ETH ZurichSwitzerland
Barbara Solenthaler is a Lecturer at the Department of Computer Science, ETH Zurich. Her research focuses on physics-based simulations, facial animation, and machine learning applications in computer graphics.
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Barbara Solenthaler is a Lecturer at the Department of Computer Science, ETH Zurich. Her research focuses on physics-based simulations, facial animation, and machine learning applications in computer graphics.
استاد پژوهش
Naonori Ueda is a Research Professor and Deputy Director at RIKEN Center for Advanced Intelligence Project. He also serves as a Visiting Fellow at NTT Communication Science Laboratories, Research Supervisor for Mathematical Information Platform at Japan Science and Technology Agency (JST), and Visiting Professor at Kobe University's Graduate School of System Informatics. His distinguished career spans academia, government research institutions, and industry collaboration, with significant contributions to advancing artificial intelligence and machine learning applications across multiple scientific domains. Dr. Ueda's research interests focus on the intersection of machine learning, artificial intelligence, and physical sciences. He specializes in physics-informed deep learning approaches that integrate governing physical equations with neural network architectures. His work spans geophysical data analysis, remote sensing applications, computational seismology, and environmental monitoring systems. He has pioneered methods for crustal deformation modeling, earthquake prediction, tsunami inundation forecasting, and satellite imagery analysis using advanced machine learning techniques. His research demonstrates how AI can solve complex scientific problems by bridging the gap between data-driven approaches and physical domain knowledge. His publication record reveals a strong trend toward applying machine learning to solve real-world geophysical and environmental challenges. His recent work shows increasing sophistication in physics-informed neural networks that incorporate domain-specific knowledge into deep learning architectures. The publications span high-impact journals like Nature Communications, demonstrating the interdisciplinary significance of his work. His research consistently focuses on practical applications of AI for disaster prevention, environmental monitoring, and scientific discovery. Fellow of IEICE (Institute of Electronics Information and Communication Engineers) Member of Japan Prize field review committee Selection Committee Member for Brilliant Female Research Award (The Jun Ashida Award) Member of Kyoto Prize Selection Committee Dr. Ueda has secured substantial research funding through multiple government-sponsored projects including RIKEN Pioneering Project 'Prediction Science,' JST AIP Acceleration Research projects on weather prediction and drug discovery, and AMED-funded medical research initiatives. His leadership extends to serving as Sub-project Director for Japan's Moonshot R&D Project. He actively mentors researchers through his roles at RIKEN, NTT, and various academic institutions, fostering the next generation of AI scientists. As Deputy Director of RIKEN Center for Advanced Intelligence Project, Dr. Ueda leads one of Japan's premier AI research initiatives. He also serves on the Advisory Board of Kobe University's Mathematical and Data Science Center and Kyoto University's Graduate School of Informatics. His leadership extends to coordinating the AI Seminar at Osaka Industrial Association and supervising the Keihanna 'Edison Society' at the International Institute for Advanced Studies, demonstrating his commitment to bridging academic research with industrial applications.
استادیار
Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow in the Department of Computing at Imperial College London. As the Principal Investigator (PI) of the CIRCLE group , his research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with theoretical interests in non-Euclidean inference and deep learning principles. Education: PhD and MSc in Computer Vision from Technical University of Munich (2018), BSc in Computer Science from Sabancı University (2008). Projects: PI for UKRI-EPSRC's UNTOLD (Topological Deep Learning), Royal Society's drug discovery initiative, and EPSRC's GNOMON (Generative Models in non-Euclidean Spaces). Leadership: Area Chair for CVPR, ICCV, and 3DV 2025 Publication Chair. His work bridges differential geometry, algebraic topology, and deep neural networks, with applications in quantum computer vision, 3D/4D generative priors, and medical imaging. Key contributions include novel frameworks for rotation forecasting, graph generation, and topological generalization bounds. Scientific Awards: UKRI Future Leaders Fellowship EMVA Young Professional Award
Jocelyn Chanussot is a Professor at Grenoble Institute of Technology, holding the AXA Chair of Remote Sensing. He is affiliated with GIPSA-Lab (Laboratoire de traitement du Signal et des Images) at Ense3 (École Nationale Supérieure de l'Énergie, l'Eau et l'Environnement) and maintains a connection with the Chinese Academy of Sciences through his AXA Chair position. His research focuses on advancing remote sensing technologies, particularly in hyperspectral imaging and artificial intelligence applications for environmental monitoring. Professor Chanussot specializes in hyperspectral imaging, which captures information across several hundred wavelengths, allowing for detailed characterization of physical properties in observed scenes. His work develops algorithms to extract meaningful information from complex remote sensing data, with applications spanning natural disaster monitoring, environmental observation, biodiversity assessment, and urban planning. He has pioneered approaches that leverage artificial intelligence, particularly deep learning techniques, to process and analyze large-scale remote sensing datasets. His scholarly output shows a clear trend toward integrating advanced AI techniques with remote sensing data. Recent publications demonstrate growing emphasis on transformer architectures, contrastive learning, generative models, and foundation models specifically adapted for hyperspectral data. His work increasingly addresses multimodal data fusion, anomaly detection, and real-time processing for applications in natural disaster response. Ranked among the 157 most cited French researchers in 2019 by Clarivate Analytics Professor Chanussot actively serves on numerous conference program committees, particularly for SPIE's Image and Signal Processing for Remote Sensing conferences. His research is supported by the AXA Research Fund through his AXA Chair in Remote Sensing, which focuses on developing algorithms for natural disaster monitoring and emergency response. He collaborates extensively with international institutions including UCLA and Stanford University on applications ranging from toxic gas detection to tropical forest biodiversity assessment. He leads research at GIPSA-Lab, focusing on developing advanced tools and algorithms for extracting information from complex remote sensing data. His team works on processing heterogeneous data including aerial photos, multispectral and hyperspectral images, and other environmental measurements to improve natural disaster prediction and response capabilities.
Curdin Derungs is a Lecturer in Data Science at the Lucerne School of Computer Science (HSLU), Switzerland. He specializes in applying statistical and machine learning techniques to energy systems, spatial analysis, and natural language processing. His professional competencies include data-driven automation, deep learning, time series analysis, and energy systems optimization. Derungs holds a PhD in Natural Sciences (University of Zurich, 2013) with a focus on NLP and spatial analysis, and a DAS in Applied Statistics (ETH Zurich, 2019). Earlier degrees include a Master's in Geography (2008) and Atmospheric Physics (ETH Zurich, 2008). His research explores intersections between data science and environmental sustainability, including energy efficiency optimization, spatial language dynamics, and landscape modeling. Recent work focuses on occupant behavior in energy systems, geotagged text analysis, and soil formation modeling. Key projects include SCCER FEEB&D Work Package 2 on renewable energy systems, the Romande Energy Demonstrator, and urban greening studies. He actively contributes to interdisciplinary initiatives like the IGE Innovationswettbewerb 2019 and Innovationspark Zentralschweiz.
پژوهشگر
Dr. Manuel Dahmen is the Head of Department at the Institute of Climate and Energy Systems (ICE-1) within the Research Center Jülich. His research focuses on designing sustainable and cost-efficient energy systems through advanced techniques like numerical optimization and deep learning. Key areas include renewable energy integration, process network analysis, and machine learning-driven system design. His work addresses challenges in decarbonizing industries, optimizing energy systems, and developing tailor-made fuels for high-efficiency engines. Dahmen leads interdisciplinary projects combining computational methods with practical engineering solutions, emphasizing robust design under uncertainty. He contributes to open-source tools like COMANDO for energy systems optimization and explores innovations in graph neural networks for molecular property prediction. Research trends reflected in his publications highlight optimization algorithms (e.g., semi-infinite programming), demand response strategies for industrial processes, and physics-informed machine learning for dynamic systems. Dahmen’s contributions bridge theoretical advancements with real-world applications, aiming to accelerate the transition to sustainable energy systems.
Jan S. Hesthaven is a Professor and Provost at EPFL, leading academic affairs. He holds a Master's from the Technical University of Denmark (DTU) and a PhD in Numerical Analysis, followed by an honorary dr.techn degree from DTU. His research focuses on high-order computational methods for wave problems, reduced order models, and machine learning integration. He has co-authored over 175 papers and 4 monographs. Previously, he served as Dean of the School of Basic Sciences at EPFL and held roles at Brown University, including Director of the Center for Computation and Visualization. Awards include the Alfred P. Sloan Fellowship and the Philip J. Bray Award. Education: Master of Science in Computational Physics, DTU (1991) PhD in Numerical Analysis, DTU (1995) dr.techn in Computational Mathematics, DTU (2009) Research Interests: Development of high-order numerical methods, computational wave propagation, geophysical flows, and machine learning applications in scientific computing. His work bridges traditional methods with AI-driven approaches for real-time modeling and structural health monitoring. Recent Work: His 2023–2025 publications emphasize machine learning-enhanced models, reduced order methods, and seismic data analysis for environmental applications. Key techniques include physics-informed neural networks and graph-based operator learning. Awards: Alfred P. Sloan Fellowship (2000) NSF Career Award (2002) Philip J. Bray Award (2004) Dr.techn from DTU (2009) Grants & Leadership: Led the Center for Computation and Visualization (CCV) at Brown (2006–2013) and co-directed the NSF Institute ICERM (2010–2013). Current roles include Provost at EPFL and leadership in MATHICSE. Collaborates with industry and applied scientists on computational challenges. Labs & Teams: Active in the MATHICSE lab, focusing on numerical methods and high-performance computing. Involved in interdisciplinary projects combining AI with traditional computational science.
استادیار
Flavio Donato is an Assistant Professor of Neurobiology at the Biozentrum of the University of Basel, Switzerland, where he leads a research group investigating the structural and functional maturation of neural networks related to orientation and memory. His work focuses on how abstract thinking develops throughout life, with particular emphasis on neurons in the entorhinal cortex and hippocampus that create the brain's 'cognitive map' for navigating physical and conceptual spaces. Nationality: Italian Position: Assistant Professor of Neurobiology since 2019 Education: PhD in Neurobiology from University of Basel (Summa Cum Laude) Research Focus: Neural network development, memory formation, spatial cognition Donato's research program examines how neural networks supporting cognitive functions emerge during development. His laboratory conducts longitudinal studies visualizing, recording, and manipulating neural activity across multiple life stages of animals. Using approaches from genetics, quantitative behavioral analysis, in vivo imaging, viral tracing, computational modeling, and optogenetics, his team identifies stereotypical patterns of neural activity associated with specific behaviors. A key aspect of his work explores sensitive periods for neurodevelopmental disorders, investigating how traumatic experiences in children may increase susceptibility to mental illness later in life. His recent publications reveal trends in developmental neuroscience with a strong focus on hippocampal-entorhinal circuitry, memory formation mechanisms, and the ontogeny of cognitive functions. His research spans from molecular mechanisms of synapse formation to systems-level understanding of spatial representation and memory dynamics during development. Eppendorf & Science prize for neurobiology of 2017 (Grand Winner) for essay 'Assembling the brain from deep within' EMBO Long-term fellowship (2013-2015) As a principal investigator, Donato mentors PhD students including Vilde Kveim, who recently received the Shepherd prize for her PhD work. His lab, Donatolab, investigates how the brain stores multiple 'copies' of events as revealed in his August 2024 study. Donato is also active in the FENS-Kavli Network of Excellence, contributing to broader neuroscience discourse and policy discussions about the future of the field. His research group employs cutting-edge methodologies to study neural development from birth to functional maturity, with implications for understanding neurodevelopmental disorders and developing targeted therapeutic interventions based on deciphering mechanisms of neurotypical cognitive development.
Mirko Birbaumer is a Professor at the Lucerne School of Engineering and Architecture (HSLU), specializing in data science and machine learning applications. He holds a PhD in computer-assisted image analysis from ETH Zurich and has completed advanced executive education at MIT in Innovation and Strategy. PhD in Systems Biology (ETH Zurich, 2010) Executive Certificate in Innovation and Strategy (MIT, 2022) Advanced Executive Certificate in Innovation, Strategy, and Technology (MIT, 2024) His research integrates statistical data analysis , computer vision , and digital health , with applications spanning industrial quality control, medical imaging, and environmental monitoring. Recent work focuses on physics-informed neural networks, interpretable anomaly detection, and causal machine learning. His projects include AI-driven solutions for medical diagnostics, predictive maintenance in manufacturing, and environmental anomaly detection. He has supervised over 20 student theses on topics like deep learning for knee osteoarthritis detection, monocular depth estimation, and AI-assisted logo similarity analysis. He leads the Data Science profile in the Master of Science and Engineering program and teaches courses on deep learning, Bayesian machine learning, and computer vision. His methodological expertise includes agent-based modeling, statistical analysis, and explainable AI.
دانشگاهی
Adway Girish is a third-year Ph.D. candidate and Doctoral Assistant at the School of Computer and Communication Sciences (IC), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is affiliated with the Information Processing Group (IPG) and the Laboratory for the Theory of Information (LTHI), working under the supervision of Prof. Emre Telatar. He has also collaborated with leading researchers including Michael Gastpar, Hyeji Kim, and Shlomo Shamai. His educational background includes a B.Tech. in Electrical Engineering with honors and a minor in Mathematics from the Indian Institute of Technology Bombay (IITB), completed in 2022. Adway's research centers on information theory and its applications in communication, security, and machine learning. He is particularly interested in foundational aspects of information measures, entropy optimization, and the theoretical underpinnings of modern deep learning architectures such as transformers and large language models. His recent work explores rate-distortion frameworks for prompt compression, learning dynamics in transformers, and entropy-constrained communication channels. His research bridges theoretical rigor with practical implications in AI and communication systems. The trend in his recent publications shows a shift from classical signal processing and micro-Doppler analysis during his undergraduate years to advanced topics in information theory and machine learning during his Ph.D. His contributions are published in top-tier venues such as ISIT, NeurIPS, ICLR, and ICML workshops, indicating a strong trajectory in theoretical computer science and applied mathematics. Scientific Awards and Recognitions: ICLR 2025 Spotlight Paper (awarded to top 5% of accepted papers) Oral presentation at ICML 2024 Workshop on Theoretical Foundations of Foundation Models (selected as one of top 4 out of 58 submissions) Adway advises no students currently, as he is himself a doctoral candidate. However, he plays an active role in collaborative research projects involving multiple co-authors across institutions. He has not received specific mention of external grants in the provided text, but his position as a Doctoral Assistant at EPFL suggests institutional funding. His collaborations with renowned researchers suggest involvement in larger research initiatives and potential access to grant-supported projects. He is a core member of the Information Processing Group (IPG) at EPFL, a research lab focused on theoretical and applied aspects of information science, including coding, communication, learning, and data analysis. The group fosters interdisciplinary research and hosts regular seminars, candidacy reviews, and internal presentations, all of which Adway actively participates in.
Mahdi Jafari Siavoshani is an Assistant Professor in the Department of Computer Science and Engineering at Sharif University of Technology, where he leads the Information, Network, and Learning (INL) Lab. He holds a PhD and MSc from the Swiss Federal Institute of Technology (EPFL), and a BSc in Electrical Engineering and Physics from Sharif University of Technology. Prior to his current role, he was a postdoctoral fellow at the Institute of Network Coding, The Chinese University of Hong Kong. His research spans fundamental problems in information processing, transmission, and analysis, with a focus on theoretical limits and practical algorithm design. Key areas include Machine Learning, Optimization, Data Science, Information and Communication Theory, and Computer Networks. His work integrates probabilistic modeling, network coding, and deep learning to address challenges in secure communication, traffic classification, and distributed systems. The recent publications highlight a strong trend in network coding, information theory, and machine learning applications. His work explores coding-theoretic solutions for secrecy, load balancing in cache networks, belief propagation algorithms, and encrypted traffic classification using deep learning. These contributions reflect a blend of theoretical depth and practical relevance in modern communication systems. Assistant Professor, Department of Computer Science and Engineering, Sharif University of Technology PhD and MSc, Swiss Federal Institute of Technology (EPFL) BSc in Electrical Engineering and Physics, Sharif University of Technology Postdoctoral Fellow, Institute of Network Coding, The Chinese University of Hong Kong He advises research in the INL Lab and teaches courses such as Computer Networks, Engineering Probability and Statistics, Network Coding, and Stochastic Processes. While no formal students are listed, his lab environment fosters collaborative research. There is no mention of external grants or funding in the provided texts. The INL Lab serves as the primary research unit, focusing on information theory, network algorithms, and machine learning applications. The lab integrates theoretical research with practical implementations, particularly in networked systems and data analysis.
پژوهشگر
Jonathan Dong is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) within the School of Engineering. He works at the Biomedical Imaging Laboratory (LIB), focusing on advanced imaging techniques and computational models. PhD Students: Hu Zhiyuan, Liu Yan His research spans biomedical imaging, computational optics, and machine learning applications in imaging inverse problems. Recent work emphasizes phase retrieval, optical reservoir computing, and quantum information in microscopy. Key article trends show expertise in MRI classification , optical tomography , deep learning for imaging, and scattering media analysis . Collaborations include technical development in optoacoustics and super-resolution microscopy. Contact: jonathan.dong@epfl.ch | Office: BM 4141, EPFL, Station 17, Lausanne
Matthieu Wyart is a Full Professor of Theoretical Physics at École polytechnique fédérale de Lausanne (EPFL), holding a position in the School of Basic Sciences within the Institute of Physics. He leads research in the Physics of Complex Systems Laboratory (PCSL) at EPFL, where he investigates fundamental questions at the intersection of condensed matter theory, statistical mechanics, and emerging connections to machine learning. Wyart completed his education at prestigious French institutions, earning his physics degree with Honors from École Polytechnique in Paris in 2001, followed by a Diploma of Advanced Studies in Theoretical Physics with highest Honors from École Normale Supérieure, Paris in 2002. He obtained his doctoral degree in Theoretical Physics and Finance from SPEC, CEA Saclay, Paris in 2006 with a thesis on electronic markets. His academic journey included postdoctoral positions at Harvard University, Janelia Farm, and Princeton University before joining New York University as an Assistant Professor in 2010, where he was promoted to Associate Professor in 2014. He moved to EPFL in July 2015 as an Associate Professor of Theoretical Physics and was promoted to Full Professor in April 2024. His research spans multiple domains including condensed matter theory, statistical mechanics, quantum information, and biophysics, with particular focus on disordered systems, glass transitions, amorphous solids, and the emerging connections between physical systems and machine learning architectures. Wyart's work often reveals deep theoretical connections between seemingly disparate fields, such as demonstrating how principles governing amorphous materials relate to the behavior of neural networks. His recent publications explore hierarchical structures in data, diffusion models, learning curves for compositional data, and the physics of creep in disordered media. Through his laboratory (PCSL), Wyart fosters interdisciplinary research that bridges traditional physics with contemporary challenges in machine learning and complex systems. His work has established important theoretical frameworks for understanding the glass transition, jamming phenomena, and the geometric principles underlying both physical and artificial learning systems.
Michel Besserve is a Full Professor in the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges artificial intelligence, causal inference, and neuroscience to develop trustworthy and interpretable AI systems for understanding complex phenomena in artificial, physical, and socioeconomic systems. Dr. Besserve completed his PhD dissertation titled Analyse de la dynamique neuronale pour les Interfaces Cerveau-Machine : un retour aux sources at Université Paris-Sud 11 in November 2007. His academic journey has led him to become a leading researcher in causal machine learning, collaborating extensively with Bernhard Schölkopf and other prominent scientists in neuroscience and AI. Professor Besserve's research centers on causal machine learning, with a focus on understanding and anticipating changes in complex systems. He investigates principles like the Independence of Causal Mechanisms (ICM) to improve causal model identifiability and develop more robust AI. His work spans theoretical foundations of causal inference to practical applications in neuroscience, brain function analysis, and socioeconomic systems. He has made significant contributions to understanding brain networks through causal inference and machine learning, with publications in major journals including Nature, PLOS Biology, and Neuron. His publication record reveals a clear trajectory from theoretical causal inference toward developing frameworks for real-world applications. Recent work focuses on building Causal Computational Models (CCMs) that integrate data, domain knowledge, and causal structure to improve robustness and interpretability of complex system models. His research shows increasing integration of causal machine learning with applications to neuroscience and socioeconomic systems, particularly in developing causal AI that can address real-world complexity while producing interpretable outcomes for decision makers. Through his leadership in the Department of Empirical Inference, Professor Besserve has established a research program that bridges theoretical machine learning with practical applications in neuroscience and complex systems. His team develops novel causal machine learning tools that uncover internal structure and transformations of complex systems, with potential applications ranging from brain function analysis to sustainable economic modeling.
Dr. Olga Taran is a Lecturer at the Department of Health Sciences and Technology at ETH Zurich, specializing in machine learning and biomedical data science. She holds a PhD in Computer Science from the University of Geneva, with a focus on machine learning for complex problems. Her postdoctoral research at the Biomedical Data Science (BMDS) Lab involves developing data-driven metrics to improve outcomes in Spinal Cord Injury (SCI) clinical trials, addressing challenges in treatment evaluation. Her work aims to enhance clinical decision-making and accelerate therapy development. Olga's research also extends to anti-counterfeiting technologies using machine learning, focusing on copy detection patterns and authentication systems. Her research interests span machine learning applications in healthcare, deep learning methodologies, and the integration of digital twins for fraud detection. She has contributed to advancements in radio astronomical image reconstruction and self-supervised learning techniques. Olga's publications reflect a strong emphasis on interdisciplinary solutions, combining machine learning with fields like astronomy, cybersecurity, and biomedical science. Notable achievements include pioneering work on authentication systems using digital blueprints and physical fingerprints, as well as stochastic digital twin models for copy detection patterns. She actively collaborates on projects addressing the limitations of traditional clinical trial metrics and exploring machine learning's role in combating adversarial attacks on authentication systems.
