Carmela Troncoso is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), heading the Security and Privacy Engineering Lab (SPRING Lab) in the School of Computer and Communication Sciences. She holds a PhD in Engineering from KU Leuven (2011) and has held academic roles at IMDEA Software Institute and Gradiant, along with postdoctoral research at COSIC Group. Research Focus: Security and privacy engineering, machine learning's societal impact, privacy-enhancing technologies, and decentralized privacy-preserving systems. Publications: Her work spans top venues like IEEE S&P, CCS, NeurIPS, and PETS, with recent emphasis on adversarial robustness, proximity tracing, and privacy metrics. Awards: IEEE S&P Distinguished Paper Award (2022) EPFL Latsis University Prize (2022) Caspar Bowden Award Runner-up (2021) CNIL-INRIA Privacy Protection Award (2017) ERCIM PhD Thesis Award (2011) She supervises active PhD students and collaborates with institutions like ICIJ. Her lab's SPRING Lab website details current projects.
Christos Dimitrakakis is a Full Professor of Data Science at the University of Neuchâtel, Switzerland, where he is affiliated with the Institute of Computer Science within the Faculty of Science. His research focuses on artificial intelligence, particularly reinforcement learning, fairness, and algorithmic privacy. He has held academic positions at several prestigious institutions including the University of Oslo, Chalmers University of Technology, and the University of Lille. His educational background includes: 1997: BEng from the University of Manchester 1998: MSc from the University of Essex 2006: PhD from IDIAP/EPFL under the supervision of Samy Bengio Professor Dimitrakakis specializes in reinforcement learning and decision making under uncertainty, with particular expertise in reverse reinforcement learning, preference elicitation, and human-AI collaboration. His research bridges theoretical foundations with practical applications, especially in autonomous systems and privacy-preserving AI. He has made significant contributions to understanding representations of uncertainty, differential privacy, and group fairness in algorithmic decision-making. His work often combines Bayesian inference with reinforcement learning frameworks to develop robust and fair AI systems that can operate effectively in uncertain environments. His recent publications demonstrate a strong focus on theoretical foundations of reinforcement learning while addressing practical challenges in fairness, privacy, and decision making. The research shows an evolving trajectory from core reinforcement learning techniques toward applications in fair and transparent AI systems, with increasing emphasis on real-world constraints and ethical considerations. His work spans multiple domains including game theory, matching markets, and bandit algorithms, reflecting an interdisciplinary approach to AI research. Notable scientific achievements include: Best Paper Award at Cooperative-AI@NeurIPS 2021 for "Interactive Inverse Reinforcement Learning for Cooperative Games" Student paper award at EAAMO'22 for "On Meritocracy in Optimal Set Selection" Professor Dimitrakakis actively mentors PhD students and has supervised numerous researchers who have gone on to academic and industry positions. His current research group at the University of Neuchâtel includes five PhD students working on reinforcement learning, fairness, and uncertainty in AI systems. He has secured funding for projects including a national initiative to develop intelligent bird deterrent systems for crop protection, demonstrating the practical impact of his research. His group collaborates with industry partners such as Agroscope, and he maintains academic connections with institutions worldwide. His research group at the University of Neuchâtel is actively working on interdisciplinary projects, most notably the development of an "intelligent scarecrow" system for bird pest control in agriculture. This project combines computer vision, reinforcement learning, and behavioral modeling to create adaptive AI systems that can minimize crop damage while accounting for bird behavior patterns. The group maintains expertise in both theoretical foundations and practical implementations of AI systems.
Dr. Federico Wadehn is a Senior Lecturer at the Institute of Signal Processing and Wireless Communications within the School of Engineering at Zurich University of Applied Sciences (ZHAW), specializing in digital signal/image processing and machine learning applications for biomedical and engineering systems. His role includes teaching core courses and supervising graduate theses. Education: PhD in Statistical Signal Processing with Probabilistic Graphical Models, ETH Zürich (2014-2018) Teaching Certificate, ETH Zürich and ZHAW (2017-2018) Visiting Researcher in Neuromonitoring, MIT (2016) MSc/BSc in Electrical Engineering and Information Technology, ETH Zürich (2009-2014) Erasmus Exchange in Control Engineering, Imperial College London (2012-2013) Continuing Education: MATLAB C Coder (2023), Embedded C (2021), IBM Rational Rhapsody (2020) Research Interests: His work bridges Digital Signal Processing (classical, ML, and deep learning), Computer Vision , Sensor Fusion/Kalman Filtering for inertial systems, Probabilistic Graphical Models for navigation, and Physiological System Modeling (respiratory/cerebrovascular). He emphasizes model-based approaches for real-world biomedical applications, particularly in neuromonitoring and respiratory care. Publication Trends: His 2015-2020 publications reveal a consistent focus on advanced signal processing for physiological monitoring, featuring model-based separation of biological signals (eye movements, blood flow), sparse state-space modeling, and Kalman smoothing innovations. The work integrates probabilistic graphical models with biomedical constraints, demonstrating strong cross-disciplinary impact in IEEE journals and conferences. Scientific Awards: Study and doctoral scholarship from German Academic Scholarship Foundation (2014) Abiturpreis Physik from Deutsche Physikalische Gesellschaft (2008) Advising and Professional Activities: He supervises bachelor's/master's theses at ZHAW while maintaining industry connections through prior roles as Systems Engineer at Bosch Sensortec (2021-2023), Research Engineer at Hamilton Medical AG (2019-2021), and Computer Vision Freelancer (2021). His dual academic-industry experience informs practical curriculum development. Laboratories and Teams: He contributes to ZHAW's Digital Health Lab and Medical Systems Platform, focusing on translating signal processing innovations into clinical tools for respiratory monitoring and motion analysis, with recent patent work in ventilation technology.
Alexander Hägele is a researcher at the Laboratoire d'apprentissage automatique et d'optimisation (Machine Learning and Optimization Laboratory, MLO) within the College of Computer and Communication Sciences at École polytechnique fédérale de Lausanne (EPFL). His work focuses on theoretical and practical aspects of machine learning optimization.
Paul Bürkner is a Full Professor of Computational Statistics at TU Dortmund University , focusing on probabilistic (Bayesian) methods. His research sits at the intersection of statistics and machine learning, with applications across quantitative sciences. Key Roles : Developer of the brms R package, member of the Stan and BayesFlow development teams. Research Pillars : Bayesian inference, uncertainty quantification, amortized workflows, simulation-based inference, and probabilistic programming. His lab advances methods for prior specification, model evaluation, and scalable inference, collaborating on applications from cognitive science to ecology. Recent work emphasizes neural superstatistics and BayesFlow for efficient mixture and multilevel models. Students and researchers are encouraged to reach out for collaboration or thesis opportunities. Key Labs/Teams : BayesFlow Development Team Stan Project ELLIS Network (European Laboratory for Learning and Intelligent Systems)
Sebastian Dalleiger is a Tenure Track Assistant Professor in Machine Learning at KTH Royal Institute of Technology's Theoretical Computer Science division in Stockholm, Sweden. His research bridges foundational machine learning, responsible AI, and data analysis algorithms with applications in the natural sciences. Current affiliation: KTH Royal Institute of Technology (Theoretical Computer Science) Research focus: Theoretically grounded methods for machine learning, responsible AI, and data analysis Application areas: Natural sciences, multi-graph analysis, and privacy-preserving techniques His recent work explores federated learning, matrix factorization, and pattern discovery across multiple datasets. Current research trends include geometric approaches to hypergraphs, entropy optimization, and explainable data decompositions.
Raoul de Charette is a Research Director in computer vision at Inria Paris, leading the Astra-Vision group within the ASTRA team. His academic journey includes a PhD from Mines Paris (2012) and Habilitation (HDR) in 2022, with research stints at Carnegie Mellon University (2011), Mines Paris (2013), and the University of Makedonia (2014). His educational background comprises: PhD from Mines Paris (2012) Habilitation (HDR) (2022) De Charette's research centers on robust and interpretable visual scene understanding , spanning 3D scene reconstruction, domain adaptation, material recognition, and physics-grounded vision foundation models. His work integrates physical principles and synthetic data to enhance model robustness in real-world scenarios like autonomous driving and urban environments. Key contributions include uncertainty-aware 3D scene completion (PaSCo), material extraction from single images (Material Palette), and prompt-driven domain adaptation (PODA). Recent publications reveal a strategic shift toward vision-language integration, material-centric scene understanding, and foundation models that minimize labeled data dependency. His group pioneers physics-informed approaches to improve interpretability and resilience against environmental challenges like adverse weather conditions. Key scientific recognition includes: Best Paper Honorable Mention at EGSR 2025 for MatSwap ELLIS Membership PR[AI]RIE-PSAI Fellowship De Charette actively mentors four PhD students—Fatima Balde, Mohammad Fahes, Ivan Lopes, and Tetiana Martyniuk—often in industry collaborations with Valeo.ai and Kyutai. He secures funding through fellowships and industry partnerships, regularly opening PhD positions (including a 2025 opening for Physics-Grounded Vision Foundation Models). As an area chair for CVPR, ECCV, WACV, and IROS, he shapes the field through conference leadership and co-organizing initiatives like the African Computer Vision Summer School. He directs the Astra-Vision group within Inria Paris' ASTRA team, driving interdisciplinary research at the intersection of computer vision, machine learning, and physics-based modeling for real-world deployment in robotics and intelligent transportation systems.
Martin Zach is a postdoctoral researcher at the Center for Biomedical Imaging (CIBM), EPFL , specializing in inverse problems in biomedical imaging. He joined the Mathematical Imaging Section under the supervision of Prof. Michael Unser in September 2024. PhD : Graz University of Technology (2024), advised by Thomas Pock Martin’s research bridges model-based reconstructions and data-driven approaches in imaging, with a focus on MRI reconstruction and diffusion models . His work explores regularization techniques, energy-based priors, and probabilistic modeling. Recent publications highlight advancements in Gaussian mixture models , non-linear inversion , and Langevin sampling for biomedical imaging. His Google Scholar profile reveals a strong focus on inverse problems (2020–2025), with applications in MRI , CT , and quantitative phase imaging . Key methodologies include diffusion models , generative priors , and regularization algorithms , spanning disciplines from machine learning to computational biology . Martin’s current role at EPFL involves collaborative research in mathematical imaging , with affiliations to the BioMedical Imaging (BIG) Group in Lausanne, Switzerland.
Prof. Dr. Andreas Zischg is a leading academic at the Institute of Geography , University of Bern, serving as Group Leader for Human-Environment-Systems Modelling and Co-Director of the Mobiliar Lab for Natural Risks. With a focus on flood risk assessment and climate change adaptation , his research integrates coupled component models and GIS-based geospatial analysis to address extreme hydrological events and their societal impacts. Key Research Areas: Flood risk dynamics, climate resilience, surface water flood forecasting, and spatiotemporal vulnerability analysis Leadership Roles: Mobiliar Lab for Natural Risks (Co-Director), Human-Environment-Systems Modelling Group (Leader) Recent publications highlight his work on probabilistic flood warnings , property-level adaptation measures , and human mobility during disasters . His 2025 work explores surrogate models for national flood forecasting, while 2024 studies analyze road network disruptions, climate change storylines, and systematic flood risk monitoring frameworks. The Mobiliar Lab collaborations demonstrate his commitment to transdisciplinary research , combining hydrometeorological data with social science methodologies. His team develops visualization tools for risk communication and storyline approaches to climate change impact analysis, particularly in alpine environments. Current methodological innovations include deep-learning damage prediction and mobile phone data analysis for disaster response. These projects emphasize both technical model development and practical implementation for Swiss emergency management and policy frameworks.
Joanna Weng is a Senior Lecturer in Mathematics, Physics, and Sensor Technology at the ZHAW School of Engineering, affiliated with the Institute of Applied Mathematics and Physics (IAMP) and the Centre for Artificial Intelligence (CAI). She holds roles as Project Leader at the Safety-Critical Systems Research Lab and serves as an Associate Diversity Officer. Her expertise spans AI in safety-critical systems, functional safety (IEC 61508), nuclear power plant safety analysis, and particle physics. Education: CAS in Project Management, Functional Safety Engineer (TÜV Rheinland), and IEEE CertifAIEd Assessor. Research focuses on AI certification frameworks, deterministic/probabilistic safety analysis, and autonomous system safety. She leads projects like the Personnel Safety System (PSS) at the European Spallation Source (ESS) and co-leads the certAInty certification scheme for AI systems. Notable contributions include work on trustworthy AI assessment and MLOps-enabled safety. Key Projects: Personnel Safety System (PSS) at ESS IEEE CertifAIEd Assessor Training Machine Protection and Autonomous Predictive Interlock Systems certAInty AI Certification Scheme Awards/Certifications: CAS in Safe and Secure AI, TÜV Functional Safety Engineer, IEEE CertifAIEd Assessor. Grants/Advising: Innovationsuisse-funded certAInty project leader, overseeing interdisciplinary teams for AI safety research and implementation. Labs/Teams: Safety-Critical Systems Research Lab (IAMP), Centre for Artificial Intelligence (CAI), and collaborations with CERN Alumni and Women in Tech networks.
Dr. Andrei Kucharavy is an Assistant Professor at HES-SO Valais-Wallis in the Faculty of Economics and Services, where he teaches Business Economics courses including Fundamentals of Algorithms and Computational Thinking. He co-founded the GenLearning Center, an applied research entity focused on safety and deployment challenges of Generative AI technologies. His research centers on machine learning safety, privacy, and distribution from cybersecurity and human factors perspectives. PhD from Paris Sorbonne University (research at Johns Hopkins University and Stowers Institute for Medical Research) Engineer degree from Ecole Polytechnique Former postdoctoral researcher at EPFL's Distributed Computing Lab Dr. Kucharavy's research spans Applied Machine Learning, Large Language Models, and Cybersecurity with particular expertise in generative ML since 2018 and its cybersecurity applications since 2020. His work bridges theoretical machine learning with practical security implementations in Swiss defense contexts. His publications reveal strong focus on LLM vulnerabilities, privacy leakage detection, and developing bibliometric methods resilient to AI-generated content. His recent publications demonstrate expertise in LLM security vulnerabilities, code generation risks, and developing frameworks for technology forecasting using iterative self-prompting. The research shows consistent progression from foundational LLM understanding to specialized security applications, particularly in code vulnerability injection through dataset poisoning and malicious fine-tuning. Distinguished Cyber-Defence (CYD) Postdoctoral Fellowship recipient Dr. Kucharavy leads multiple Cyber-Defence Campus-funded projects totaling over CHF 430,000, including work on characterizing LLM attacks in code generation, developing vulnerability characterization frameworks, and secure privacy leakage detection protocols. His research team includes Percia David Dimitri and Vallez Cyril, with collaborations extending to Cyber-Defence Campus experts like Dr. Ljiljana Dolamic. He actively develops applied solutions through the GenLearning Center, focusing on on-premises deployment of generative models for healthcare applications and knowledge transfer tools that maintain data privacy while enhancing educational outcomes. His work directly informs Swiss defense technology standards for LLM-based solutions in cyber-physical systems.
Francesco D'Angelo is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the Institute of Computer Science and Communications Systems (IINFCOM). He is part of the Theory of Machine Learning Laboratory (TML) and enrolled in the Doctoral program in computer and communication sciences (EDIC) under the EPFL Doctoral School (EDOC). His research focuses on foundational aspects of machine learning, including sparse attention mechanisms, Bayesian neural networks, uncertainty estimation, and continual learning. He holds the position of Doctoral Assistant at the TML lab, where he contributes to advancing theoretical and practical applications of machine learning algorithms. Research Interests: Francesco's work bridges theoretical computer science with applied machine learning. His key areas include developing robust models for uncertainty quantification, improving generalization in overparameterized networks through regularization techniques like weight decay, and exploring the interplay between transformer architectures and causal structure induction. He also investigates methods to mitigate catastrophic forgetting in continual learning scenarios and applies Bayesian approaches to enhance out-of-distribution detection capabilities. Publications: His recent work explores topics such as sparse attention mechanisms in transformers, the necessity of weight decay in deep learning optimization, and Bayesian methods for uncertainty estimation. These contributions highlight his expertise in both foundational theory and practical algorithm design within the machine learning domain. Labs & Teams: As part of the TML lab, he collaborates on interdisciplinary projects at the intersection of machine learning theory and applications, contributing to advancements in neural network architectures and probabilistic modeling techniques.
Fateme Jamshidi is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Management of Technology and the Institute for Management, Technology and Economics. Her research focuses on: Causal inference and discovery Bayesian network modeling Multi-armed bandit algorithms Context-specific independence analysis Hidden confounding in causal networks Nonparametric statistical testing Recent work trends demonstrate expertise in causal modeling under uncertainty, with applications to machine learning and decision systems. Her publications explore graph-dependent regret bounds, causal imitability, and structural side information integration.
Kieran Chin-Cheong is a Researcher affiliated with the Professur für Informatik (Computer Science Professorship) at ETH Zürich. His position is part of the Department of Computer Science, focusing on Medical Data Science. He holds an email address at kieran.chincheong@inf.ethz.ch and is based at CAB G33.1, Universitätstrasse 6, 8092 Zürich, Switzerland. His research interests center on applying machine learning and computational methods to address challenges in healthcare, including medical imaging analysis, predictive modeling for clinical outcomes, and development of interpretable AI systems for medical diagnostics. His work spans areas such as pediatric disease prediction (e.g., pulmonary hypertension, appendicitis), glucose forecasting in diabetes, and synthetic medical data generation while preserving privacy. Notable contributions include advancements in deep learning for echocardiogram analysis, interpretable ultrasonography models, and methodologies for constrained clustering and multimodal data processing. His interdisciplinary approach bridges computer science and clinical medicine, aiming to improve diagnostic accuracy and patient care through data-driven solutions. No specific grants, awards, or advisees are explicitly listed in the provided materials. His research aligns with ETH Zürich's focus on translational medical informatics and computational healthcare technologies.
Dr. Amir Joudaki is a Researcher affiliated with the Department of Biomedical Informatics at ETH Zürich. His role is part of the Professorship for Data Analytics, focusing on interdisciplinary research at the intersection of machine learning and biomedicine. He is stationed at CAB F 53.1, Universitätstrasse 6, Zurich. His research interests span machine learning, biomedical data analytics, neural network theory, genomics, and bioinformatics algorithms. Notable contributions include work on deep neural network dynamics, batch normalization techniques, and graph-based genomic sequence analysis. Though not explicitly listed in the provided texts, his work suggests involvement in projects related to alignment-free bioinformatics methods, optimization in deep learning, and theoretical aspects of neural networks. Lab/Team Affiliation: Part of the Biomedical Informatics group at ETH Zürich, likely contributing to interdisciplinary data science initiatives in healthcare and genomics.
