matter theory, and developmental biology to address fundamental questions about information processing in biological systems. Brückner's research focuses on stochastic dynamics in developmental systems , exploring how cells make reliable decisions despite biological noise. He investigates Mechanical and biochemical information transmission in tissues Cellular memory mechanisms in confined migration General physical principles across developmental systems Self-organization in multicellular structures His approach combines biophysical modeling with information theory and machine learning to connect theoretical predictions with experimental data. His recent publications reveal groundbreaking insights into cellular memory during migration, hyperdisordered biological packing, and information flow in developmental systems. Notable work published in Nature Physics (2025) demonstrates how the actin cortex serves as a mechanical memory enabling efficient navigation through confined spaces – crucial for understanding wound healing and cancer metastasis. Brückner's scientific achievements have been recognized through prestigious awards including an ERC Starting Grant (2025), the Gustav Hertz Prize (2022), and an EMBO Long-Term Fellowship (2022). His research is supported by international collaborations with experimental laboratories worldwide. As a newly established group leader, Brückner actively recruits PhD students and postdoctoral researchers for his ERC-funded "InfoFate" project, which investigates how cells integrate dynamical, neighborhood, and mechanical signals to make fate decisions. His laboratory employs interdisciplinary approaches combining theoretical physics with cutting-edge biological data.
Daniele Silvestro is a researcher at ETH Zürich's Department of Biosystems Science and Engineering, working within the Computational Evolution group based in Basel, Switzerland. His research spans evolutionary biology, computational methods, and biodiversity science, with a focus on developing and applying novel analytical approaches to understand macroevolutionary patterns. Dr. Silvestro's research interests center on evolutionary biology and computational approaches to understanding biodiversity patterns through time. His work bridges micro- and macroevolutionary scales, with particular emphasis on phylogenetic methods, speciation processes, and the integration of fossil data with molecular phylogenies. He applies machine learning and artificial intelligence techniques to analyze large-scale biodiversity datasets, addressing questions about species diversification, extinction dynamics, and ecological interactions across deep time. His recent publications demonstrate a strong trend toward computational innovation in evolutionary biology, with increasing integration of artificial intelligence methods to tackle complex questions in biodiversity science. His work spans multiple biological systems, from plant-soil interactions to mammalian evolution, reflecting an interdisciplinary approach that combines theoretical modeling with empirical data analysis. Dr. Silvestro collaborates extensively with researchers across institutions and disciplines, contributing to major initiatives such as the 2030 Declaration on Scientific Plant and Fungal Collecting. His research has significant implications for biodiversity conservation, particularly in understanding how species and ecosystems respond to environmental change. His work on computational methods, including software development like DeepDiveR, demonstrates a commitment to creating practical tools for the broader scientific community. His research group at ETH Zürich appears to focus on developing and applying cutting-edge computational approaches to evolutionary questions, emphasizing the importance of integrating multiple data sources and analytical frameworks.
Professor Jenny leads the Jenny Research Group at ETH Zürich, specializing in turbulent reactive flows, rarefied gas kinetics, and biomedical fluid dynamics. Her work bridges fundamental research with industrial applications in energy systems and fluid mechanics. Develops advanced turbulence models (TDDM, hybrid LES/RANS) for multi-scale flows Pioneers data assimilation frameworks for RANS simulations using adjoint methods Advances particle-based stochastic algorithms for fractured porous media transport Her recent publications emphasize adaptive time integration techniques, probabilistic modeling of non-linear transport phenomena, and optimized simulation tools for hydrogen storage systems. The group's methodological innovations focus on reducing computational costs while maintaining physical accuracy through novel regularization strategies. Key applications include combustion device optimization, high-pressure tank filling analysis, and fractured reservoir simulations. Current projects integrate machine learning with traditional CFD methods to address challenges in droplet clustering, flame surface density propagation, and supersonic spray dynamics. The research framework spans from direct numerical simulations of fundamental flow physics to industrial-scale hybrid modeling implementations.
