Esther Hänggi is a Professor and Co-Head of the Applied Cyber Security Research Lab at Lucerne University of Applied Sciences and Arts (HSLU), within the Lucerne School of Computer Science and Information Technology. She holds a Dr. sc. ETH Zurich in quantum information and cryptography and a MSc in Physics from EPF Lausanne. Her research focuses on cyber security, quantum cryptography, and quantum computing, with emphasis on practical applications like quantum-safe cryptography and post-quantum algorithms. She has led projects such as the Quantum-safe Hardware Security Module and contributed to initiatives like the IFZ FinTech. Her work bridges theoretical advancements and real-world implementations, including privacy amplification libraries and quantum key distribution systems. Notable awards include the ETH Medal for her doctoral thesis. She actively collaborates with institutions like the Swiss Quantum Initiative and the European Cyber Security Organisation, promoting quantum resilience in security systems.
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.
Michele Lanza is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), Lugano, where he co-founded the faculty in 2004. He is the founder and director of the Software Institute (since 2017) and leads the REVEAL research group, focusing on software visualization, evolution, and analytics. He holds a PhD from the University of Bern (2003), where he also earned his MSc (1999), and was a postdoctoral researcher at the University of Zurich. Research Interests: Software Engineering Software Visualization (notably the 'City Metaphor') Mining Software Repositories (MSR) Software Evolution and Analytics Program Comprehension Reverse Engineering and Architecture Recovery His recent publications (2023–2025) emphasize immersive software visualization in virtual reality, automated documentation, code refactoring, and empirical studies on developer behavior and documentation. There is a strong trend toward human-centric, visual, and data-driven approaches, often leveraging VR and interactive tools for deeper software understanding. Scientific Awards: Ernst Denert Award (2003) Credit Suisse Teaching Award (2007, 2009) Best Paper Awards (SANER 2024, VISSOFT 2022, ICPC 2016) Most Influential Paper Award (MSR 2010) Advising and Grants: Prof. Lanza has supervised over 13 PhD students and numerous MSc and BSc theses, fostering a vibrant research group. He leads multiple funded projects, including SNF grants (FORCE, TUML, INSTINCT, PROBE, ESSENTIALS, BIGDATA), FFL (CSRD), and industry R&D initiatives. Funding supports travel, hardware, software, and PhD positions, enabling cutting-edge research in software engineering. Labs and Teams: He leads the REVEAL research group and the Software Institute at USI, which serve as hubs for innovation in software visualization, mining, and evolution. These teams are highly collaborative, interdisciplinary, and active in top-tier conferences (ICSE, FSE, ICSME, MSR, VISSOFT).
Pablo Timoner is a Researcher at the Institute of Global Health, part of the Faculty of Medicine at the University of Geneva. He holds a PhD in Environmental Sciences (2021) and a Master’s in aquatic ecology (2017). His work focuses on climate change impacts on biodiversity, geospatial modeling for health accessibility, and river ecosystem dynamics. Currently, he collaborates with the WHO on geospatial models to enhance healthcare accessibility in emergencies and fragile contexts. Education : PhD in Environmental Sciences, University of Geneva (2021) Master’s thesis on aquatic macroinvertebrates in restored river channels, University of Geneva (2017) Research Interests : Climate change impacts on biodiversity, geospatial health service modeling, river ecosystem resilience, and freshwater invertebrate ecology. His work integrates GIS tools, biostatistical approaches, and environmental data to address global challenges in health and ecology. Articles Trends : Recent publications emphasize snow cover dynamics in Swiss alpine regions, healthcare accessibility modeling in Mali, and biodiversity shifts in freshwater ecosystems. Tools like the inAccessMod R package reflect his focus on open-source geospatial solutions. Scientific Awards : No awards explicitly listed. Advising & Grants : Collaborates on WHO-funded projects and contributes to initiatives like EUROPONDS. No formal advisees listed. Labs/Teams : Active member of the GeoHealth group, focusing on global health and environmental data integration.
Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .
Georg Spinner is a Lecturer and Head of the Research Group for Medical Image Analysis & Data Modelling at the Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW). His work integrates computational methods with biomedical research, focusing on stroke management, intracranial aneurysm risk modeling, and quantitative medical imaging. Primary affiliation: Zurich University of Applied Sciences Research focus: Bayesian networks, medical imaging, computational epidemiology Projects: GEMINI (digital twins for stroke), Stroke DynamiX (causal networks in stroke care), IVIM muscle activation studies Using Bayesian networks and advanced imaging techniques like IVIM DWI, his research aims to develop data-driven decision support systems for neurovascular diseases. He has contributed to modeling stroke health data and intracranial aneurysm risk stratification through international multicenter collaborations. His work spans computational biology, neuroinformatics, and digital health, with publications in journals like Medical Image Analysis and conferences including the International Conference on Computational and Mathematical Biomedical Engineering. Key methodologies include causal inference, dynamic disease modeling, and quantitative image analysis.
Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.
Franck Iutzeler is a Professor of Applied Mathematics at Université de Toulouse, working within the Statistics & Optimization team of the Institut Mathématique de Toulouse and teaching in the Department of Mathematics. He previously served as an Assistant Professor at Université Grenoble Alpes from 2015 to 2023 and completed his Habilitation à Diriger des Recherches in 2021. His research focuses on the intersection of optimization, statistics, and optimal transport theory to develop robust data-driven models. Key areas include numerical optimization, statistical learning, stochastic programming, and optimal transport. He is particularly interested in distributionally robust optimization using Wasserstein metrics and has developed the skwdro Python library for implementing these methods. Iutzeler's recent publications demonstrate a strong focus on Wasserstein Distributionally Robust Optimization (WDRO), with multiple papers in top venues like NeurIPS and SIAM Journal on Optimization. His work bridges theoretical guarantees with practical implementation, particularly through the skwdro library which provides efficient code for WDRO in machine learning applications. ANR JCJC grant for project STROLL: Harnessing Structure in Optimization for Large-scale Learning Co-PI of ANITI chair on Trust and Responsibility in Artificial Intelligence led by JM. Loubes and J. Bolte Iutzeler actively supervises PhD students including Yu-Guan Hsieh (awarded Université Grenoble Alpes's PhD award), Gilles Bareilles, Waïss Azizian, and Victor Mercklé. He has secured research funding through the ANR (MAD project on Automatic Differentiation) and ANITI. His current research includes statistical fairness using optimal transport theory and automatic differentiation for stochastic optimization. He leads the development of the skwdro library for Wasserstein Distributionally Robust Optimization and is involved with ANITI (Toulouse's AI Cluster), where he also took responsibility for the 2nd year of the Master SID in Data Science & Engineering in September 2024.
Prof. Dr. Stefan Brönnimann serves as a Professor and Unit Leader of Climatology at the Institute of Geography, University of Bern. His extensive research portfolio spans historical climatology, climate dynamics, and atmospheric circulation reconstruction, with particular focus on the past 400 years. He plays a leadership role in major international climate initiatives including the IPCC assessment reports and various climate reanalysis projects. Brönnimann's research centers on reconstructing historical weather and climate patterns through the innovative combination of early instrumental data, proxy records, and climate models. His work examines large-scale climate variability, interannual-to-decadal atmospheric circulation patterns, volcanic eruption effects on climate, and climate-society interactions. His methodologies often involve transforming historical documents into usable climate datasets and applying advanced machine learning techniques to weather reconstruction challenges. Recent publications reveal a strong emphasis on European climate patterns, hydroclimate extremes, and the development of novel datasets and tools for climate research across high-impact journals in climate science, paleoclimatology, and climate informatics. Notable scientific achievements include: Lead author for Chapter 2 of the IPCC Working Group I 5th Assessment Report President of the Commission 'Atmospheric Chemistry and Physics' (ACP) of sc.nat Editorial leadership for multiple prestigious journals including Meteorologische Zeitschrift, Climate of the Past, and Geographica Bernensia Leadership roles at the Oeschger Centre for Climate Change Research Active participation in international initiatives like the Twentieth Century Reanalysis Project and Atmospheric Circulation Reconstructions over the Earth (ACRE) Brönnimann has secured substantial funding through numerous national and international projects including SNF, NCCR Climate, EU FP-7, HORIZON2020, COST, and ERAnet.RUS. He has organized multiple international workshops on weather and climate extremes, atmospheric circulation variability, and historical climate events like the Tambora eruption. His work connects closely with the Oeschger Centre for Climate Change Research at the University of Bern, where he leads Work Package 2 and serves on the Steering Group, demonstrating his significant institutional leadership within Bern's climate research community.
Zapater Sancho Marina is an Associate Professor at the ReDS Institute (Institute of Reconfigurable and Embedded Digital Systems) within the School of Engineering and Management Vaud (HEIG-VD), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). She holds dual master's degrees in Electronic and Telecommunication Engineering from Universitat Politècnica de Catalunya (2010) and a PhD in Computer Science from Universidad Politécnica de Madrid (2015). Her career includes postdoctoral work at EPFL (2016-2020) and assistant professorship at Universidad Complutense de Madrid (2015-2016). Education BSc & MSc in Electronic Engineering (UPC 2010) PhD in Computer Science (UPM 2015) Research Focus spans cross-layer optimization of heterogeneous architectures for performance and energy efficiency, with emphasis on: Embedded systems (IoT/edge computing) High-performance compute architectures Analog in-memory computing for AI Thermal/power management in 3D chips Cloud-edge AI workload orchestration Publication Trends show expertise in RISC-V simulation frameworks, analog computing tiles for CNNs, virtual memory redesign, and AI-driven cloud performance prediction. Her recent work explores thermal-aware 3D chip management, hybrid-cache reliability optimization, and open-source teaching platforms for radio theory. Awards include a Spanish government PhD fellowship. She has led 4 European H2020 projects since 2016 and currently serves as PI for 4 industrial collaborations (Facebook/Intel/Huawei), Innosuisse projects, and HES-SO initiatives. Labs & Teams include the ReDS Institute, EPFL's Embedded Systems Laboratory, and collaborations with Yale/Edinburgh. She co-developed the ALPINE simulation framework and SO3 operating system modifications for Midgard project validation.
Iyán Méndez Veiga is a Research Associate and Doctoral Student at the Lucerne School of Computer Science and Information Technology, part of the Lucerne University of Applied Sciences and Arts (HSLU). Their research focuses on quantum cryptography, post-quantum security protocols, and privacy amplification. They have conducted studies on adversarial wiretap channels and reproducible builds in open-source systems like Arch Linux. Education includes a BSc in Physics from the University of Oviedo, Spain, and an MSc in Physics from Ulm University, Germany. Their work bridges theoretical cryptography with practical implementations, such as developing the randextract library for validating privacy amplification algorithms. Key projects include quantum-safe hardware security modules, implications of post-quantum cryptography on certificate management, and quantum cryptography in practice. Presentations emphasize topics like quantum hardware verification and secure communication protocols. Collaborations with institutions like the University of Applied Sciences and Arts Northwestern Switzerland highlight their applied research focus.
Andrea Mocci is a Lecturer at the Faculty of Informatics of the Università della Svizzera italiana (USI). His work focuses on software engineering methodologies, developer productivity, and IDE interaction analysis. He is affiliated with the Software Institute and actively contributes to academic events such as the IEEE International Workshop on Mining and Analyzing Interaction Histories (MAINT). His research explores empirical software engineering techniques, including developer behavior analysis, code documentation improvement, and the application of natural language processing to software artifacts. Key areas of investigation include: IDE interaction and navigation efficiency Code redundancy and quality metrics Video tutorial analysis for educational content Runtime systems and annotation APIs Defect prediction and software maintenance Publications from 2016-2020 highlight trends in developer-centric tools, holistic recommender systems, and visualization techniques for software evolution. His work often bridges theoretical formal methods with practical developer workflows, aiming to improve both software quality and developer productivity through empirical studies and tool development.
Yaniv Benhamou is an Associate Professor of Digital Law at the Faculty of Law, University of Geneva. Specializing in data protection, intellectual property, art law, and technology law, he focuses on emerging technologies like AI and Web3, digital commons, and self-regulatory mechanisms. He has conducted research visits at Harvard University, Melbourne Law School, and the Max Planck Institute. Co-founder of the Digital Law Summer School and a WIPO expert on copyright and museum issues, he authored the Report on Copyright Practices and Challenges of Museums . He advises on cybersecurity, chairs the Digital Transformation Office (BTN) at the Rectorate, and co-founded Artists Rights , offering pro bono legal advice. Active in cultural sectors, he collaborates with institutions like the Musée de l'Elysée and Swiss Museums Association (AMS). His work bridges legal frameworks with digital innovation, emphasizing ethical governance and policy. Research Interests: His research spans AI governance, digital transformation, cultural policy, and data ethics. Key areas include the impact of digital technologies on cultural value chains, collective data governance (open source, data trusts), and legal challenges in AI-generated content. He explores how digital tools reshape intellectual property rights and museum practices, advocating for equitable frameworks in the digital age. Professional Activities: As Of Counsel in a Geneva law firm and member of CIMBAR (Bar modernization), he advises on legal tech and innovation. He serves on the Steering Committee of the Digital Law Center and the Art Law Center. Awards include the 2008 Artcurial Prize for Controversies: an ethical and legal history of photography . Advising & Grants: He advises public authorities on cybersecurity and chairs the BTN, driving University-wide digital initiatives. His projects include the Sovereignty Digital Study and Future of Culture reports. Collaborations with EPFL and EPFZ on the Swiss Digital Trust Label promote ethical AI governance. Grants and institutional roles highlight his interdisciplinary approach to digital challenges. Labs & Teams: Leads the Digital Law Center, CAS Digital Law Finance, and collaborates with the Art Law Center. His work integrates legal research with practical solutions for digital-era challenges, fostering interdisciplinary dialogue between law, technology, and culture.
Roberto Minelli is a Scientific Collaborator and Academic Coordinator at the Software Institute, Faculty of Computer Science, Università della Svizzera italiana (USI), where he also completed his Bachelor, Master, and PhD in Informatics. His work bridges research, education, and technology outreach, with a strong focus on software engineering, visualization, and developer interaction analysis. His research centers on leveraging interaction data from development environments to enhance software comprehension and evolution. Key areas include software visualization , mining software repositories , reverse engineering , and program comprehension . He has pioneered work in visual metaphors such as Software Cities and explored immersive environments like virtual reality for code visualization. The recent publications reflect a consistent trend in visual analytics for software engineering, with increasing emphasis on social and collaborative aspects of development (e.g., Discord, GitHub issues), large-scale system comprehension, and the integration of diverse data sources into unified visual models. His work combines empirical studies, tool development, and human-centered evaluation. Best Paper Award, IWESEP 2016 Most Influential Paper Award, ICPC 2015 Distinguished Reviewer Award, ICPC 2020 Minelli actively mentors students, co-supervising numerous Bachelor and Master theses in software visualization and analytics. He has secured multiple research and development mandates, including projects like Self-Driving Cars on Interactive Dynamic Tracks (SNF Agora) and Sphere Two: Swiss Pavilion @ Expo 2025 . He plays a central role in organizing key events such as VISSOFT, SIESTA, and #FormulaUSI, and contributes to curriculum development and outreach programs targeting secondary education. He is involved in several labs and teams, primarily the REVEAL research group (led by Prof. Michele Lanza) and the Software Institute at USI. His leadership in initiatives like CodeLounge and #FormulaUSI underscores his commitment to experiential learning and public engagement in computing.
Matthias Stürmer serves as a Professor at Bern University of Applied Sciences (BFH) within the School of Management and Head of the Institute for Public Sector Transformation (IPST). He concurrently holds a lecturer position at the University of Bern. His professional identity centers on Swiss digital governance initiatives, with active leadership roles in @Parldigi, @DigitalImpactCH, @CH_Open, and @OpendataCH advocating for open source, open data, and transparent public sector innovation. His research program bridges legal technology and digital governance, specializing in multilingual (German/French/Italian) processing of Swiss jurisprudence. Core focus areas include developing AI systems for judicial summarization and criticality prediction, advancing digital sovereignty frameworks, and analyzing sustainable public procurement practices. He investigates the tension between open justice principles and privacy preservation in court documentation, while pioneering methods for anonymizing legal texts against re-identification threats from large language models. Stürmer's publication trajectory reveals a strategic shift toward legal AI applications since 2020, with 12 of his 15 most recent works addressing multilingual legal processing challenges. His scholarship consistently targets Swiss institutional contexts, creating specialized datasets like Multilegalpile and Lextreme while examining practical implementation barriers for open source adoption in public administration. As director of IPST at BFH, he leads institutional efforts to transform public sector services through open standards and collaborative governance models. His team develops practical frameworks for digital sovereignty implementation and sustainable ICT procurement, directly influencing Swiss federal policies on open data and public sector technology adoption.
