Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.
Dr. Stefan Strub is a Researcher at ETH Zurich's Institute of Geophysics within the School of Earth and Planetary Sciences (D-EAPS). He is affiliated with the Professorship for Seismology and Geodynamics. His primary research focuses on gravitational wave astronomy, seismology, and geodynamics, with a strong emphasis on data analysis techniques for space-based missions like LISA. He develops advanced algorithms for parameter estimation in gravitational wave signals, including applications of machine learning and Bayesian methods. His work contributes to understanding massive black hole mergers, extreme mass ratio inspirals, and galactic binary systems. Key technical contributions include optimizing parameter estimation through genetic algorithms and GPU computing, as well as applying Gaussian Process Regression to LISA data. Dr. Strub's expertise spans both theoretical modeling and experimental sensor technology, as evidenced by his 2019 work on strain sensing applications. His research bridges geophysics with astrophysics, leveraging interdisciplinary approaches to tackle challenges in observational astronomy and earth sciences.
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.
Prof. Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). He holds a doctorate in computer science and mathematics from the University of Trier and has held roles including SNSF Professor at the University of Zurich, Full Professor at Bergische Universität Wuppertal, and Senior Assistant at ETH Zürich. His research focuses on higher-order graph analytics for temporal networks, machine learning, and computational social science. Education: PhD in Computer Science (University of Trier, Germany), Postdoctoral Research at ETH Zürich (2011–2016), and prior roles at Karlsruhe Institute of Technology and CERN. Research Interests: Machine learning on graphs, temporal network analysis, higher-order network models, and their applications in software engineering and social systems. He develops open-source tools like pathpy and git2net for network analysis. Recent Work: Focuses on causality-aware graph neural networks, temporal graph isomorphism, and network science applications in AI. Recent articles include studies on temporal network dynamics, path prediction, and Bayesian inference of network transitions. Awards: SNSF Professorship (2018), Junior-Fellowship (2014), and German Academic Scholarship Foundation (2004-2005). Active in editorial roles for EPJ Data Science and leadership in GI's Computational Social Science working group.
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.
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.
Tanja Stadler is a Full Professor at the Department of Biosystems Science and Engineering , ETH Zürich, and has served as Vice-Chair of the department since February 2024. She is also the president of the Swiss Science Advisory Panel COVID-19 and a member of the German National Academy of Sciences Leopoldina and EMBO. Education: MSc in Applied Mathematics, Technical University of Munich (2006) PhD in Applied Mathematics, Technical University of Munich (2008) Research Focus: Tanja's work integrates computational evolution and phylodynamics to address questions in macroevolution , epidemiology , and immunology . Her lab develops statistical tools to extract evolutionary and population dynamics from genomic data, with applications ranging from biodiversity studies to infectious disease outbreaks like COVID-19 and Ebola. Awards & Honors: ERC Starting Grant (2013) ERC Consolidator Grant (2020) SMBE Mid-Career Excellence Award (2021) Carus Medal (2021) Cloëtta Jubilee Prize (2023) Grants & Leadership: She has led major consortia producing genomic data and has advised the Swiss government through the Swiss National COVID-19 Science Task Force. Her lab continues to push boundaries in Bayesian phylogenetics and real-time pathogen surveillance .
Andrea Emilio Rizzoli is a Professor at SUPSI's Department of Innovative Technologies and Director of the Dalle Molle Institute for Artificial Intelligence (IDSIA). With dual Swiss-Italian citizenship, he holds a PhD in Computer and Automatic Control Engineering from Polytechnic University of Milan (1993). His career includes international research positions at Université de Lausanne and CSIRO Australia. His research focuses on: Intelligent decision support systems Logistics and environmental systems modeling Sustainable transportation solutions Uncertainty management in computational models He leads projects like GoEco! for sustainable mobility and rail freight optimization systems. Honors include: Presidency of iEMSs (2012-2014) Membership in ZALF's Scientific Committee 200+ publications with significant citation impact (h-index 22/30) He teaches courses on dynamic systems modeling and simulation, serving as head of several academic modules at SUPSI.
