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Pascal FrossardView profile

Professor

• Machine Learning
• Data Science
• Graph Signal Processing
• +6 more

Pascal Frossard is a Full Professor at the Department of Electrical Engineering in the School of Engineering (STI) at EPFL, with a courtesy appointment in the School of Computer and Communication Sciences. He founded and directs the LTS4 laboratory since 2003, co-leads the EPFL AI Center and Swiss Data Science Center, and serves as Associate Dean for Research at STI. Research Focus: Machine Learning, Graph Signal Processing, AI Applications in Healthcare, Computer Vision Academic Leadership: IEEE Fellow, ELLIS Fellow, Conference Chair roles Key Projects: Digital Pathology for Oncology, Cardiac Digital Twins, Robust Machine Learning Research Interests: His work bridges signal processing, machine learning, and applied mathematics, emphasizing biomedical applications. Recent research includes adversarial robustness in classifiers, network representation learning, and 360-degree video analysis. Scientific Awards: IEEE Fellow ELLIS Fellow Leadership in IEEE technical committees Advising & Grants: Supervised 20+ PhD students and postdocs. Secured major grants from PHRT, Hasler Foundation, FNS-Sinergia, Armasuisse, Google, and Cisco.

Olga Sorkine-HornungView profile

Professor

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +5 more

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

Mathieu SalzmannView profile

Academic

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Mathieu Salzmann is a Senior Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) in the School of Computer and Communication Sciences (IC). He also holds a courtesy appointment with the EPFL College of Humanities and serves as Deputy Chief Data Scientist at the Swiss Data Science Center (SDSC). He has held concurrent roles in teaching units including SIN, SODH, and SSC, reflecting his interdisciplinary engagement. His research focuses on the intersection of machine learning and computer vision, particularly in deep learning for 2D and 3D visual scene understanding, efficient and robust models, domain adaptation, and interpretable AI. These interests are evident across his extensive publication record in top-tier venues. His recent publications (2023–2024) show a consistent trend in advancing deep learning methods for visual recognition, with strong representation at CVPR, ICCV, ECCV, ICML, ICLR, and NeurIPS. Topics include domain generalization, 3D understanding, model robustness, and multimodal learning, often with applications in real-world systems. His editorial roles as Associate Editor for IEEE TPAMI and Action Editor for TMLR further highlight his leadership in the field. Area Chair: ICML 2023, CVPR 2023, ICCV 2023, NeurIPS 2023, AAAI 2024, ECCV 2024 Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Action Editor: Transactions on Machine Learning Research (TMLR) Mathieu Salzmann has supervised numerous PhD students at EPFL, both current and past, including Bouquet Yann Yanis, Javed Saqib, Li Shuangqi, and others. He has also been involved in research grants and collaborative projects, such as his work with S. Süsstrunk and R. Baroni on comics reconfiguration. His part-time role as Senior GNC Engineer at ClearSpace (2020–2024) illustrates his applied research engagement in aerospace systems. He is actively involved in EPFL’s data science and AI research ecosystem through SDSC and multiple labs.

Giovanni De MicheliView profile

Professor

• Integrated Circuit Design
• Hardware Synthesis
• Logic Optimization
• +5 more

Giovanni De Micheli is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding appointments in both the School of Computer and Communication Sciences (IC) and the School of Engineering (STI). He is affiliated with the Integrated Systems Laboratory (LSI) and serves as Scientific Director of EcoCloud. His primary office is located in INF 341 building at EPFL's Lausanne campus where he conducts research and teaching activities. Professor De Micheli's research spans multiple domains in electronic design automation and integrated circuit design. His primary interests include: Integrated Circuit Design and Hardware Synthesis Logic Optimization and Boolean Methods Nanoelectronics and 2D Electronics Quantum Computing and Emerging Technologies Low-Power Electronics and Energy-Efficient Systems Electronic Design Automation for Novel Computing Paradigms His recent publications demonstrate a strong focus on advancing logic synthesis techniques for both conventional and emerging technologies. Professor De Micheli's work bridges theoretical foundations with practical applications, particularly in adapting traditional EDA methods to novel computing paradigms like quantum computing and superconducting electronics. His research group has made significant contributions to the development of efficient algorithms for circuit optimization across multiple technology domains, with particular emphasis on area, power, and delay optimization while maintaining functional correctness. Professor De Micheli has supervised numerous doctoral students throughout his career at EPFL, mentoring over 40 PhD candidates whose research spans various aspects of electronic design automation, circuit design, and emerging technologies. His students have gone on to make significant contributions in both academia and industry. As Scientific Director of EcoCloud, Professor De Micheli leads research initiatives focused on energy-efficient computing systems and sustainable cloud infrastructure. His laboratory, the Integrated Systems Laboratory, serves as a hub for interdisciplinary research connecting computer science, electrical engineering, and materials science, with particular focus on next-generation computing technologies.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +6 more

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Michael MoorView profile

Assistant Professor

• Medical AI
• Machine Learning in Healthcare
• Causal Learning
• +3 more

Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.

Eleni ChatziView profile

Professor

• Structural Health Monitoring
• Nonlinear System Identification
• Damage Detection
• +7 more

Prof. Eleni Chatzi is a Full Professor and Chair of Structural Mechanics at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. She holds a PhD from Columbia University (2010) and has held roles from Assistant to Full Professor at ETH since 2010. Her research focuses on intelligent structural monitoring and data-driven asset management, emphasizing nonlinear dynamics and sensor integration. Affiliations : Institute of Structural Engineering, European Academy of Wind Energy (EAWE President), Swiss Community for Computational Methods (SWICCOMAS Chair) Research interests include Structural Health Monitoring (SHM), system identification, and advanced simulation tools. She pioneered work on data-driven diagnostics and self-aware infrastructure, supported by grants like the ERC Starting Grant (2015). Awards include the 2020 Walter L. Huber Prize and 2024 SHM Person of the Year Award. Her work spans wind energy infrastructure, metamaterials for vibration control, and AI-driven structural analytics. Over 600 publications and 200k+ citations highlight her impact. She teaches computational science and structural dynamics in ETH's programs and collaborates globally on sustainable infrastructure projects.

Karsten BorgwardtView profile

Professor

• Machine Learning
• Systems Biology
• Bioinformatics
• +3 more

Karsten Borgwardt is a Professor and Director of the Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry. He holds a PhD in Computer Science (2007) from LMU Munich and has held academic positions at ETH Zürich, Universität Tübingen, and the Max Planck Institutes in Tübingen. His research focuses on machine learning applications in biology and medicine, including biomarker discovery, personalized medicine, and systems biology. Education: PhD in Computer Science (2007), LMU Munich M.Sc. in Biology (2003), University of Oxford Diplom (M.Sc. equivalent) in Computer Science (2004), LMU Munich Research Interests: Development of machine learning algorithms for large biomedical datasets Pattern recognition in genomic and clinical data Applications in sepsis biomarkers, antimicrobial resistance prediction, and personalized medicine Grants & Projects: Scientific Coordinator of Marie Curie Networks (2013-2022) Swiss National Science Foundation Starting Grant (2014) Personalized Swiss Sepsis Study (CHF 5.3M, 2018) Awards: Krupp Award (2013), Golden Owl Teaching Award (2017), multiple 'Top 40 under 40' recognitions (2014-2016). Labs: Leads the Machine Learning and Systems Biology Department at MPI, collaborating with 22+ labs in sepsis research and international networks.

Olga FinkView profile

Assistant Professor

• Machine Learning
• Predictive Maintenance
• Industrial IoT
• +3 more

Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.

Andrea CavallaroView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +7 more

Andrea Cavallaro is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and Director of the Idiap Research Institute. He holds dual appointments in the School of Engineering (STI) within the Institute of Electrical Engineering and Measurements (IEM) and the School of Engineering's Education Unit (SEL-ENS). His research focuses on machine learning for multimodal perception, privacy-preserving AI, and autonomous systems. Cavallaro earned his PhD in Electrical Engineering from EPFL in 2002 and has held leadership roles including Director of Research at Queen Mary University of London and Turing Fellow at The Alan Turing Institute. Education: PhD in Electrical Engineering (EPFL, 2002) Leadership: Idiap Director, Affiliate at ELLIS Society Editorial Roles: Editor-in-Chief of Signal Processing: Image Communication (2020–2023), Senior Area Editor for IEEE Transactions on Image Processing Research Interests: Machine learning for audio-visual sensing, privacy in AI, autonomous systems perception, and ethical AI frameworks. Key projects include AlignAI (trustworthy AI alignment) and CORSMAL (multimodal object manipulation). Recent articles explore privacy-aware AI models, adversarial attacks, and multimodal perception systems. His work bridges theoretical advancements with practical applications in robotics, healthcare, and education. Awards include the Royal Academy of Engineering Teaching Prize and IAPR Fellowship. Teaching: Leads courses on deep learning ethics and multimodal AI at EPFL. Advising: Supervises 11 PhD students in areas like privacy-preserving algorithms and autonomous systems. Labs/Teams: Coordinates Idiap’s Audiovisual Intelligence and Learning Lab (LIDIAP) and collaborates on projects like GraphNEx (explainable AI via graph neural networks).

Gianni FranchiView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Uncertainty Quantification
• +2 more

Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.

• Machine Learning
• Bioinformatics
• Computational Biology
• +5 more

Jian Peng is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on computational biology, machine learning, and their applications to protein structure prediction, drug design, and molecular modeling. He has contributed to advancements in antibody engineering, protein-ligand docking, and generative models for biological systems. Key research areas include: Machine Learning for Molecular Modeling Protein Structure Prediction Antibody and Peptide Design Genomics and Single-Cell Analysis Structure-Based Drug Discovery His work emphasizes integrating deep learning techniques with biological datasets to address challenges in precision medicine, drug development, and systems biology. Notable achievements include developing the FastFold system to accelerate AlphaFold training and pioneering flow-based methods for antibody design. Awards include the Overton Prize (2020), recognizing contributions to computational biology. His research has been published in top journals and conferences, spanning topics from protein mutation prediction to geodesic-based immune complex modeling.

Kjell JornerView profile

Assistant Professor

• Digital Chemistry
• Computational Chemistry
• Machine Learning
• +5 more

Kjell Jorner is an Assistant Professor of Digital Chemistry in the Institute for Chemical and Bioengineering at ETH Zurich's Department of Chemistry and Applied Biosciences. His research group focuses on integrating computational methods and machine learning to address challenges in chemical synthesis, materials design, and reaction prediction. Education: PhD from Uppsala University (Photochemistry of aromatic compounds) Postdoctoral studies at AstraZeneca UK (Reaction prediction using computational chemistry and ML) Postdoctoral studies at University of Toronto (Molecular design of catalysts and organic electronic materials) Research Interests: Professor Jorner's work bridges computational chemistry, machine learning, and experimental design. Key areas include: Development of quantum mechanics-machine learning hybrid approaches for reaction feasibility prediction Inverse molecular design of functional materials (e.g., singlet-fission systems) Computational catalyst optimization and high-throughput screening methods Digital tools for chemical education and cheminformatics Publication Trends (2023-2025): Recent articles demonstrate a strong focus on machine learning applications in chemistry, including reaction prediction algorithms, catalyst design frameworks, and automated molecular generation. A recurring theme is the development of computational tools to accelerate materials discovery and optimize chemical processes. Laboratory & Team: Leads the Digital Chemistry research group at ETH Zurich (HCI E 137) exploring computational approaches to chemical challenges.

• Explainable AI
• Computational Argumentation
• Quantitative Bipolar Argumentation Frameworks
• +2 more

Xiang Yin is a Research Associate at the Department of Computing in Imperial College London , affiliated with the Computational Logic and Argumentation group (CLArg) . His work bridges Explainable AI (XAI) and Computational Argumentation (CA) , focusing on the explainability of Quantitative Bipolar Argumentation Frameworks (QBAFs) through attribution and counterfactual explanations. Research Interests: Explainable AI (XAI) Computational Argumentation Quantitative Bipolar Argumentation Frameworks Model Interpretability Human-AI Interaction Logical Reasoning for AI Publication Trends reveal a focus on argumentation-based explainability, with 2025-2024 works addressing large language models for claim verification, truth-discovery frameworks, and counterfactual explanations. Earlier works (2023-2022) explore random forest explanations, faithfulness criteria, and QBAF analysis. His 2018 publications on aircraft prediction systems demonstrate applied machine learning expertise. Education PhD in Artificial Intelligence under Prof. Francesca Toni and Dr. Nico Potyka Pre-PhD: Machine Learning R&D Engineer at Baidu Labs & Teams Xiang is part of the CLArg group at Imperial College London, focusing on integrating computational argumentation with AI explainability and contestability.

Daniel SageView profile

Lecturer

• Biomedical Imaging
• Image Processing
• Deep Learning Applications
• +5 more

Daniel Sage is a Lecturer and Scientific Advisor at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Biomedical Imaging Laboratory (LIB) under the College of Engineering (STI) and School of Life Sciences (SV) . He specializes in bioimage informatics , structured-illumination microscopy , and deep learning applications for biomedical imaging. His work spans algorithm development for single-molecule localization microscopy (SMLM) , fluorescence imaging , and 3D reconstruction . His research group has developed open-source tools like FlexSIM for light inhomogeneity correction, DeepImageJ for integrating deep learning in ImageJ, and Steer'n'Detect for orientation-accurate template detection. His publications focus on correcting multiple-blinking artifacts in PALM, optimal transport metrics for SMLM evaluation, and contextual feature analysis for xenograft cell classification. He mentors PhD students and contributes to interdisciplinary education through courses such as Bioimage Informatics and Fundamentals of Image Analysis , emphasizing practical software solutions and Java programming for bioimage processing. His collaborations include institutions like Howard Hughes Medical Institute and Centre National de la Recherche Scientifique (CNRS) .