Find Academic Supervisors

Jean-Christophe Olivo-MarinView profile

Professor

• Bioimage Analysis
• Computational Biology
• Machine Learning
• +13 more

Jean-Christophe Olivo-Marin is the Head of the Biological Image Analysis Unit and Director of the Carnot Pasteur Institute for Microbes and Health at the Institut Pasteur in Paris, France. He previously served as Director of the Department of Cell Biology and Infection (2010–2014) and continues to lead impactful research at the intersection of biology, computation, and imaging. His research focuses on bioimage informatics , developing computational methods in Machine learning and deep learning for image analysis Bayesian tracking and optical flow algorithms Statistical modeling of spatial patterns in biological systems Mathematical imaging and computational cell biophysics Digital pathology and cancer microenvironment analysis His work enables rigorous quantitative analysis of complex biological phenomena such as cell motility, host-pathogen interactions, and neural dynamics. His recent publications (2024–2025) reflect a strong trend toward AI integration in bioimaging , with advances in deep learning-based segmentation (e.g., Deep ContourFlow), large-scale image annotation (SAMJ), and frameworks for evaluating neuron tracking. There is also a focus on spatial analysis in disease contexts , particularly in cancer and neurodevelopment, combining deep learning with spatial statistics. He has been awarded research funding for projects including Next-generation Structured Illumination Microscopy (SIM) Machine learning for cancer detection using FFOCT Compressive sensing in biological imaging Statistical analysis of spatial coupling in bioimaging (SODA) Olivo-Marin actively mentors students and postdoctoral researchers and contributes to open science through the development and maintenance of Icy , a widely used open-source bioimage analysis platform. He regularly participates in international conferences such as QBI, ICPR, and NEUBIAS, and leads advanced training courses, including the upcoming Advanced Bioimage Analysis with Artificial Intelligence (AI) course at Institut Pasteur in 2026. He leads a dynamic research unit with PhD students, postdocs, and engineers working on cutting-edge image analysis challenges. The team fosters collaboration across disciplines and institutions, promoting open science and community-driven software development.

Alvin CheungView profile

Associate Professor

• Data Management
• Programming Languages
• Software Systems
• +3 more

Alvin Cheung is an Associate Professor at the University of California at Berkeley, affiliated with the Department of Electrical Engineering and Computer Sciences (EECS). He leads the Data Systems and Foundations group, Programming Systems group, Sky Lab, and SLICE Lab, while also serving as a faculty affiliate at the Berkeley Institute for Data Science. His research focuses on integrating data management, programming languages, and software systems to develop tools for scalable data processing pipelines and improved data programming experiences. PhD students advised: Sahil Bhatia, Mick Kittivorawong, Jongseok Park Research keywords include Data Management , Programming Languages , Program Synthesis , Formal Verification , and Machine Learning . His recent work explores Verified Lifting techniques for database applications, stencil computations, and cloud systems, alongside novel programming paradigms for geospatial video analytics and speculative decoding. His publications from 2023-2025 demonstrate trends in LLM-driven code optimization , automated SQL equivalence , and neural code generation across domains like tensor operations and geospatial video systems. Notable scientific achievements include the Dahl-Nygaard Prize (2024) , VLDB Early Career Award (2023) , and CHI Best Paper Award (2021) .

Fang LuoView profile

Lecturer

• Machine Vision
• Transfer Learning
• Neural Networks
• +3 more

Fang Luo serves as a Professional Teacher of electrical automation technology at the School of Mechatronics and Automotive Engineering, Qingyuan Polytechnic in Guangdong, China. With a strong research focus bridging industrial applications and affective computing, she maintains active collaborations across multiple institutions as evidenced by her 26 co-authors. Her educational background includes: M.S. in Control Theory and Control Engineering from Guangdong University of Technology (2013) Dr. Luo's research spans two primary domains with significant industrial impact. In machine vision, she develops innovative neural network architectures for industrial defect detection, addressing challenges like low-contrast defects in metal parts and magnetic tiles. Her technical contributions include attention mechanisms, multi-view analysis, and scale-adaptive networks that improve precision in manufacturing quality control. Simultaneously, she explores affective computing applications in educational contexts, developing methods for student anxiety assessment and shyness trait recognition using multi-modal data. Her work demonstrates exceptional versatility across computer vision applications while maintaining practical relevance to industry and education. Her scientific contributions are reflected in 7 publications with 36 citations between 2022-2025, primarily in IEEE journals including IEEE Transactions on Affective Computing and IEEE Access. Her research addresses critical challenges in industrial automation and educational technology, with particular focus on improving detection accuracy for small defects in metal components and developing non-invasive methods for student emotional assessment. As an educator and researcher, Dr. Luo supervises academic projects in electrical automation technology while maintaining active research collaborations. Her work bridges theoretical neural network advancements with practical industrial implementations, particularly in manufacturing quality assurance systems. She also contributes to educational technology through research on student emotional states in online learning environments. Her laboratory work focuses on computer vision applications for industrial inspection systems, developing neural network architectures optimized for defect detection in challenging manufacturing environments. These systems address specific industry pain points including low-contrast defects, small anomaly regions, and complex background interference that traditional inspection methods struggle to resolve.

• Machine Learning
• Maritime Engineering
• Robotics
• +4 more

Shengkai Zhang is an active researcher with 26 publications and 444 citations spanning engineering, computer science, and environmental disciplines. His work demonstrates strong interdisciplinary collaboration through co-authorship with researchers like Kezhong Liu and Mozi Chen across multiple high-impact venues including IEEE conferences, arXiv, and specialized journals. His research interests center on Machine Learning applications in maritime systems , with significant contributions to Large Language Model integration for ship navigation, wireless sensing for bridge officer monitoring, and sensor fusion techniques. Additional expertise spans robotics perception (visual-inertial systems, mmWave radar enhancement), environmental modeling (urban energy systems, climate studies), and biomedical applications of traditional medicine. Recent work shows increasing focus on AI foundation models and their security implications. Zhang's publication trajectory reveals consistent output with accelerating impact since 2023, featuring 15+ papers in 2024 alone. His research clusters around three core themes: Maritime AI Systems (LLM navigation, track association, watchkeeping monitoring) Advanced Sensing Technologies (mmWave radar, Wi-Fi sensing, GNSS fusion) Environmental & Biomedical Applications (urban energy modeling, gut microbiome studies) These areas demonstrate both technical depth in signal processing/computer vision and practical focus on real-world engineering challenges.

Faizal HAFIZView profile

Research Fellow

• Computational Intelligence
• Machine Learning
• Systems and Control
• +5 more

Faizal HAFIZ is a Postdoctoral Research Fellow at SKEMA Business School, France, specializing in computational intelligence and machine learning applications. He holds a PhD from The University of Auckland (2020) and previously served as a Research Assistant Professor at King Saud University (2010-2016) and Utility Assistant Manager at Reliance Energy Ltd. (2008-2010). His research focuses on multi-criteria optimization, neural network architectures for financial forecasting, epidemic control strategies, and energy-efficient building systems. Education: PhD in Computational Intelligence, University of Auckland, 2020 Research interests span computational intelligence, machine learning, and their applications in systems control, supply chain resilience, and financial modeling. His work integrates game theory for epidemic policy analysis and employs advanced optimization techniques like particle swarm algorithms and epsilon-constraint methods. His recent publications address epidemic control strategies in heterogeneous networks, stock market forecasting using sparse neural architectures, and balancing energy consumption with thermal comfort in buildings. Collaborations include studies on supply chain reconfiguration under divergent policies and the psychological impacts on epidemic dynamics. Scientific Awards: New Zealand International Doctoral Scholar in Computational Intelligence and Control (2016-2020) Advising and Grants: Co-supervised M. MUBASHIR WANI's PhD thesis (2022) Labs/Teams: Affiliated with SKEMA's Centre for Analytics and Management Science , focusing on interdisciplinary research in operations research and data-driven decision-making.

• Computational Neuroscience
• Motor Control
• Machine Learning
• +3 more

Elizabeth Thomas is an Associate Professor at the University of Burgundy, affiliated with the Institut Marey. She holds an HDR (Habilitation à Diriger les Recherches) in Neuroscience and a Ph.D. in Chemistry from the University of Texas, Austin, along with a Bachelor's in Chemistry (summa cum laude) from St. John’s University, New York. Her research focuses on computational neuroscience, motor control, and embodiment of cognition. She employs machine learning techniques to analyze electrophysiological recordings in the motor cortex and investigates how motor control strategies adapt across aging and neurological conditions. Key themes include neural network dynamics, neuromuscular coordination, and the intersection of movement and cognitive processes. Her teaching includes courses such as Introduction to Neuroscience, Neuroscience for Rehabilitation, and MATLAB programming for engineers. Over 15 years, she has published extensively on motor control mechanisms, temporal processing, and computational models of neural activity. Notable recent work includes studies on neural clustering in primate motor cortices and machine learning applications in movement analysis. Dr. Thomas collaborates on projects related to assistive technologies and aging motor systems. Her work bridges basic neuroscience with clinical applications, aiming to improve rehabilitation strategies through computational insights.

Franck VarenneView profile

Professor

• Philosophy of Science
• Epistemology
• History of Contemporary Science
• +4 more

Franck Varenne is a University Professor of Philosophy at the University of Rouen Normandy, affiliated with the UFR Letters and Human Sciences. His research focuses on the philosophy of science, epistemology, and the history of contemporary science with particular attention to models, simulations, and complex systems. He holds a Supélec engineering degree in computer science, a DEA in contemporary history, and an HDR in philosophy. Education: Supélec Engineer (Computer Science, Signal Processing), Master's and CAPES in Philosophy, DEUG in Economics, DEA in Contemporary History. Thesis in History and Philosophy of Science, HDR in Philosophy. Research Interests: Focus on epistemological aspects of computational simulations, philosophy of applied mathematics, and theoretical biology. Key themes include the role of models in science, simulation validation, and the philosophy of complex systems. Recent work emphasizes interdisciplinary approaches to modeling, including agent-based simulations in social sciences and the ethical implications of AI. His publications span over 30 books and articles, with contributions to journals like Revue Philosophique and Acta Biotheoretica. Supervision: Currently advises multiple doctoral students on topics ranging from AI explainability to morphogenetic systems. Has co-directed theses in modeling and simulation ethics. Labs: Active in ERIAC (Research Unit in Philosophy and Human Sciences), leading projects on digital humanities and simulation epistemology.

Yi JiangView profile

Associate Professor

• Entrepreneurship
• New Venture Creation
• Venture Capital Dynamics
• +3 more

Yi Jiang is an Associate Professor of Entrepreneurship at ESCP Business School in Paris, specializing in the dynamics of new venture creation and entrepreneurial behavior under uncertainty. Her research focuses on founding teams, venture capital interactions, and entrepreneurship in challenging contexts such as high-tech sectors and refugee communities. Her work employs process-based methodologies, analyzing how entrepreneurs navigate uncertainty through signaling, network strategies, and effectuation frameworks. She has collaborated closely with high-tech startups in China and refugee entrepreneurs in Europe, contributing insights into opportunity production and innovation barriers. Her research has been presented at leading conferences like the Academy of Management (AoM) and Babson College, and published in top-tier journals such as Entrepreneurship Theory and Practice and Journal of Business Venturing . Dr. Jiang also brings seven years of industry experience in micro-electronics and online retail, informing her teaching in strategic management and entrepreneurship. Her studies frequently explore temporal dimensions of uncertainty, the role of luck in entrepreneurial success, and contextual heterogeneity in entrepreneurial ecosystems. Her recent articles ( e.g. , 2024’s AlphaVC ) investigate algorithmic approaches to venture capital decision-making, while earlier work ( e.g. , 2021’s Refugee Entrepreneurs’ Opportunity-Production ) highlights resilience mechanisms in marginalized communities. Though no awards are explicitly listed, her prolific publication record reflects sustained academic impact.

Fotios StavrouView profile

Assistant Professor

• Information Theory
• Stochastic Control/optimization
• Wireless Communications
• +4 more

Fotios Stavrou is an Assistant Professor at the Communication Systems Department of EURECOM, a leading research institution in France. His academic journey includes a Diploma in Electrical and Computer Engineering from Aristotle University of Thessaloniki (2008), a PhD in Electrical Engineering from the University of Cyprus (2016), and postdoctoral research at Aalborg University (2016–2017) and KTH Royal Institute of Technology (2017–2021). Currently, he leads research in goal-oriented semantic communication, networked control systems, and interdisciplinary applications combining control theory, communication, optimization, and AI. His research focuses on semantic-aware communication paradigms, emphasizing the mathematical framework of information significance and utility. Key areas include rate-distortion-perception theory, digital twin-enabled optical networks, and AI-driven automation. He actively contributes to projects like the EU-funded 6G-GOALS initiative, aiming to integrate AI-native networks and semantic communication for future 6G systems. Notable achievements include a Best Poster Award at MenaML 2025 and a Best Paper Award at ACP 2023. His work spans over 40 publications, with recent focus on optimizing communication systems for semantic efficiency, digital twin applications, and resource allocation under uncertainty. He mentors PhD students and postdoctoral researchers, fostering innovation in both theoretical and applied domains. Current projects involve autonomous optical network management, leveraging digital twins and large language models, alongside foundational studies in rate-distortion-perception functions. His research bridges fundamental theory with practical implementations, addressing challenges in 5G/6G networks and networked control systems.

• Physical Activity Monitoring
• Cancer Rehabilitation
• Respiratory Physiology
• +3 more

Steven Gastinger is a Lecturer at the Université Catholique de l'Ouest (UCO) within the Faculty of Sciences, affiliated with IFEPSA (Sports Sciences Federation). He holds qualifications from the National Council of Universities in France. His research focuses on physical activity monitoring, particularly in cancer patients, and strategies to improve patient adherence to exercise programs. He completed a postdoctoral fellowship at the University of Limerick (Ireland) in 2011-2012, studying sedentary behavior and cardiometabolic health. Gastinger leads international relations for IFEPSA and serves as an educational advisor for the 'Education and Motor Skills' program. His work integrates wearable sensors and machine learning for respiratory and energy expenditure measurements, with applications in clinical and pediatric populations. Key projects include developing mobile health applications for prostate cancer patients and virtual reality exergames for hospitalized children. He has authored over 20 peer-reviewed publications and serves as a peer reviewer for journals like Medicine & Science in Sports & Exercise and Journal of Occupational and Environmental Medicine . Education: PhD in Sports Sciences (2010, Rennes 2 University), Master’s in Physiology (2006), STAPS Degree (2004) Research emphasizes objective measurement techniques using biosensors, with a focus on translational applications in healthcare settings. Recent work explores respiratory magnetometer plethysmography for energy expenditure estimation and chronic fatigue management in cancer patients.

Pascale Le GallView profile

Researcher

• Computer Science
• Formal Methods
• Conformance Testing
• +6 more

Pascale Le Gall is an active researcher specializing in formal methods and computer science, with primary research conducted through the Mathematics and Computer Science for Complexity and Systems laboratory. Their work spans theoretical computer science, software engineering, and interdisciplinary applications in biological systems modeling. Le Gall's research focuses on formal verification techniques, particularly in conformance testing, symbolic execution, and graph transformations. Their work bridges theoretical computer science with practical applications in distributed systems verification, geometric modeling, and biological network analysis. Key research themes include developing frameworks for stochastic process discovery, feature interaction resolution, and topological operations in geometric modeling, demonstrating both theoretical depth and practical implementation value across multiple domains. Analysis of their recent publications reveals a strong trend toward interdisciplinary applications of formal methods, particularly in biological systems. Their work increasingly integrates statistical approaches with traditional formal verification techniques, as seen in Bayesian inference for process discovery and statistical model checking of biological pathways. The research shows consistent development of symbolic execution techniques applied to increasingly complex systems, from abstract data types to distributed biological networks. Pascale Le Gall maintains active collaborations with researchers including Christophe Gaston, Marc Aiguier, and Paolo Ballarini across multiple projects. Their publication record shows consistent output with significant contributions to model-based testing frameworks, geometric modeling using graph transformations, and formal analysis of biological systems. The researcher has contributed to both theoretical foundations and practical implementations of verification techniques, with numerous conference papers and journal articles spanning over 15 years of active research.

Claude MarchandView profile

Professor

• Electrical Engineering
• Renewable Energy Systems
• Electric Vehicles
• +3 more

Claude Marchand is a Professor in the Department of Electrical and Electronic Engineering at the University of Paris. He leads the Laboratory for Electrical and Electronic Engineering of Paris, focusing on advanced research in electric machines, energy systems, and non-destructive evaluation techniques. His work spans hybrid powertrains, renewable energy integration, and electromechanical system optimization. Affiliations: Laboratory of Electrical and Electronic Engineering of Paris, University of Paris Key Research Areas: Electric Vehicles, Renewable Energy Systems, Non-Destructive Testing, Energy Management, Advanced Actuator Design Research Interests: Dr. Marchand’s research emphasizes improving energy efficiency through innovative control strategies and machine design. His studies include optimizing hybrid powertrain architectures, developing high-performance electric motors (e.g., switched reluctance machines), and advancing eddy-current-based non-destructive evaluation methods for material analysis. He also explores supercapacitor integration and photovoltaic system optimization for sustainable energy solutions. Recent work highlights the application of neural networks for defect detection in conductive materials and metamodel-based approaches for electric vehicle powertrain optimization. He has contributed to over 100 peer-reviewed publications, including studies on energy management algorithms for fuel cell scooters and vibration reduction in electric motors. Awards: Best Paper Award on Renewable Energies (2019) Dr. Marchand collaborates extensively with industry partners on projects like circulatory assistance actuators for medical devices and high-efficiency klystron designs. His team also investigates model-order reduction techniques for electric machine simulation and copper loss calculations.

Olivier BernardView profile

Professor

• Medical Image Processing
• Artificial Intelligence in Cardiac Imaging
• Domain Adaptation
• +4 more

Olivier Bernard is a Professor of Image and Signal Processing at Université de Lyon (INSA), France, and Deputy Director of the CREATIS laboratory. He leads the MYRIAD research team and holds editorial roles in IEEE Transactions on Medical Imaging and the European Heart Journal . His work bridges artificial intelligence and medical imaging, focusing on cardiac applications. Education: HDR (2019), PhD in Medical Image Analysis (2006), M.S. in Imaging (2003), Engineering Degree (2003) – all from Université de Lyon (INSA). His research explores representation learning , domain adaptation , and physics-guided AI for cardiac imaging. Key areas include uncertainty modeling, echocardiographic segmentation, and clinical data fusion. Recent publications highlight reinforcement learning frameworks (RL4Seg) and diffusion models for cardiac diagnosis. Scientific contributions include domain adaptation techniques, open-source tools like CMRSegTools , and synthetic data generation pipelines. Awards include Senior Membership in the Institut Universitaire de France (IUF).

Gabriel PeyréView profile

Research Professor

• Optimal Transport
• Machine Learning
• Data Science
• +4 more

Gabriel Peyré is a CNRS Research Professor at the Department of Mathematics and Applications (DMA) of École normale supérieure (ENS) in Paris, France. A specialist in data science and artificial intelligence, he is renowned for his work on optimal transportation theory and its applications to imaging, machine learning, and neural network training. His research bridges mathematical theory with computational algorithm design, earning him the CNRS Silver Medal (2021) and multiple European Research Council (ERC) grants, including the 2024 Advanced Grant. Research interests include: Optimal Transport Machine Learning AI Theory Image Processing Computational Mathematics Neural Network Training His recent publications focus on advancing optimal transport methods in AI, with applications in neural network learning, spatial transcriptomics, and unsupervised data analysis. He has developed algorithms for large-scale optimal transport computations and contributed to theoretical understanding of transformer models and residual networks. Scientific awards: CNRS Silver Medal (2021) ERC Advanced Grant (2024) ERC Consolidator Grant (2016) ERC Starting Grant (2011) Blaise-Pascal Prize from the Academy of Sciences (2017) He supervises PhD students and postdoctoral researchers, including Raphaël Barboni, Valérie Castin, and Geert-Jan Huizing. His work involves collaborations with institutions like INRIA, MIT, and Heriot-Watt University. Peyré's affiliations include the Center for Data Sciences at ENS, where he contributes to interdisciplinary projects in biology and physics.

Micheal DaviesView profile

Visiting Professor

• Signal and Image Processing
• Sparse Representations
• Compressed Sensing
• +3 more

Micheal Davies is a Professor of Signal and Image Processing at the University of Edinburgh and a Visiting Professor at the Laboratoire de Physique (LPENSL) at ENS Lyon (2023-2024). His research focuses on sparse representations, compressed sensing, machine learning, and inverse problems. He holds a First-Class Honours degree in Engineering from the University of Cambridge (1989) and a PhD in Nonlinear Dynamics from University College London (1993). His collaboration with the SiSyPh team at ENS Lyon centers on self-supervised deep learning methods for scientific imaging. Key contributions include advancing theoretical frameworks for inverse problems and algorithmic strategies in imaging. He delivered talks at the MLSP seminar and lectures for master's students in physics and computer science. Awards: Fellow of the Royal Academy of Engineering (2017), Fellow of the Royal Society of Edinburgh (2018). Research Impact: Pioneered work in compressed sensing for MRI and sparse signal processing. His visit strengthens interdisciplinary research at ENS Lyon, bridging physics, AI, and signal processing. Current projects with J. Tachella aim to eliminate ground-truth data reliance in scientific imaging.