Andrea Dunbar is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Management of Technology and Entrepreneurship Institute - Management Department . Her research bridges technical domains like machine learning and biomedical signal processing. Her work focuses on spiking neural networks , photometric stereo , and respiratory rate estimation , with publications in venues like CVPR and ICASSP. She has supervised EPFL PhD student Sepehri Yamin .
Raphaël Butté is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences and the Institute of Physics. He leads research at the Laboratory of Advanced Semiconductors for Photonics and Electronics (LASPE) and contributes to the School Council SB and Physics Doctoral Program committees. Institution: EPFL Division: School of Basic Sciences (SB) Lab: LASPE Editorial roles: Associate Editor for Physical Review Research (2024), Editorial Board Member (2019–2024) Research Focus : His work centers on III-nitride semiconductors for nanophotonics, including polariton lasers, photonic crystals, and single-photon emitters. Key achievements include room-temperature polariton lasing, defect analysis in GaN, and nonlinear optical devices. Scientific Recognition : APS Outstanding Referee (2012) Outstanding Reviewer Award for Applied Physics Express (2021) ANR committee CE57 member (2025)
Henning Stahlberg is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences, Institute of Physics, and also holds a Professor ad personam position at the University of Lausanne in the Department of Fundamental Microbiology, School of Medicine and Biology. He leads the Laboratory of Biological Electron Microscopy (LBEM), a joint research unit of EPFL and UNIL, and serves as Academic Director of the Dubochet Center for Imaging. He previously held leadership roles at the University of Basel and UC Davis. PhD in Chemistry, EPFL (1997) Habilitation in Structural Biology, University of Basel (2002) Diploma in Solid State Physics, Technical University of Berlin (1993) His research centers on high-resolution cryo-electron microscopy to study neurodegenerative diseases such as Parkinson’s and Multiple System Atrophy, with a focus on alpha-synuclein fibrils and Lewy pathology. He also pioneers advancements in electron microscopy technology, including 4D-STEM ptychography, automated data analysis software (2dx, FOCUS, DYNAMO), and microfluidic sample preparation (CryoWrite). His work integrates physics, biology, and engineering to push the limits of structural imaging. The most recent publications highlight innovations in low-dose cryo-ptychography, microfluidic isolation of proteins, and atomic-level structures of disease-related fibrils. His research spans from fundamental biophysics to clinical neuropathology, using multimodal imaging techniques like correlative light and electron microscopy (CLEM) and cryogenic X-ray nanotomography. He has developed widely used software tools that automate and streamline cryo-EM data processing workflows. Henning Stahlberg mentors several PhD students and has supervised multiple doctoral theses at EPFL. His leadership extends to directing major imaging centers and fostering collaborations across physics, medicine, and bioengineering. He is actively involved in teaching courses such as 'Physics of Life' and practical lab instruction. His lab has developed key technologies now commercialized through CryoWrite AG, and he continues to lead cutting-edge research in structural biology and imaging instrumentation. The group is supported by both EPFL and the University of Lausanne, reflecting its interdisciplinary nature.
Christian Gabriel Theiler is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Swiss Plasma Center (SPC) under the School of Basic Sciences . He leads research on tokamak boundary physics and detachment dynamics , contributing to projects like the TCV Tokamak and Toroidal Fusion . He also serves as a Teaching Assistant for physics courses and chairs the PhD Program Committee in Physics . Research Focus : Tokamak boundary physics, plasma turbulence, divertor design, and diagnostic techniques. Awards : IAEA Nuclear Fusion Award (2020) SNSF Eccellenza Grant (2019-2023) EUROfusion Enabling Research Grant (2019-2020) Young Scientist Award (IUPAP) (2015) Key Collaborations : MIT, EUROfusion, SNSF, and ASDEX Upgrade teams. Students : Supervises multiple PhD candidates and has mentored past students in plasma physics. His recent research trends emphasize detachment physics in advanced divertor configurations, plasma shaping effects on turbulence, and machine learning for filament tracking. Publications span topics like parallel flows , SOLPS-ITER validation , and neutral interactions in fusion plasmas. Teaching includes General Physics and Plasma Physics courses.
Anders Meibom is a Full Professor at both École Polytechnique Fédérale de Lausanne (EPFL) and the University of Lausanne. At EPFL, he is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), where he leads the Laboratory for Biological Geochemistry (LGB). He also holds a professor ad personam position at the Institute of Earth Sciences, University of Lausanne. His roles include teaching in Environmental Sciences and Engineering and serving on the CDH Academic Evaluation Committee. His research focuses on Environmental Bio-Geo-Chemistry , sub-cellular stable isotope imaging , and biomineralization in marine systems. He is a pioneer in the use of NanoSIMS for high-resolution isotopic imaging, enabling breakthroughs in understanding cell metabolism and biogeochemical processes . His work bridges disciplines such as geochemistry, biology, and physics. His recent publications span fields including isotope geochemistry, cosmochemistry, marine biology, and analytical method development. The integration of NanoSIMS with biological systems has led to innovative studies on coral calcification, microbial metabolisms, and early solar system materials. No scientific awards listed in the provided text. Anders Meibom has supervised numerous PhD students at EPFL, including current and past advisees such as Hal Hunt Jones II, Jonathan Paul Sauder, and several others who have completed their doctoral work. He teaches core courses like Introduction to Environmental Engineering and leads interdisciplinary innovation initiatives such as the SKIL Student Kreativity and Innovation Laboratory . He has not been associated with any major grants in the text, but his leadership of national facilities (e.g., French NanoSIMS lab) indicates substantial research support. He leads the Laboratory for Biological Geochemistry (LGB) at EPFL, a multidisciplinary research team focused on applying advanced analytical techniques like NanoSIMS to biological and environmental systems. The lab fosters collaborations across earth sciences, life sciences, and engineering disciplines.
Paolo Perona is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Laboratory of Environmental Hydraulics (PL-LCH). He holds the title of Professeur titulaire and serves as the Academic Director of the PL-LCH. His roles also include membership in the ENAC Faculty Council. His research focuses on the eco-morphodynamics of rivers, integrating fluid mechanics, ecology, and environmental engineering. Key areas include riparian vegetation dynamics, sediment transport, hydropower management, and sustainable water allocation. He examines how vegetation interacts with flow and sediment, particularly in restored river corridors and alpine environments. His recent publications reveal a strong trend in experimental and modeling work on vegetation uprooting, flow-vegetation interactions, environmental flow optimization, and the ecological impacts of hydropower. These studies span disciplines such as hydrology, geomorphology, ecology, and environmental engineering, often using a combination of laboratory experiments, field data, and stochastic modeling. Scientific Awards: No specific awards were mentioned in the provided text. Perona has advised several PhD students, including Katharina Maria Edmaier, Lorenzo Gorla, Amin Niayifar, and Samuel Vorlet. He teaches courses such as Water Resources Engineering and Management, River Eco-Morphodynamics and Bioengineering, and Irrigation and Drainage Engineering. He leads a research group focused on environmental hydraulics and river restoration, contributing to both fundamental science and practical water management solutions.
Parsa Rahimi Noshanagh is a Doctoral Assistant and PhD student at the School of Engineering , École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Department of Electrical Engineering and the LIDIAP laboratory (Idiap Research Institute) . He is conducting research in synthetic data generation and its applications in computer vision under the supervision of Prof. Marcel and Prof. Alahi. Master of Science in Electrical Engineering, Sharif University of Technology Currently pursuing Doctoral Program in Electrical Engineering, EPFL His research focuses on enhancing discriminative models such as classifiers using synthetic data generated by generative models (e.g., StyleGANs, Diffusion models, Flows). He explores the concept of Analysis by Synthesis , aiming to make generative models practically useful in real-world applications beyond entertainment. His work investigates realism transfer in 3D face renderings and synthetic data augmentation to improve face recognition accuracy. His recent publications have been accepted at top conferences including ECCVW 2024 (ORAL) , ICASSP 2024 , and IJCB 2023 (ORAL) , demonstrating performance gains in benchmarks like IJB-C, IJB-B, and LFW. These works highlight that synthetic augmentation can rival architectural improvements in model performance. Paper accepted as ORAL presentation at ECCVW 2024 Paper accepted at ICASSP 2024 Paper accepted as ORAL presentation at IJCB 2023 Parsa previously worked as a Lead Research Engineer at MCI before starting his PhD. He has contributed to the release of datasets such as RealDigiFace and plans to release synthetic datasets from AugGen. He does not currently supervise any students but is actively involved in research collaborations with the Biometric Group at Idiap. He is a member of the Doctoral Program in Electrical Engineering (EDEE) at EPFL and contributes to advancing privacy-conscious and resource-efficient machine learning through self-contained synthetic data generation.
Deniz Sayin Mercadier is a Doctoral Assistant at the Computer Vision Laboratory (CVLAB) within the College of Engineering at École Polytechnique Fédérale de Lausanne (EPFL). They are affiliated with both the doctoral program in Computer and Communication Sciences (EDIC) and the CVLAB staff, focusing on research in computer vision and related fields. Their work involves collaboration with the IC-IINFCOM department. Research Interests: Computer Vision, Machine Learning, Image Processing Contact: deniz.mercadier@epfl.ch | Office: BC 300, EPFL
Haoqi Wang is a Doctoral Assistant at the Computer Vision Laboratory (CVLAB) at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the Institute of Electrical Engineering (IINFCOM). He is also a doctoral student in the Doctoral Program in Computer and Communication Sciences (EDIC) at EPFL. His research is centered in the field of computer vision, with likely interests in machine learning, deep learning, and image processing, as inferred from his association with CVLAB, a prominent research laboratory in visual computing. No scientific awards or publications were listed in the provided text. However, as a Doctoral Assistant, he is actively engaged in research and academic training. There is no indication of advising students or managing grants at this stage. He is based in office BC 301 and can be contacted at haoqi.wang@epfl.ch.
Zhen Wei is a Doctoral Assistant and PhD student at École polytechnique fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) within the School of Computer and Communication Sciences (IC) and the Institute of Computer Science (IINFCOM). He is actively engaged in research and holds a formal staff position at the university. His research interests span Computer Vision , Machine Learning , Deep Learning , and Artificial Intelligence , with additional expertise in Multidisciplinary Design Optimization and Aerospace Engineering , as evidenced by his awards. His work is conducted primarily within the CVLAB, a leading research group in vision and machine learning. Zhen Wei has been recognized with prestigious awards including the AIAA Multidisciplinary Design Optimization Best Paper Award and the AIAA Aviation Forum Best Student Paper Award in Multidisciplinary Design Optimization. He is also a recipient of the Toulouse Graduate School of Aerospace Engineering (TSAE) Scholarship. As a doctoral researcher, he contributes to academic advising and research projects within CVLAB. Though no formal advisees are listed, his role involves collaborative research and technical contributions to the lab’s scientific output. He is also Treasurer of the Chinese Students & Scholars Association Lausanne (CSSA), demonstrating leadership in student community engagement. His research is supported through institutional affiliation with EPFL and scholarship funding. He is based at BC 303, Building BC, Station 14, 1015 Lausanne, Switzerland, and is actively involved in both academic and student life at EPFL.
Yitao Xu is a Doctoral Assistant and PhD student at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC), the Institute of Electrical Engineering (IINFCOM), and the Image and Visual Representation Laboratory (IVRL). He is pursuing his doctoral studies in the EDIC program under the EDOC doctoral school. His research focuses on computer vision and visual representation , with broader interests in machine learning , deep learning , image processing , and artificial intelligence . These interests are inferred from his affiliation with the IVRL lab, a leading research group in visual computing. No publications are listed in the provided text, so no trends can be identified at this time. Awards: No scientific awards mentioned. As a Doctoral Assistant, Yitao Xu likely contributes to research and teaching activities within IVRL. There is no mention of grants or formal student advising in the provided information. He is actively involved in the Image and Visual Representation Laboratory (IVRL) at EPFL, a research group dedicated to advancing the state of the art in visual computing, including image understanding, representation learning, and multimodal AI.
Dimitri Van De Ville is a Full Professor of Bioengineering at École Polytechnique Fédérale de Lausanne (EPFL) and the University of Geneva (UniGE), affiliated with the School of Engineering at EPFL and the Faculty of Medicine at UniGE. He leads the Medical Image Processing Laboratory (MIP:Lab) at Campus Biotech in Geneva and is a key figure at the CIBM Center for Biomedical Imaging. His work bridges signal processing, computational neuroscience, and clinical neuroimaging. Education: M.S. and Ph.D. in Computer Science, Ghent University, Belgium (1998, 2002) Post-doctoral Fellow, Biomedical Imaging Group, EPFL (2002–2005) His research focuses on advancing non-invasive brain imaging through methodological innovations in signal and image processing. He investigates the dynamic and network aspects of brain function using fMRI and EEG, with a special emphasis on dynamic functional connectivity, graph signal processing, and real-time neurofeedback. His work has demonstrated that EEG microstate sequences exhibit scale-free dynamics, linking fast electrophysiological events to slow hemodynamic changes. He pioneered connectivity decoding and contributed to the development of sparsity-based deconvolution methods for fMRI. The recent articles reflect a strong trend toward modeling brain function as a dynamic network process. Key themes include graph signal processing on brain connectomes, decomposition of transient brain activity, and the use of machine learning to decode brain states. His work increasingly integrates structural and functional data to understand brain organization at multiple scales. Scientific Awards: Technical Achievement Award, IEEE EMBS (2024) Fellow, EURASIP (2023) Distinguished Lecturer, IEEE Signal Processing Society (2021–2022) Fellow, IEEE (2020) Leenaards Award (2016) NARSAD Independent Investigator Award (2014) NeuroImage Editors' Choice Award (2013) Pfizer Research Award (2012) Van De Ville has secured substantial research funding through grants such as the SNSF Professorship and has advised numerous researchers. He plays a major role in the scientific community as founding chair of the EURASIP BISA SAT and former chair of the IEEE BISP TC. He has held editorial roles in top journals including IEEE Transactions on Signal Processing , SIAM Journal on Imaging Sciences , and Imaging Neuroscience . He leads the Medical Image Processing Laboratory (MIP:Lab) at Campus Biotech, which specializes in developing advanced signal processing tools for neuroimaging. The lab is part of a broader collaborative ecosystem involving EPFL, UniGE, and the CIBM, fostering interdisciplinary research in biomedical imaging and brain science.
Prof. Dr. Ivan Dokmanić is an Associate Professor for Data Analytics at the Department of Mathematics and Computer Science, University of Basel, where he leads the Research Group Signal and Data Analytics. His work bridges data science, signal processing, and physics to address challenges in computational imaging, acoustics, and audiovisual reasoning. His research focuses on developing principled machine learning and signal processing techniques grounded in physical models. Key interests include: Signal Processing (especially scientific and wave-based) Scientific Machine Learning (SciML) Inverse Problems and Imaging Computational Acoustics and Audio Seismic Signal Processing Dokmanić is the principal investigator of the ERC Starting Grant project SWING (Signals, Waves, and Learning), which aims to create a data-driven framework for wave-based inverse problems. This project underscores his leadership in integrating physical models with modern learning techniques. He has received significant recognition through competitive funding, most notably the ERC Starting Grant, highlighting the innovative and high-impact nature of his research. While no detailed list of publications is provided in the text, his work appears to trend toward interdisciplinary methodologies combining physics, signal processing, and machine learning for scientific discovery. Dokmanić is actively involved in research and academic leadership. He supervises a research group and contributes to the academic mission of the University of Basel. Although specific advisees are not listed, his group likely includes PhD and master’s students working on advanced topics in data analytics and signal processing. He has secured substantial grant funding, indicating active research projects and collaboration opportunities. He is affiliated with the Research Group Signal and Data Analytics, which serves as the hub for his interdisciplinary investigations into wave phenomena, inverse modeling, and data-driven scientific computing.
Prof. Pier Luigi Dragotti is a Professor of Signal Processing in the Department of Electrical and Electronic Engineering at Imperial College London, Faculty of Engineering. He leads the Communications, Signal Processing, and Control (CSP) research group and is actively engaged in both theoretical and applied research in signal processing and computational imaging. His research focuses on wavelet theory, sampling theory, sparse signal processing, computational imaging, and data-driven image processing . He integrates classical signal processing frameworks with modern deep learning techniques, particularly in solving inverse problems and image restoration. His work bridges model-based algorithms with data-driven approaches, enabling breakthroughs in fields such as art conservation and neuroscience imaging. The recent publications highlight a strong trend towards invertible neural networks, physics-informed deep learning, and hybrid model-data methods for image restoration, microscopy, and cultural heritage analysis. His team develops algorithms that are not only accurate but also interpretable and grounded in physical models. Scientific Awards and Recognitions: Editor-in-Chief of IEEE Transactions on Signal Processing (2018–2020), Certificate of Merit Supervised students winning: Eurasip 3M Thesis Competition, IEEE MMSP Best Paper Award, Outstanding PhD Thesis Awards (2020–2022), Ivor Tupper Prize Student Adam won Gold at the Commonwealth Games Advising and Grants: Prof. Dragotti has successfully supervised numerous PhD and MSc students, many of whom have gone on to achieve significant recognition. His group has secured funding that supports interdisciplinary research in imaging science, and several of his former students have launched innovative projects, including a start-up company. He fosters a dynamic research environment with strong industry and cross-institutional collaborations. Labs and Research Groups: He is a key member of the CSP (Communications, Signal Processing, and Control) group at Imperial College London, where he leads a vibrant team working on cutting-edge signal processing challenges. The group is known for its contributions to sampling theory, wavelets, and the integration of deep learning in imaging systems.
Ekansh Sareen is a Doctoral Assistant at the Medical Image Processing Laboratory (MIPLAB) within the School of Engineering at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is concurrently enrolled in the Doctoral Program in Electrical Engineering (EDEE) under the Doctoral School (EDOC), reflecting his dual role as a researcher and PhD candidate. His work is based at Campus Biotech in Geneva, where MIPLAB conducts cutting-edge research in biomedical imaging and computational methods for healthcare applications. His research interests lie at the intersection of engineering and medicine, with a focus on medical image processing , signal processing , and machine learning . These areas are central to developing automated tools for disease detection, image segmentation, and diagnostic support systems. Given his affiliation with MIPLAB, his work likely involves deep learning models applied to MRI, CT, or ultrasound data, contributing to advancements in precision medicine and digital health. While no publications are listed in the provided text, the research output from MIPLAB typically spans computer vision, artificial intelligence in healthcare, and biomedical signal analysis. The lab’s work often emphasizes translational research, aiming to bridge the gap between algorithmic innovation and clinical utility. Future trends in his research may include explainable AI, federated learning for medical data, and integration with electronic health records. Scientific Awards: No awards mentioned. As a Doctoral Assistant, Ekansh is involved in research and potentially teaching or mentoring, though no advisees are listed. He does not appear to lead independent grants, but likely contributes to larger collaborative projects within EPFL or with clinical partners. MIPLAB is part of a broader ecosystem of health technology innovation at EPFL, collaborating with hospitals and industry partners to develop real-world medical solutions. The laboratory environment fosters interdisciplinary collaboration, combining expertise from electrical engineering, computer science, and clinical medicine. Ekansh’s position places him at the forefront of academic research in medical AI, with opportunities to publish in top-tier journals and present at international conferences. His career trajectory is aligned with academic or industrial research in biomedical engineering and AI-driven healthcare technologies.
