Dr. Yongling Zhao is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, affiliated with the Chair of Building Physics. His research focuses on urban climate modeling, fluid mechanics, and heat transfer, with a strong emphasis on mitigating urban heat through innovative solutions like PCM-based cooling systems and urban vegetation interventions. He has contributed to projects such as the 'Future Resilient Systems' initiative at the Singapore-ETH Center and led the Urban Physics course at ETH Zürich. Key achievements include receiving a Certificate of Research Excellence from the University of Sydney and securing an SNSF scientific exchange project on bridging urban physics and data-driven design. His work integrates computational fluid dynamics, machine learning, and experimental methods to address urban climate challenges, such as heatwave impacts on buildings and green infrastructure efficacy. Publications highlight advancements in multiscale urban climate modeling, thermal buoyancy effects, and policy-driven cooling strategies. Collaborations span institutions like the Swiss Federal Laboratories for Materials Science and Technology (EMPA), focusing on practical applications of his research in sustainable urban development.
Dimosthenis Pasadakis is a Research Fellow at the Institute of Computing (CI) of the Università della Svizzera italiana (USI), where he serves as the Principal Investigator (PI) of the Huawei Research Center Zürich-funded project on Directed Acyclic Graph Partitioning for Scheduling Tasks. His research focuses on graph learning, combinatorial optimization, and large-scale algorithms for clustering, anomaly detection, and data analysis. He also holds the role of Chief Operating Officer (COO) at Panua Technologies Sagl, a Lugano-based software company specializing in high-end solutions for graph analytics and optimization. His academic journey includes a PhD successfully defended in 2023, supported by the Swiss National Science Foundation (SNSF) project on Balanced Graph Partition Refinement. He has organized minisymposia at SIAM LA 24 and PASC 24, and his work has been recognized with awards such as the IEEE HPEC Outstanding Paper Award (2024) and the IEEE SDS24 Best Poster Award (2024). Key research interests include spectral clustering, graph-based fraud detection, and high-performance computing. He actively collaborates with industry through grants and maintains an academic-industrial bridge through Panua Technologies.
Dr. Xiang-Zhao Kong is a Lecturer at the Institute of Geophysics, ETH Zurich, within the Department of Earth and Planetary Sciences (D-EAPS). He holds a PhD in Environmental Engineering from ETH Zurich (2010), where he was awarded the ETH Medal for his dissertation. His career includes postdoctoral research at the University of Minnesota and a Research Fellowship at the University of Queensland before returning to ETH Zurich in 2015. His research focuses on geothermal energy, flow and transport processes in porous media, reactive transport modeling, and subsurface engineering. Key areas include fractured formations, geothermal reservoir optimization, and CO₂ sequestration. He employs advanced computational methods like lattice-Boltzmann solvers and machine learning for subsurface flow modeling and reservoir characterization. Dr. Kong’s work bridges experimental and theoretical approaches, with notable contributions to mineral precipitation dynamics, fluid-rock interactions, and phase transition fracturing. His publications span geothermal systems, carbon capture, and subsurface energy storage. Recent efforts emphasize de-risking CO₂-Plume Geothermal (CPG) technologies and advancing fracture modeling via neural networks. Awards: ETH Medal for PhD Dissertation (2011) Teaching: Leads the 'Groundwater' course (Autumn Semester 2025).
Federica Lanza is a Lecturer at the Department of Earth and Planetary Sciences at ETH Zurich, affiliated with the Schweiz. Erdbebendienst (SED), the Swiss Seismological Service. Her work focuses on seismology, geophysics, and geothermal systems, with expertise in induced seismicity, fault dynamics, and advanced monitoring technologies like Distributed Acoustic Sensing (DAS). She teaches courses such as Seismic Waves II in the Autumn Semester 2025. Her research integrates field experiments, computational modeling, and machine learning to address challenges in seismic hazard assessment, geothermal energy development, and tectonic processes. Key areas include forecasting induced earthquakes at geothermal sites, analyzing fault interactions in fold-and-thrust belts, and developing innovative sensor systems for subsurface monitoring. Dr. Lanza collaborates on large-scale projects like the Utah FORGE initiative, advancing techniques for real-time seismic monitoring and fracture network characterization. Her contributions bridge fundamental geophysical research with practical applications in energy systems and risk mitigation.
Prof. Alexandre Refregier is a Full Professor at the Department of Physics, ETH Zürich. He leads research in cosmology and astrophysics, focusing on dark energy, dark matter, and large-scale structure analysis using observational, theoretical, and instrumental approaches. His work spans gravitational lensing, cosmic microwave background, and galaxy cluster studies. He has contributed to major projects like the Dark Energy Survey and Euclid mission, leading instrumental development and data analysis efforts. Refregier holds a PhD from Columbia University (1997) and has held positions at institutions including Princeton University and the University of Cambridge. Education: PhD in Physics, Columbia University, 1997 M.Phil. and M.A. in Physics, Columbia University, 1992–1993 B.S. in Physics (Summa cum Laude), University of Texas at Austin, 1991 Research Interests: Dark energy/dark matter dynamics, weak lensing, baryon acoustic oscillations, cosmic microwave background analysis, and cosmological probes. His interdisciplinary methods combine theoretical modeling with cutting-edge observational techniques and instrument design. Key Contributions: Over 200 published papers (e.g., on Euclid mission instrumentation, joint lensing-CMB analyses, and baryonic feedback modeling). Active in developing simulation tools like PyCosmo and GalSBI for cosmological inference. Awards/Grants: Not explicitly listed, but his leadership in major collaborations implies significant funding and recognition. Active in Simons Foundation-supported research. Labs/Teams: Leads the Cosmology Group at ETH Zurich, involved in Euclid's science and instrument teams, and collaborates on projects like HIRAX and SKA simulations.
Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.
Dr. Nicolò Pagan is a Postdoctoral Researcher at the Social Computing Group within the Department of Informatics at the University of Zurich. He is also a member of the National Centre of Competence in Research (NCCR) Automation. Pagan holds a Ph.D. from ETH Zurich (2021), an M.Sc. from EPF Lausanne, and a B.Sc. from Politecnico di Torino. His research focuses on AI ethics, fairness in automated decision-making systems, and generative AI applications for health interventions. He has published in top venues like Nature Communications and has supervised multiple student projects on topics like feedback loops in recommendation systems and generative AI modeling. Education: Ph.D. in Automatic Control, ETH Zurich (2021) M.Sc. in Computational Science and Engineering, EPF Lausanne B.Sc. in Applied Mathematics, Politecnico di Torino Research Interests: AI ethics and societal impact Feedback loops in automated systems Generative AI for health interventions Algorithmic fairness in social media Network formation dynamics His recent work explores fairness effects of algorithmic systems and uses LLM-based generative AI to model large-scale human behavior for health campaigns. Over 10 peer-reviewed publications span topics like recommendation systems' societal impact and game-theoretic network analysis. Advising and Grants: Supervised 5+ student projects on topics like opinion dynamics and fairness in ride-hailing systems. Active in NCCR Automation's interdisciplinary research initiatives. Labs/Teams: Member of the Social Computing Group and NCCR Automation, collaborating on projects linking AI ethics to real-world systems.
Prof. Manuel Eisner is the Wolfson Professor of Criminology and Director of the Institute of Criminology at the University of Cambridge, and a retired Professor of Sociology at the University of Zurich. His research focuses on explaining societal and historical patterns of interpersonal violence, psychological and social mechanisms influencing violent behavior, and evidence-based prevention strategies. He leads the Violence Research Center and founded the Zurich Project on Social Development (z-proso). Eisner has authored 15 books and over 100 journal articles, with work spanning English, German, Spanish, and French. His advisory roles include collaborations with the WHO, UNICEF, and World Bank. Notable awards include the Sellin-Glueck Award from the American Society of Criminology. Education: Studied history at the University of Zurich, earned a doctorate in sociology. His research integrates criminology, sociology, and public health, examining topics like adolescent victimization impacts, historical homicide patterns, and global violence reduction frameworks. He co-organized the 2014 WHO global violence reduction conference at Cambridge. Research interests emphasize transdisciplinary approaches to violence prevention, combining longitudinal cohort studies with innovative methods like ecological momentary assessment and machine learning. His work bridges academic theory with practical policy, influencing international initiatives such as the WHO INSPIRE framework. Eisner's team includes researchers like Denis Ribeaud and Margit Averdijk, advancing understanding of violence dynamics through large-scale data analysis.
Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.
Lettry Louis is an Assistant Professor at HES-SO Valais-Wallis, affiliated with the School of Engineering and the Department of Computer Science and Communication Systems. His research focuses on Computer Vision, Deep Learning, and Image Processing, with applications in intrinsic image decomposition, multi-camera systems, and pattern recognition. He leads projects funded by Innosuisse, including WhiteNightSkEyes (CHF 13'928, 2022–2023) and Calligranalytics (CHF 15'000, 2022–2023), which explore virtual architecture visualization and automated handwriting digitization, respectively. His research contributions include seminal work on unsupervised deep learning methods for image decomposition, adversarial networks, and large-scale pedestrian detection datasets like Wildtrack. He collaborates with academic partners at VS - Institut Systèmes industriels and researchers such as Luc van Gool and Pascal Fua. His projects address challenges in immersive virtual environments and robust computer vision algorithms.
Konrad Tiefenbacher is a dual tenure-track Assistant Professor at the University of Basel and ETH Zürich, holding positions in the Department of Chemistry (Basel) and the Department of Biosystems Science and Engineering (ETH Zürich). He specializes in applying artificial intelligence and remote sensing to study glaciology, climate science, and environmental systems. His research bridges molecular synthesis (from his PhD in natural product chemistry) with cutting-edge AI-driven Earth observation. Education: Chemical studies at the Technical University of Vienna and University of Texas at Austin, followed by a PhD under Prof. Mulzer (University of Vienna) focusing on total synthesis of bioactive natural products. Postdoctoral research in molecular recognition at The Scripps Research Institute (Prof. Rebek). Became an independent researcher at TU Munich in 2012 before his current dual appointment since 2016. Research interests include deep learning for glacier calving front detection, retrogressive thaw slump mapping, GNSS reflectometry, and self-supervised environmental monitoring. His work integrates multi-sensor data and transformer networks for climate foundation models, with projects like AI-CORE addressing Arctic and Antarctic cryosphere challenges. Publications emphasize AI applications in polar regions, such as calving front dynamics in Greenland/Svalbard, permafrost disturbances, and methane detection. His methods combine semantic segmentation (e.g., PixelDINO) and transformer architectures (DDM-Former) for high-resolution Earth surface analysis. Labs/Teams: Leads the Synthesis of Functional Modules group, collaborating on AI-driven environmental monitoring tools and large-scale datasets like DARTS and IceLines.
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.
Markus Meierer is an Assistant Professor at the Institute of Management, University of Geneva, and serves as an External Lecturer at the Chair of Marketing for Social Impact, Department of Business Administration, University of Zurich. His interdisciplinary work integrates marketing, network science, and data science to study large-scale consumer networks and behavior. His research focuses on understanding how consumers' social ties influence behavior, using advanced methodological tools such as multilevel modeling to account for contextual effects. His expertise spans machine learning, marketing analytics, and computational linguistics, applied to real-world challenges in telecommunications, insurance, banking, and retail sectors. Markus holds a PhD and has an extensive educational background, including studies at the Kellogg School of Management, University of Zurich, and University of Trier. He is fluent in German, English, and French, and brings both academic rigor and industry experience to his roles. He is actively involved in leveraging data science to build customer-centric organizations, leading research initiatives on social networks and consumer behavior at the University of Zurich. Assistant Professor, Institute of Management, University of Geneva External Lecturer, Chair of Marketing for Social Impact, University of Zurich His professional experience includes roles as a Data Scientist and Research Group Leader at UZH, where he collaborates with major industries to apply data-driven strategies. No scientific awards are listed in the provided text. He advises on data strategy and market development, with a strong focus on translating academic research into practical business applications. His teaching includes Machine Learning at UZH. He leads the research group 'Social Networks & Consumer Behavior' at the University of Zurich, fostering interdisciplinary collaboration between marketing and data science.
Edy Portmann is a Professor at the Department of Informatics, University of Fribourg, where he leads the Resilient Systems Group at the Human-IST Institute and chairs the Mobiliar Cluster for Resilience. He holds a doctorate in fuzzy logic and has extensive experience in both academia and industry, having worked at Swisscom, PwC, and EY, and conducted research at the National University of Singapore, UC Berkeley, and the University of Bern. His research interests include Fuzzy Logic , Cognitive Cities , Computational Intelligence , Digital Ethics , and Human-Computer Interaction . He explores how fuzzy systems can enhance knowledge representation, decision-making, and urban governance, particularly in smart and resilient city contexts. His work integrates digital humanities and connectivist learning theories to bridge the social and semantic web. The recent publications (2022–2025) highlight a strong trend toward ethical AI , explainable systems , public participation , and the application of fuzzy logic in conversational agents , decision support , and urban informatics . He frequently publishes on topics such as digital ethics, emotional chatbots, and systemic consensus in group decision-making. HMD Best Paper Award 2018 He advises several PhD and research students and collaborates extensively with researchers such as Sara D’Onofrio, Patrick Kaltenrieder, and Luis Terán. His work often involves interdisciplinary grants and partnerships with public institutions like Swiss Post. He leads a dynamic research team focused on resilient, human-centered computing and cognitive city technologies.
Lena Jaeger is an Associate Professor of Digital Linguistics at the University of Zurich, where she leads research at the intersection of linguistics, computational cognitive science, and machine learning. She joined the Chair of Computational Linguistics at UZH in July 2020 after establishing a Machine Learning Junior Research Group at the University of Potsdam, funded by the German Federal Ministry of Education and Research. Her educational background spans multiple disciplines: she earned an MA in Chinese Language and Culture (Sinology) from the University of Freiburg im Breisgau, Tongji University Shanghai, Beijing Language and Culture University, and Université Paris 7 Denis-Diderot; followed by an MSc in Experimental and Clinical Linguistics at the University of Potsdam; and completed her doctorate in cognitive science at the same institution. Notably, she also earned a bachelor's degree in computer science during or after her doctoral studies. Professor Jaeger's research focuses on investigating cognitive mechanisms underlying human language processing using experimental psycholinguistics, computational modeling, and machine learning methods. Her current work develops machine learning techniques for analyzing eye-tracking data to understand cognitive processes reflected in eye movement behavior. This interdisciplinary approach combines insights from linguistics, cognitive science, and artificial intelligence to create models that bridge human and machine language understanding. Her recent publications reveal a strong trend toward developing eye-tracking methodologies, creating multilingual corpora, and applying machine learning to understand reading behavior and language processing. Her work spans from fundamental research on cognitive mechanisms to practical applications in educational technology, medical diagnostics, and AI development. Best student late breaking work award for Reporting Eye-Tracking Data Quality: Towards a New Standard Best short paper award for Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models Professor Jaeger actively supervises multiple PhD students across computational linguistics, machine learning, and phonetics disciplines. Her research group collaborates extensively on large-scale projects like the MultiplEYE initiative, which establishes standards for multilingual eye-tracking data collection. She has secured significant research funding, including a Machine Learning Junior Research Group grant from the German Federal Ministry of Education and Research before moving to UZH. Her laboratory work centers on eye-tracking methodologies, developing tools like pymovements for eye movement data processing, and creating comprehensive corpora such as MECO (Multilingual Eye-Movement Corpus), MultiplEYE, and CoLAGaze. These resources support cross-linguistic research on reading behavior and language processing across diverse populations.
