Savas Ceylan is a Researcher at ETH Zurich's Institute of Geophysics, affiliated with the Swiss Seismological Service (SED) and the Department of Earth and Planetary Sciences. His work focuses on planetary seismology, particularly analyzing seismic data from Mars using advanced techniques like deep learning. Ceylan's research addresses Martian tectonic activity, impact cratering, and seismic event characterization, contributing to understanding Mars' geological dynamics through missions like InSight. Key research interests include seismic denoising, fault rupture modeling, and planetary interior structure analysis. He has collaborated on studies of large earthquakes in Turkey and Martian seismicity patterns, emphasizing real-time seismic analysis and planetary hazard assessment. Publications highlight innovations in seismic data interpretation, from denoising algorithms to impact rate estimation on Mars. His work bridges computational methods with geophysical observation, advancing knowledge of extraterrestrial seismology.
Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.
Mauro Pezzè is a Full Professor of Software Engineering at the Università della Svizzera italiana (USI) and Università di Milano Bicocca, leading the STAR research group since 2006. He holds a laurea from the University of Pisa and a PhD from Politecnico di Milano. His research focuses on software testing, analysis, self-adaptive systems, and cloud systems. He has held editorial roles, including Editor-in-Chief of ACM Transactions on Software Engineering and Methodologies (TOSEM), and served on numerous program committees. Education: Laurea (Pisa), PhD (Politecnico di Milano). Professional roles include Dean of the Faculty of Informatics at USI (2009-2013), visiting scientist at UC Irvine and Edinburgh, and technical lead for international projects. He co-authored a seminal book on software testing (Wiley, 2007), with over 670 citations. Research Interests: Software Testing, Self-Adaptive Systems, Cloud Computing, AI in SE, Sustainable Software. Projects include work on field-based testing, failure prediction in distributed systems, and neuro-symbolic approaches for test oracles. Grants and Advising: Led STAR Lab projects in self-healing systems, GUI testing, and semantic matching. Advised numerous PhD/postdoc students (e.g., Ciniselli, Di Grazia, Qiu). Collaborations with European tech firms on R&D initiatives. Labs/Teams: STAR Group at USI/Constructor Institute, Bicocca, and Politecnico di Milano. Current members include postdocs and PhD students working on AI-driven testing and cloud reliability.
PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.
Frédérick Massin is a researcher at ETH Zürich's Swiss Seismological Service (SED), specializing in seismology, earthquake early warning systems, and volcanic monitoring. He is affiliated with the Bedretto Underground Lab (BULGG) and contributes to global seismic research. His work focuses on developing advanced algorithms for rapid earthquake rupture characterization (e.g., FinDer), improving earthquake early warning (EEW) systems in regions like Central America and New Zealand, and analyzing seismic data to enhance disaster preparedness. Key research areas include: Earthquake Source Physics and Real-Time Modeling Seismic Data Center Innovation and Global Networks Volcanic Seismicity and Lahar Detection Socio-Technical Aspects of EEW Systems Recent projects involve assessing EEW impacts in Central America, improving seismic catalogues (FEAR-1), and investigating lake ice seismicity. He collaborates internationally on initiatives such as the Nepal School Seismology Network and Yellowstone volcano-tectonic studies.
Farhad Rachidi is a Full Professor at the Swiss Federal Institute of Technology (EPFL), where he serves as the Head of the Electromagnetic Compatibility (EMC) Laboratory within the School of Engineering's Department of Electrical Engineering. His research group has been active in EMC research since the early 1980s and maintains collaborations with numerous international institutions including Universities of Bologna and Rome (Italy), Uppsala University and KTH (Sweden), University of Toronto (Canada), University of Florida (USA), and others. Professor Rachidi's research spans electromagnetic compatibility, lightning electromagnetics, lightning and EMP interaction with transmission lines, electromagnetic time reversal, fault location, numerical computation of electromagnetic fields, and power line communications. His work integrates theoretical modeling with experimental validation, particularly in the context of lightning phenomena and electromagnetic interference. The research group develops innovative techniques such as electromagnetic time reversal for applications ranging from lightning detection to partial discharge localization in power systems. Analysis of his recent publications (2023-2025) reveals a strong focus on electromagnetic time reversal techniques, lightning physics and modeling, machine learning applications in electromagnetic phenomena, and advanced computational methods for electromagnetic field analysis. His work bridges fundamental electromagnetic theory with practical applications in power systems, atmospheric electricity, and security technologies. 2025 IEEE EMC Technical Achievement Award 2005 CIGRE Technical Committee Award 2006 Blondel Medal from SEE 2016 Berger Award from ICLP Best Paper Awards of IEEE Transactions on EMC (2016, 2018) Motohisa Kanda Award for most cited papers (2012-2018) 2024 Distinguished Honorary Professor at Tsinghua University 2014 Honorary Professor at Xi'an Jiaotong University Professor Rachidi has supervised numerous students through semester projects, diploma projects (equivalent to MS), and PhD programs at EPFL. His research is primarily sponsored by Swiss National Science Foundation, European Community programs, European Space Agency, Swiss Electrical Utilities, and private companies. He has served in leadership roles including President of the International Conference on Lightning Protection (2008-2014), Editor-in-Chief of IEEE Transactions on Electromagnetic Compatibility (2013-2015), and President of the Swiss National Committee of the International Union of Radio Science (2012-2020). The EMC Laboratory at EPFL, which he heads, maintains the Säntis lightning research facility and has been instrumental in advancing our understanding of lightning phenomena through direct measurements at instrumented towers. The group has developed innovative techniques including electromagnetic time reversal for fault location in power networks and lightning detection systems.
Nirupam Gupta is an Assistant Professor at the Department of Computer Science (DIKU), University of Copenhagen. His research focuses on robustness, privacy, and fairness in distributed machine learning systems, with particular emphasis on federated learning and Byzantine fault tolerance. He actively contributes to the design of secure, resilient, and privacy-preserving algorithms for collaborative AI systems. He teaches courses in Privacy in Machine Learning (PriMaL) Machine Learning B (MLB) both offered in hybrid formats with full remote participation support. His latest book, Robust Machine Learning: Distributed Methods for Safe AI , provides foundational insights into the field. Key research themes in his work include: Byzantine-Robust Optimization Differential Privacy in Federated Systems Personalization under Adversarial Conditions Secure Aggregation Techniques Contact: nigu@di.ku.dk
Antonios Papaemmanouil is the Head of the Institute of Electrical Engineering and the Competence Center for Digital Energy and Electric Power at the Lucerne School of Engineering and Architecture (HSLU). He holds an MSc from the University of Patras, Greece, and a PhD from ETH Zurich. His academic rank is Lecturer in the field of Digital Energy and Electric Power. His work focuses on digitalization of power systems, e-mobility integration, and local energy markets. Education: MSc in Electrical Engineering and Information Technology, University of Patras PhD in Power Systems Planning, ETH Zurich Research interests include smart grids, data-driven power systems, digital infrastructure management, and decentralized AI applications. He leads projects such as SWEET RECIPE, LANTERN, and ENFLATE, which address energy transition challenges. His work emphasizes grid analytics, asset management, and innovation in energy systems. Key projects include studies on e-mobility aggregation, federated learning for load forecasting, and stability validation of hydropower plants using Hardware-in-the-Loop. His research outputs span peer-reviewed articles on topics like fault detection in distribution networks and decarbonization strategies for local electricity systems. Professional roles include Track Chair at IEEE Smart Cities Conference 2022 and Co-Guest Editor for Journal MDPI Energies . He actively contributes to Swiss energy initiatives via SwissT.Net and IEEE PES Schweiz.
Tao Lin is a Tenure-Track Assistant Professor and Principal Investigator of LINs Lab at Westlake University, School of Engineering. He leads cutting-edge research in deep learning optimization, generalization, and robustness, particularly in distributed and federated settings. Prior to this, he was a Ph.D. student at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, under the supervision of Prof. Martin Jaggi and Prof. Babak Falsafi. Doctor of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2017–2022) Master of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2014–2017) Bachelor of Engineering (with honors), College of Electrical Engineering, Zhejiang University, China (2010–2014) His research focuses on the intersection of optimization and generalization in deep learning, leveraging theoretical and empirical insights into loss landscapes and training dynamics to design efficient and robust learning and inference methods. This includes work on decentralized and federated learning under noisy, heterogeneous, and hardware-constrained environments. His work spans algorithmic innovation, theoretical analysis, and practical system integration. The recent publications from his lab demonstrate a strong trend in advancing federated learning, efficient inference for large language models, multimodal foundation models in pathology, and robust training under distribution shifts. Key themes include communication efficiency, model personalization, gradient tracking, and hardware-aware learning. His group has published at top venues including NeurIPS, ICML, ICLR, CVPR, and ECCV, with several papers receiving oral or spotlight presentations. ECCV Best Paper Candidate, 2024 Top 2% Scientists Worldwide 2024 (Stanford University) Doctoral Program Thesis Distinction Award, EPFL, 2022 Outstanding Performance Bonus, EPFL, 2021–2022 Top Reviewer: NeurIPS, ICML, AISTATS He advises multiple Ph.D. and master’s students, including Yongxin Guo, Futing Wang, Peng Sun, and Yuxuan Sun, whose work has been accepted at premier conferences. He has secured competitive grants as PI and participant, including the National Natural Science Foundation of China for Excellent Young Scientists Fund (Overseas) and the Science and Technology Innovation 2030 – Major Project. He also contributes to the community through service as an area chair (NeurIPS, ICML), reviewer for top journals and conferences, and organizer of workshops and academic events. His open-source contributions, such as Post-local SGD, have been integrated into PyTorch. Tao Lin teaches graduate courses such as Research Methodology of Computer Science and Technology and Deep Learning at Westlake University. He is actively involved in academic governance, serving on committees for student seminars, academic exchange, doctoral studies, and teaching leadership. The LINs Lab runs a regular research seminar on Deep Learning and Optimization, fostering a collaborative and dynamic research environment.
Matteo Biagiola is a Researcher in the Faculty of Informatics at Università della Svizzera italiana (USI), Lugano, Switzerland, and a PostDoctoral researcher at the University of St. Gallen (HSG). He specializes in software testing, particularly test generation for Web applications, deep reinforcement learning systems, and autonomous driving software. His work focuses on enhancing AI robustness through testing and improving software testing via AI techniques. Biagiola holds a Ph.D. from Università degli Studi di Genova (Italy) in collaboration with Fondazione Bruno Kessler, Trento. He has conducted postdoctoral research at USI on the Precrime ERC Advanced Grant project under Paolo Tonella. His research tools include μPRL (mutation testing for RL agents), STILE (web test parallelization), and GenBo (boundary state generation for autonomous systems). Education: Ph.D.: Università degli Studi di Genova / Fondazione Bruno Kessler (2016–2020) M.Sc.: Università Politecnica delle Marche (2014–2016) Affiliations: PostDoc & Scientific Collaborator: University of St. Gallen / USI (2025–present) PostDoc: USI (2020–2025) Visiting Ph.D. Student: University of British Columbia (2018) His research interests span AI-driven testing tools, autonomous system validation, and simulation-based testing. He has received the Distinguished Paper Award at ICST 2025 and serves on program committees for major conferences like FSE, ICSE, and ICST. He co-organizes workshops like DeepTest (co-located with ICSE) and the Cyber-Physical Systems tool competition (SBFT).
Dr. Alberto Ceccato is a Lecturer at the Department of Earth and Planetary Sciences, ETH Zurich, Switzerland. He specializes in structural geology and tectonics, with a focus on crustal deformation mechanisms, fluid-rock interactions, and fault zone processes. His research integrates field observations, experimental studies, and numerical modeling to address questions in orogenic systems, subduction zones, and continental collision. His work spans diverse geological settings, including the Alps, Norwegian margin, and Western Alps meta-ophiolites. Key research interests include the role of fluids in fault activation, crustal rheology during collisional orogeny, and the structural evolution of fractured basement systems. Dr. Ceccato collaborates with international teams to advance understanding of subsurface connectivity, seismic hazard, and the interplay between tectonics and geochemical cycles. Recent studies highlight analyses of Alpine catchment hydrogeology, multiphase deformation in passive margins, and nanoindentation experiments on quartz mechanics. His publications emphasize methodological innovations, such as combining virtual outcrop models with discrete fracture network simulations to quantify fault zone properties.
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.
Dr. Indranil Basu is a Researcher at ETH Zurich's Department of Materials, specifically within the Metal Physics and Technology group led by Prof. J.F. Löffler. His research focuses on light alloys, high entropy alloys, additive manufacturing, and process-structure-property relationships. He holds a Bachelors from IIT Roorkee, a Masters from the University of Western Ontario, and a PhD from RWTH Aachen University, where he investigated rare earth effects on Mg alloys. His postdoctoral work at the University of Groningen addressed mechanical behavior and dislocation dynamics in Mg/Ti-based and high entropy alloys, collaborating with industries like Bosch and SKF. Key research areas include magnesium alloy ductility enhancement via atomic segregation, strengthening mechanisms in high entropy alloys, and additive manufacturing of metallic multimaterials. He has been recognized with the prestigious Borcher's Prize from RWTH Aachen University for his doctoral work. His publications span topics such as ferritic steel embrittlement mitigation, twinning-induced stress gradients, and interfacial effects in high entropy alloys. Advising and grants details are not explicitly mentioned in the text. He is affiliated with the Metal Physics and Technology group, contributing to both academic and industrial projects.
Sven Daniel Wolfe is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich, where he leads the Swiss National Science Foundation (SNF) Ambizione project examining the aftereffects of mega-events on global cities. He holds a PhD in Geography from the University of Lausanne, an MSc in Geographies of Global Change from the University of Zurich, and an MA in Political Science from the European University St. Petersburg. His research focuses on the socio-spatial impacts of mega-events, urban sustainable development, and everyday geopolitics, often analyzing the interplay between authoritarian regimes and urban transformation. Education: PhD in Geography, University of Lausanne (2019) MSc in Geographies of Global Change, University of Zurich (2016) MA in Political Science, European University St. Petersburg (2014) Research Interests: Wolfe investigates the long-term urban consequences of large-scale international events like the Olympics and FIFA World Cup, emphasizing their geopolitical dimensions and sustainability challenges. His work critiques Potemkin neoliberalism—where authoritarian states use event-driven development to project soft power while neglecting local communities. He also explores everyday geopolitics, analyzing how state policies and conflicts manifest in daily urban life, particularly in post-Soviet regions. Scientific Awards: Swiss National Science Foundation Ambizione Fellowship (2023–2027) Advising & Grants: Wolfe’s SNF Ambizione grant supports his longitudinal study of mega-event legacies in cities worldwide. While no formal advisees are listed, he has contributed to research networks like the Ukrainian Geopolitical Fault-line Cities advisory group (2019) and co-founded the City Collaboratory urban studies network (2020). His work bridges academia and public discourse through media engagements with The New York Times, BBC, and others. Labs & Teams: He collaborates with the Spatial Development and Urban Policy group at ETH Zürich and serves as Vice President of the Swiss Association of Geography (since 2023). The City Collaboratory network, co-founded in 2020, fosters interdisciplinary urban research across Switzerland and beyond.
Dr. Anne Obermann is a Lecturer at the Department of Earth and Planetary Sciences at ETH Zürich, Switzerland. She is affiliated with the Schweiz. Erdbebendienst (SED) and conducts research at the Bedretto Underground Laboratory for Geosciences and Geoenergies (BULGG). Her work focuses on seismic monitoring, geothermal energy systems, and hydraulic stimulation experiments. Key projects include CO2 mineral storage characterization at Iceland's geothermal sites and real-time seismic data processing using tools like DUGseis. Research interests revolve around the integration of geophysical imaging techniques (seismic tomography, noise interferometry) with hydro-mechanical processes in subsurface environments. She has led studies on induced seismicity, reservoir engineering, and fault structure analysis in fractured rock systems. Notable contributions include developing monitoring frameworks for geothermal reservoirs and evaluating stress heterogeneity impacts during stimulation. Publications highlight advancements in microseismic event detection using deep learning, multi-scale monitoring systems, and CO2 storage integrity. Collaborations involve international projects like CarbFix2 (Iceland) and BedrettoLab (Switzerland). Her work bridges theoretical geophysics with applied engineering solutions for sustainable energy and subsurface resource management.
