Julien Baste is an Associate Professor at the University of Lille , teaching computer science at IUT de Lille . He conducts research within the ORKAD team at CRIStAL (CNRS UMR 9189), focusing on theoretical and applied aspects of algorithms and graph theory. Research Interests : Algorithms, graph theory, parameterized complexity, multi-objective optimization, and computational diversity. Recent Publications : His work spans FPT algorithms for subtree problems, reload cost optimization, diversity in hitting sets, and edge modification problems, appearing in venues like SIAM Journal on Computing , Discrete Mathematics , and Artificial Intelligence . Julien Baste collaborates with researchers across Europe and contributes to international conferences such as CEC , ICGT , and SIDMA . His academic role is full-time and ongoing since 2020.
Silvio Montresor is a Teacher-researcher at the Institute of Acoustics within Le Mans Université, actively contributing to acoustics and optical metrology research since at least 2018. His work bridges theoretical acoustics with practical engineering applications, particularly in structural health monitoring and digital holography. His research focuses on speckle noise reduction in digital holography , acoustic emission monitoring of structural damage , and ultrasonic characterization of composite materials . Key contributions include developing deep learning algorithms for phase image denoising and novel methods for damage localization in reinforced concrete using acoustic emissions. His work spans civil engineering, materials science, and biomedical applications. Analysis of his publication trends reveals consistent focus on computational imaging techniques (2018-2024), with increasing integration of machine learning since 2021. His research demonstrates strong interdisciplinary connections between acoustics , optics , and structural engineering , particularly in non-destructive testing methodologies. As a core member of the Institute of Acoustics, Montresor contributes to transversal research axes including Metamaterials , Non-linear Acoustics , and NDET (Non-Destructive Evaluation Technologies). His work supports the lab's mission in Materials Acoustics , Elastic Waves in Complex Media , and Physics of Musical Instruments , utilizing advanced facilities for laser ultrasonics and electroacoustic sensor development.
Charles Pézerat is a Researcher at the Institute of Acoustics at Le Mans University. His work focuses on advanced acoustic and vibration analysis techniques for automotive, structural, and material applications. Acoustic source identification Vibration damping mechanisms Laser ultrasonics and optical measurement Elastic wave propagation in complex media His recent publications demonstrate expertise in force analysis adaptation to polar coordinates, Bayesian source localization, and full-field vibration measurement validation. Collaborations span multiple laboratories including the Laboratory of Mechanics and Acoustics. Research trends show emphasis on: Automotive NVH (Noise, Vibration, and Harshness) Micro-perforated material damping Non-invasive measurement techniques Computational inversion methods for acoustics Structural interaction with turbulent flows Acoustic metamaterials development He has actively participated in international conferences like Eurodyn and French Congress of Acoustics since 2014.
Serrestou Youssef is a Lecturer at ENSIM and associated researcher at LAUM (Laboratoire d'Acoustique de l'Université du Mans) within Le Mans Université. His work bridges acoustics, signal processing, and machine learning. Research Interests : Acoustic signal processing Wireless sensor network optimization Speech emotion recognition using machine learning Application of reinforcement learning and genetic algorithms Scientific Production : Focuses on digital correction techniques, acoustics, and smart network deployment. Collaborates with researchers like Kosai Raoof and Mohamed Mbarki.
François HU is a Lead AI Researcher and Lecturer in Machine Learning and Computational Statistics at institutions including Conservatoire national des arts et métiers (Cnam), Institut Polytechnique de Paris (ENSAE), EPITA, and Institut des Actuaires. Since 2024, he has led the R&D AI Lab at Milliman France, focusing on Generative AI and Trustworthy AI for insurance and finance applications. PhD in Machine Learning and Insurance (2019-2022), Institut Polytechnique de Paris (CREST-ENSAE) Postdoctoral Researcher (2022-2024), Université de Montréal (Department of Mathematics and Statistics), affiliated with MILA and Algora Lab His research spans algorithmic fairness, semi-supervised learning, NLP, and GenAI, with applications in insurance, biostatistics, and finance. He developed an Early Warning System for Infectious Diseases under the Mathematics for Public Health initiative and contributed to ESG concept identification in Canadian companies via Algora Lab. Recent publications focus on fairness in multi-class classification, Wasserstein barycenters for bias mitigation, and trustworthy AI frameworks. Scientific awards include the 2022 French best actuarial science thesis award. He has taught Python programming, numerical algorithms, Bayesian machine learning, and optimization at EPITA and Institut Polytechnique de Paris, and developed educational materials including notebooks and datasets for practical workshops.
Clément Aubert is an Associate Professor with tenure at Augusta University's School of Computer and Cyber Sciences. He specializes in formal methods, complexity theory, and reversible computing, with significant contributions to concurrency theory and implicit computational complexity. His research bridges theoretical computer science with practical applications in programming languages and security. Aubert's research focuses on reversible concurrent calculi, computational complexity analysis, and formal verification. His work explores how reversibility can provide insights into traditional computing problems, with applications in security protocols and distributed systems. He has developed static analysis techniques like the mwp-analysis for determining polynomial growth bounds in programs, contributing to the field of implicit computational complexity. His publication record shows consistent contributions to top theoretical computer science venues including CONCUR, RC (Reversible Computation), and POPL-affiliated workshops. His research combines deep theoretical insights with practical implementations, as evidenced by tools like pymwp. The publications demonstrate a clear trajectory from foundational work in complexity theory to applied research in concurrency and security. Tenure at Augusta University (2023) NSF funding for 'Concurrency in Reversible Computations' project Two research grants from Augusta University Research Development Travel Grant for NSF visit Aubert actively mentors students including Neea Rusch, Assya Sellak, and Gabriele Cecilia. He has organized multiple ICE (Interaction and Concurrency Experience) conferences and served on numerous program committees. His research group focuses on reversible computing implementations and complexity analysis tools. He collaborates extensively with researchers across Europe and the United States, maintaining strong connections with French institutions through CNRS.
Christian Laforest is a Professor of computer science at Université Clermont Auvergne, affiliated with Clermont-Auvergne INP. He conducts research at LIMOS (French laboratory of computer science; associated with CNRS, UMR 6158) and teaches at ISIMA (French graduate engineering school focused on computing and applications). His primary research interests include: Graph algorithms and theory (vertex cover, independent dominating set, Steiner tree) Discrete optimization Approximation algorithms (worst case and average case analysis) Online algorithms and adaptive algorithms Random algorithms Multicriteria optimization/approximation Distributed algorithms Professor Laforest's publication record reveals a consistent focus on theoretical computer science with practical applications in network design. His recent work has explored domination problems, vertex cover algorithms, and independent domination set problems across various graph structures. He has made significant contributions to approximation algorithms, particularly in worst-case and average-case analyses, as well as online algorithms that must make decisions without complete future information. His research methodology often involves using Maple for algorithm testing and theoretical calculations on graphs. His notable scientific achievements include: Prix Tangente du meilleur article 2015 for "Sur les algorithmes en ligne" Article primé en 2014 for "Sur les algorithmes d'approximation" Professor Laforest has supervised numerous PhD students throughout his career, including Alexis Irlande, Christian Destré, Fabien Baille, Nicolas Thibault, François Delbot, Romain Campigotto, Raksmey Phan, Benjamin Momège, and Alexis Cornet. His research has been supported by various projects funded by CNRS, RNRT, and the French ANR, including Multipoints, AcTAM, ROM, ROM-EO, ALGOL, TODO, and SHAMAN. He is also active in scientific vulgarization through magazine articles, a published book on graph theory, and a dedicated YouTube channel.
Katarzyna WEGRZYN-WOLSKA serves as Professor of Computer Science and Deputy Director of the Efrei Research Lab at Efrei Paris, with concurrent associate researcher positions at Mines ParisTech (CRI) and Télécom Paris (SAMOVAR laboratory). Her academic leadership spans social network analysis, web intelligence, and AI-driven decision systems development. Her educational foundation includes: Engineer in Electronics, Silesian Polytechnic University (1987) Master in Computer Science, University of Val d'Essonne/ENSIIE/Télécom SudParis (1996) PhD in Automation and Computer Science, MinesParisTech (2001) HDR (Habilitation to Direct Research), University of Val d'Essonne (2012) Dr. WEGRZYN-WOLSKA's research integrates four synergistic domains: Social Network Analysis (influence propagation and reputation metrics), Web Intelligence (search engine evaluation and multimodal sentiment mining), Decision Support Systems (ML-based recommendation and fuzzy logic defuzzification), and Emotion Recognition (contextual analysis via multimodal signals). Her methodological framework combines graph theory, machine learning, and human-computer interaction to address complex real-world challenges. Analysis of her 2022-2025 publications reveals strategic focus on heterogeneous graph neural networks for professional recruitment systems, explainable AI architectures, and cross-domain applications from precision agriculture (bee/horse behavior monitoring) to sustainable Industry 4.0 production. This demonstrates consistent translation of theoretical advances into tangible technological solutions. She actively contributes to major research consortia including COST Actions SHIINE (Management Committee) and cHiPSet, PNAPI digital platform for beekeepers, and EU-funded ENGINE project, demonstrating strong international collaboration capabilities. Holding HDR status since 2012, she supervises doctoral candidates while serving on program committees for premier conferences (WI, SNAA, BESC). Current work emphasizes multimodal emotion detection and AI-driven sustainability frameworks for industrial systems.
Hadrien Hendrikx is a Researcher (Chargé de Recherche) at Inria Grenoble in the Thoth team . He was previously a postdoctoral researcher at EPFL (2021-2022) and completed his PhD at Inria Paris in the SIERRA and DYOGENE (now ARGO) teams, affiliated with the Computer Science Department of Ecole Normale Supérieure . He also collaborated with the MSR-INRIA joint center . Education: BSc from Ecole Polytechnique (2016) MSc in Computer Science from EPFL (2018) PhD in Machine Learning under Francis Bach and Laurent Massoulié (2021) Hendrikx’s research focuses on decentralized and distributed optimization in machine learning, emphasizing robustness, privacy, and personalization. He also explores mirror descent methods and their stochastic theory, alongside interdisciplinary applications in ecology , such as the CASCA project using computer vision for underground fauna analysis. His recent publications span topics like Byzantine-robust gossip protocols , variance reduction in decentralized optimization , and topology-aware learning , reflecting a blend of theoretical rigor and practical ecological impact. Notable awards include an Excellence Fellowship and an Outstanding Paper Award at NeurIPS 2021 . Supervision: Current PhD students: Renaud Gaucher, Loukas Duqué, Géraud Ilinca Current Master’s students: Morgan Scalabrino, Manuela Giraldo Obando Former advisees: Mohamed Bacar Abdoulandhum, Daniel Morales Broton, Abdellah El Mrini, Rustem Islamov, Mathieu Even Hendrikx teaches Numerical Optimization at Université Grenoble Alpes and part-time Generalization Properties of Machine Learning Algorithms at Orsay. He also contributes to peer review for top conferences (ICML, NeurIPS, AISTATS) and journals (Mathematical Programming, IEEE Transactions).
Yuemin Zhu is a Permanent Professor at INSA-Lyon and CNRS Permanent Researcher Director at CREATIS (Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé), where he directs the MYRIAD work group focused on Modeling & analysis for medical imaging and diagnosis. He also serves as China Affairs Coordinator at INSA-Lyon, facilitating international academic collaboration between France and China. His research spans multiple areas of medical imaging with particular expertise in diffusion tensor imaging (DTI), cardiac MRI, and advanced image reconstruction techniques. His work integrates sophisticated mathematical approaches with clinical applications, focusing on improving imaging quality, developing novel reconstruction algorithms, and applying machine learning to medical image analysis. His research has significant implications for cardiac diagnostics, tumor characterization, and materials identification in medical imaging contexts. The publication trends show a clear evolution from fundamental image reconstruction techniques to increasingly sophisticated deep learning approaches. His recent work heavily features self-supervised learning methods, transformer architectures, and multi-modal fusion techniques applied to challenging medical imaging problems. The research spans cardiac imaging, oncology applications, and materials science, demonstrating remarkable versatility while maintaining focus on core imaging methodology. Professor Zhu has mentored numerous researchers through collaborative projects, as evidenced by his extensive publication record with varied co-authors. His work has been supported by various research grants enabling the development of advanced imaging techniques and their clinical translation. He leads the MYRIAD research group at CREATIS, which focuses on developing innovative mathematical and computational approaches for medical imaging analysis and diagnosis, with particular emphasis on cardiac applications and diffusion imaging techniques.
Nozha Boujemaa is a Research Director at Inria and Director of DATAIA Institute, leading projects in Data Sciences, Algorithmic Transparency, and Societal Impact. She co-founded the Digital Society Institute (ISN) and serves as a Senior Scientific Advisor for AI initiatives. Expert in Large Scale Multimedia Content Search, Pattern Recognition, and Machine Learning Developed methods for visual content enrichment, interactive retrieval, and satellite image analysis Scientific leader of Pl@ntNet (plant identification), CHORUS (multimedia search engines), VITALAS, MUSCLE, and TRENDS projects Her research spans Multimedia Retrieval , Big Data Applications , and Algorithmic Transparency , with over 150 publications. She has supervised 25+ PhD/Master's students and organized conferences like ACM Multimedia 2013 and European Big Data Value Forum 2017. Key scientific awards include: Knight of the National Order of Merit (France) She has contributed to international projects (NSF, European Commission), served on editorial boards for Multimedia Tools and Applications , and co-chaired workshops on visual digital libraries and AI ethics. Her work impacts web search, cybersecurity, biodiversity, and earth observation.
Aldo Gonzalez-Lorenzo is an Associate Professor (maître de conférences) at Aix-Marseille University in Arles, France, where he conducts research at the GMOD team of the Laboratoire d'Informatique et des Systèmes (LIS). His academic work bridges theoretical computer science with practical applications in computational topology and discrete geometry. His primary research interests focus on computational topology and discrete geometry, with particular emphasis on homology computation, digital topology, and geometric modeling. Gonzalez-Lorenzo develops algorithms for analyzing topological features in digital objects, with applications ranging from 3D mesh processing to motion planning for robotics. His work combines theoretical foundations with practical implementations, often resulting in efficient computational methods for complex topological problems. Analysis of his recent publications reveals a strong trend toward practical applications of topological methods. His work spans from theoretical contributions in Alexander duality and discrete Morse theory to applied research in motion planning, CO2 storage analysis, and 3D mesh processing. A notable pattern is his development of efficient computational methods for measuring and analyzing topological holes across various domains, demonstrating the versatility of topological approaches in solving diverse computational problems. 2022 CG:SHOP Geometric Optimization Challenge winner (with team Shadoks) for coordinated motion planning Best paper award at GTMG 2021 for research on measuring holes in 3D meshes Gonzalez-Lorenzo actively participates in multiple research groups including IAPR-TC18, GT GDMM, and GTMG. His work at the GMOD team within LIS focuses on developing computational methods that bridge theoretical topology with practical applications in computer graphics, robotics, and scientific computing. He has developed several interactive tools and web applications to demonstrate and apply his research, including visualizations of Parcoursup algorithms and interactive tools for working with HDVFs (Homological Discrete Vector Fields).
Gregory NAIN is a researcher at INRIA Rennes, affiliated with the TRISKELL Team and Rennes 1 University. His work focuses on software engineering for home automation, middleware development, and adaptive systems. He has contributed to projects like S-Cube (a European network of excellence) and IDA (Innovation Domicile Autonomie), aiming to improve home control systems for dependent individuals. Projects: S-Cube, IDA, Kevoree Key Technologies: Middleware, Component-Based Applications, Model-Driven Engineering His publications span topics such as dynamic software product lines, fuzzy logic in touch-sensitive interfaces, and user interface adaptation. Gregory is also involved in teaching Java, embedded computing, and software modeling at the Master and Licence levels.
Mohamed Ndaoud is an Associate Professor of Statistics at ESSEC Business School and a member of the Statistics Department at CREST. Previously, he held a tenure-track position as Assistant Professor in the Department of Mathematics at the University of Southern California (USC) from August 2019. He earned his PhD in theoretical statistics under the supervision of A.B. Tsybakov. His educational background includes: PhD in Theoretical Statistics, supervised by A.B. Tsybakov. Dr. Ndaoud's research centers on high dimensional statistics, with core contributions in variable selection, estimation, and community detection. He also actively explores robust statistics, stochastic processes, harmonic analysis, random matrix theory, and spiked models, often bridging theoretical statistics with machine learning applications. Analysis of his publication record (2018-2024) reveals a consistent focus on developing robust and adaptive methods for high-dimensional data. Key themes include outlier-robust regression, clustering algorithms for mixture models, minimax optimal procedures, and harmonic analysis techniques for Gaussian processes. His work frequently introduces non-convex and tuning-free approaches to address challenges in sparse modeling and statistical learning. Scientific Awards: No awards were listed in the provided information. Research funding includes support from the CY Initiative of Excellence Paris-Seine. There is no information available regarding student advising or additional grants. Dr. Ndaoud is an integral member of CREST's Statistics Department and serves on the organizing committee for the Meeting in Mathematical Statistics (2023-2025) in Luminy, France. He actively participates in the international statistics community through workshops and tutorials, such as the upcoming Heidelberg-Paris workshop on mathematical statistics in January 2025.
Minh Man Nguyen serves as an Associate Professor at the School of Architecture of Paris-Malaquais (ENSAPM), part of PSL University, within the Digital Matters department under the Theories and Practices of Architectural and Urban Design (TPCAU) disciplinary field. He coordinates a digital technology think tank at the École Spéciale d'Architecture and teaches intensive programs and project studios at ENSAPM. His educational background includes: Building Engineering Diploma from ESTP DPLG Architect degree from ENSAPLV Master of Science in Architecture from Georgia Tech Nguyen's research centers on digital tool integration in architectural practice, specializing in computational geometry and optimization algorithms for complex structures. His work with Vienna University of Technology on projects like the LVMH Foundation demonstrates applied research in algorithmic form-finding. He emphasizes hands-on 'making' through his WAO architecture firm and promotes collaborative fabrication via WoMa neighborhood factory. Professional engagements include structural engineering at RFR Paris (complex geometry department) and architectural practice at Mack Scogin Merill Elam. His pedagogical focus bridges digital theory with workshop-based learning in architectural education.
