Higher School of Aeronautical Techniques and Automotive ConstructionFrance
Amine MEHEL is a Research Professor at ESTACA's Mechanics and Environment Center (MSCE) since 2010, specializing in air quality and pollution control in transport systems. His work bridges experimental and numerical studies of turbulent flow interactions with pollutants and nanoparticles. Research Axes : CQA (Characterization of Air Quality) and EDP (Spatiotemporal Dynamics of Pollutants) Key Projects : CEPARER (2022-2025), AmCoAir (2020-2023), CAPNAV (2019-2022), CAPTIHV (2015-2018) His expertise includes experimental facilities like wind tunnels, PIV/LDV measurements, and CFD simulations using Eulerian-Lagrangian approaches. He supervises PhD students and coordinates teaching projects like PIRATE and PRI. Recent publications focus on ultrafine particle dispersion in vehicle wakes, brake emissions in underground stations, and cabin air quality characterization.
Prasenjit Mandal is an Associate Professor in the Department of Information Systems, Supply Chain Management, and Decision Support at NEOMA Business School (France). He holds a PhD in Decision Sciences and Information Systems from the Indian Institute of Management (IIM) Bangalore. Previously, he served as an Assistant Professor of Operations Management at IIM Calcutta and worked as an Oracle ERP consultant at Tata Consultancy Services. His research focuses on supply chain finance, revenue optimization, multi-channel retail strategies, and strategic decision-making in supply chains. He has published in top journals like European Journal of Operational Research and IEEE Transactions on Engineering Management. He currently serves as a reviewer for multiple academic journals. Education: PhD in Decision Science and Information Systems, IIM Bangalore, India Oracle ERP Techno-Functional Certification Research Interests: Revenue Management in Retail & E-commerce Supply Chain Finance & Platform Financing Multi-channel Distribution Strategies Empirical Modeling of Supply Chain Trade-offs Strategic Decision-Making under Competition Key Contributions: His recent work explores platform financing models, strategic supplier financing choices, and omnichannel retail challenges. His research integrates optimization models with real-world operational scenarios to address supply chain complexities. Professional Experience: Current: Associate Professor, NEOMA Business School 2016-2019: Assistant Professor, IIM Calcutta 2012-2015: Oracle ERP Consultant, Tata Consultancy Services
Vasily Pestun is a Permanent Professor of theoretical physics at the Institut des Hautes Études Scientifiques (IHÉS) in Bures-sur-Yvette, France, a position he has held since 2014. Previously he was a member at the Institute for Advanced Study (2011–2014) and a Junior Fellow of the Harvard Society of Fellows (2008–2011). Education Ph.D. in Physics, Princeton University (2008). Thesis: Wilson loops in supersymmetric gauge theories under the supervision of Edward Witten. B.S. & M.S. in Physics (summa cum laude), Moscow Institute of Physics and Technology (MIPT) (2003). Research Interests Pestun’s research lies at the intersection of quantum field theory, string theory and integrable systems . He is renowned for developing supersymmetric localization techniques that yield exact results in strongly-coupled supersymmetric gauge theories placed on curved manifolds. His recent work explores deep connections between quiver gauge theories , conformal field theories , quantum algebras and integrable systems , with applications to the geometric Langlands programme . Selected Awards & Honours Hermann Weyl Prize (2016) ERC Starting Grant QUASIFT (2015–2020) Junior Fellow, Harvard Society of Fellows (2008–2011) Porter Ogden Jacobus Fellowship, Princeton University (2007–2008) Centennial Fellowship, Princeton University (2003–2008) Joseph Henry Merit Prize, Princeton University (2003) Pomeranchuk Fellowship, ITEP (2003) Russian Federation President Fellowship (1997) Gold Medal, 28th International Physics Olympiad (1997) Grants & Funding Principal Investigator, ERC Starting Grant Quantum Algebraic Structures In Field Theories (QUASIFT) – €1.5 million (2015-2020) Professional Service & Outreach Pestun serves as an editor for Letters in Mathematical Physics and regularly referees for leading journals. He has organised several high-profile meetings and schools, including the 2018 month-long programme “Localization Techniques in Quantum Field Theories” at Stony Brook, and the 2019 IHES conference “Higher Structures in Holomorphic and Topological Field Theory”. He has given more than 100 invited lectures and seminar talks world-wide.
Murielle Rivenet is a Professor in the Solid State Chemistry Department at Centrale Lille, specializing in actinide chemistry and materials for sustainable nuclear power. She is affiliated with the Catalysis and Solid State Chemistry Unit (UCCS), a CNRS research unit (UMR CNRS 8181). Her office is located in building C7, room 228 at the Scientific City campus in Villeneuve d'Ascq, France. Dr. Rivenet's research focuses on the solid-state chemistry of actinides and lanthanides, particularly exploring oxalate compounds and their applications in nuclear materials. Her work spans several key areas: Crystal growth and structural characterization of actinide compounds Nuclear waste immobilization materials Coordination chemistry of uranium, thorium, and plutonium Materials for sustainable nuclear power generation Synthesis and characterization of oxalate-based coordination polymers Her recent publications demonstrate a strong focus on developing materials for nuclear applications, with particular attention to crystal engineering of actinide compounds. She has made significant contributions to understanding the structural chemistry of oxalate-based materials containing uranium, thorium, and other actinides, which have implications for nuclear fuel cycles and waste management. Dr. Rivenet has received recognition for her work in actinide chemistry as evidenced by her extensive publication record in high-impact journals including Inorganic Chemistry, Journal of Solid State Chemistry, and Chemical Communications. She actively collaborates with researchers across France and internationally, working on projects related to nuclear materials science and sustainable nuclear power. Her research group develops advanced materials for nuclear applications, with a focus on understanding fundamental chemical behaviors of actinides in solid-state systems.
Hirokatsu Kataoka serves as Chief Senior Researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, with multiple academic affiliations including Academic Visitor at the Visual Geometry Group (VGG) at University of Oxford, Visiting Associate Professor at Keio University, and Adjunct Associate Professor at Tokyo Denki University. He is Principal Investigator of both cvpaper.challenge and LIMIT.Lab, and serves as Research Advisor for SB Intuitions. Dr. Kataoka earned his Ph.D. in Engineering from Keio University (April 2011 - March 2014), where he received the Fujiwara Prize in 2014 as valedictorian equivalent. His research primarily focuses on innovative pre-training methodologies that eliminate dependency on natural image datasets, with his Formula-Driven Supervised Learning (FDSL) framework being particularly influential in the field. Kataoka's research interests center around representation learning with limited data resources, including zero-shot, unsupervised, and synthetic learning approaches. His work explores how visual/multimodal models can be effectively trained with minimal real-world data, addressing critical ethical concerns related to large-scale datasets. He has pioneered methods using fractal geometry, mathematical formulas, and procedural generation to create effective pre-training frameworks that rival traditional ImageNet-based approaches. His publication record shows a clear trajectory toward solving the challenges of learning with limited resources, with recent work expanding FDSL to audio processing, microfossil analysis, and visible-to-infrared translation. His papers consistently address the core challenge of building robust visual recognition systems without relying on massive annotated datasets, with increasing focus on practical applications across diverse domains. Scientific Awards & Recognition ACCV 2020 Best Paper Honorable Mention Award for 'Pre-training without Natural Images' AIST Best Paper Award (2019, 2022) BMVC 2023 Best Industry Paper Finalist Featured in MIT Technology Review His 3D ResNets paper ranks among the top 0.5% most-cited CVPR papers over a five-year period Dr. Kataoka actively advises numerous researchers across multiple institutions, with his research team comprising Ph.D. and Master's students from various universities. He has served as Area Chair for CVPR 2024 and 2025, will serve as IEEE TPAMI Associate Editor beginning in 2025, and organizes the LIMIT Workshop series at major computer vision conferences. His LIMIT.Lab, established in June 2025, serves as a collaboration hub focused on building multimodal AI models under constrained resources including compute, data, and labels.
Nicolas Joly is a Researcher at the Le Mans University 's Institute of Acoustics , affiliated with the Transducers team. His work focuses on numerical modeling of acoustics and vibroacoustics, particularly thermoviscous diffusion phenomena in boundary layers. Research Areas : MEMS Transducers, Thermoviscous Fluid Dynamics, Inverse Methods, Composite Material Analysis Recent Contributions : Modeling micro-scale acoustic devices, homogenization of complex elastic moduli in curved beams, and fluid-loaded microbeam dynamics. Scientific Production Trends : Recent publications highlight advanced numerical simulations for miniaturized transducers, inverse methods in material characterization, and thermoviscous effects in MEMS devices. His work bridges acoustics with structural mechanics and sensor design.
Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD) . He is also a Visiting Academic at Amazon Web Services (AWS) . His research focuses on helping people write trustworthy software through techniques in program synthesis, formal verification, and machine learning robustness. Bachelor and Master in Computer Science from University of Torino (2008, 2010) PhD in Computer Science from University of Pennsylvania (2015) His research integrates programming languages , automata theory , and formal methods to ensure software reliability. Recent work explores semantics-guided synthesis and specification-aligned LLMs , with applications in network security, machine learning fairness, and automated code repair. Key trends in his publications include program synthesis , formal verification , and trustworthy AI systems . He has contributed to tools like AutomataTutor and SemGuS , a framework for customizable synthesis problems using constrained Horn clauses. Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award NSF CAREER Award Microsoft Research Faculty Fellowship Google and Facebook Faculty Awards Best Paper Award at ICDCN 2023 Distinguished Paper Award at SBES 2021 D'Antoni actively contributes to academic community service as a committee member in PLDI , OOPSLA , POPL , and CAV . He leads the Programming Systems Group at UCSD and collaborates with SemGuS research team on synthesis frameworks.
Houman BOROUCHAKI is a Professor at the University of Technology of Troyes (UTT), France, with over 20 years of academic leadership. He has served as Head of the Automatic Mesh Generation and Advanced Methods (GAMMA3) project team since 2008 and previously led the Laboratory of Mechanical Systems and Concurrent Engineering (LASMIS) (2005-2007). His work bridges academic research and industrial applications through collaborations with INRIA , French Petroleum Institute (IFPEN) , Dassault Aviation , and others. Research Interests: A pioneer in adaptive meshing , he focuses on finite element methods , geometric modeling , and numerical simulations . His innovations underpin mesh generation algorithms , 3D triangulation software , and industrial applications in metal forming, composite simulation, and subterranean modeling. Scientific Trends: His recent work emphasizes metric-based meshing , high-order geometric validity , and parallel processing for mesh generation , with applications in petroleum reservoirs, aviation surfaces, and nanomaterials. His Google Scholar profile reflects 25+ years of contributions to meshing and simulation. Teaching: With 22 years of experience, he teaches courses on meshing , numerical analysis , geometric modeling , and computer graphics at UTT, covering undergraduate to PhD levels. Labs & Teams: He leads the interdisciplinary GAMMA3 team and has contributed to LASMIS (mechanical engineering), L2n (CNRS-UMR 7076) (nanomaterials), and LIST3N (computer science).
Craig Lee is a Professor of Oceanography at the University of Washington, where he also serves as Senior Principal Oceanographer and Assistant Director for Research at the Applied Physics Laboratory. His work focuses on physical oceanography with emphasis on observational studies and instrument development. Lee leads research programs studying upper ocean dynamics, coastal processes, and high-latitude oceanography across diverse regions including the Arctic, North Atlantic, and South China Sea. Dr. Lee's educational background includes: B.S. in Electrical Engineering and Computer Science from the University of California, Berkeley (1987) Ph.D. in Physical Oceanography from the University of Washington (1995) Lee's primary research interests center on three interconnected areas: (1) upper ocean dynamics, particularly mesoscale and submesoscale fronts and eddies; (2) interactions between biology, biogeochemistry and ocean physics; and (3) high-latitude oceanography in changing Arctic environments. His work often combines field observations with instrument development to address fundamental questions about ocean circulation and its role in climate systems. He has pioneered approaches using autonomous platforms to study difficult-to-access regions like ice-covered waters. Analysis of Lee's recent publications reveals a strong focus on Arctic oceanography, upper ocean mixing processes, and the application of autonomous observing technologies. His research spans multiple ocean basins with particular emphasis on the Arctic, North Atlantic, and western Pacific. A notable trend is the increasing integration of biogeochemical measurements with physical oceanography to understand coupled systems. His work often addresses climate-relevant questions about ocean circulation, heat transport, and ecosystem responses to environmental change. Dr. Lee provides leadership through service on science steering committees for large research programs and advisory panels for U.S. Arctic efforts. He actively supports and advises graduate students while teaching courses on ocean circulation observations and experimental design. His team has developed innovative technologies including autonomous gliders for ice-covered waters, high-performance towed vehicles, and lightweight mooring systems. Lee leads a research team pursuing diverse field programs including Arctic PISCES, Stratified Ocean Dynamics of the Arctic (SODA), and studies of the Kuroshio Current. His group collaborates extensively with institutions worldwide and contributes to major international research initiatives focused on understanding ocean processes and their climate implications.
Kiwon Um is a tenured Assistant Professor in the Computer Graphics group at Télécom Paris, France, since October 2019. He focuses on physics-based simulations and data-driven approaches using deep learning, with an emphasis on human visual perception in computer graphics and engineering applications. Education: Ph.D. in Computer Science and Engineering from Korea University His research explores effective simulation of natural phenomena through refined data utilization and develops reliable data acquisition methods for machine learning. He also investigates perceptual evaluation of simulations to advance numerical method understanding. Recent work trends include: (1) turbulence modeling with machine learning integration, (2) elastic material simulation stability, (3) fluid dynamics optimization, and (4) differentiable physics frameworks. His publications range from 2008 to 2025, covering topics like SPH solvers, porous shell simulations, and numerical method validation.
Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.
Higher School of Economic and Commercial SciencesFrance
Overview Prof. Harris Kyriakou is an Associate Professor and Chair Holder of the Media & Digital Chair at ESSEC Business School. His research focuses on leveraging artificial and collective intelligence to enhance organizational value creation, digital strategy, and data-driven decision-making. He has advised multinational firms like Airbnb, Facebook, and Yelp, and his work is supported by grants from NSF and the Spanish government. Education Ph.D. in Management Sciences (Stevens Institute of Technology, 2016) M.S. in Engineering & Technology Innovation Management (Carnegie Mellon University, 2010) B.Sc. in Digital Systems (University of Piraeus, 2007) Research Focus His research explores intersections between AI/collective intelligence, blockchain, sharing economy regulations, and platform governance. Key themes include data network effects, algorithmic regulation, and digital transformation. Recent work on ChatGPT vs. Google examines AI-driven competitive dynamics in search markets. Recognition Awarded the 2024 Case Centre Triple Award, 2022 Early Career Award (AIS), and multiple best paper awards (AoM, INFORMS). Recognized as a 40-Under-40 MBA Professor by Poets & Quants. Teaching & Leadership Co-leads the 'Algorithmic Governance in Platform Economy' thesis Teaches courses on AI, digital strategy, and IT management at ESSEC and IESE Former Assistant Professor at IESE Business School (2016–2021) Professional Contributions Serves as a European Commission advisor on digitalization, reviewer for top journals (MIS Quarterly, Academy of Management Review), and mentor for doctoral candidates.
Nicolas Riviere is a Professor at INSA Lyon in the Department of Mechanical Engineering, working within the Laboratory of Fluid Mechanics and Acoustics (LMFA - UMR 5509). He is part of the "Fluides complexes et transferts" (Complex Fluids and Transfers) group and the Environment team. His teaching activities primarily focus on fluid mechanics at the Mechanical Engineering Department of INSA Lyon, covering: General balances (mass, momentum, energy) Aerodynamics Compressible flows Numerical simulation of flows Free surface hydraulics Prof. Riviere's research centers on free surface hydrodynamics, with applications to natural and industrial risks. His work takes an experimental approach, utilizing the laboratory's channel facilities, particularly the channel intersection installation. His research spans river floods with compound beds, urban flooding, sanitation networks, torrential flows, and flow-obstacle interactions. He has developed a strong interdisciplinary focus, co-leading the "Baignades en Rivières Urbaines" studio with Oldrich Navratil from University Lyon 2 and the EVS Laboratory. His publication record demonstrates consistent contributions to the fields of fluid mechanics and environmental hydraulics, with recent work focusing on open-channel flows, urban flooding phenomena, vegetation-flow interactions, and experimental techniques for studying complex hydraulic phenomena. His research often bridges theoretical fluid mechanics with practical environmental applications. Prof. Riviere has received recognition for his work in environmental fluid mechanics, with numerous publications in high-impact journals in hydraulic engineering and fluid mechanics. He has supervised multiple PhD students and research projects related to environmental fluid mechanics and has collaborated with various institutions on interdisciplinary research projects addressing water-related challenges. The laboratory where he works, LMFA, provides extensive experimental facilities including wind tunnels, hydrodynamic channels, and advanced measurement techniques such as PIV (Particle Image Velocimetry), LDV (Laser Doppler Velocimetry), and other state-of-the-art instrumentation for fluid flow analysis.
Olivier Tougait is a Professor at the Chemistry, materials and processes for sustainable nuclear power (CIMEND) department within the Unité de Catalyse et Chimie du Solide (UCCS) at Université Lille . He specializes in solid-state chemistry, nuclear materials, and actinide-based compounds, with a focus on understanding fuel cycle processes for nuclear energy. Academic Background: PhD in Chemistry (1998, Université de Rennes1), Postdoctoral Fellow at Northwestern University (1998-2000). Career: Lecturer at Rennes1 (2000-2014), now Professor at UCCS since 2014. Collaborations include the French Alternative Energies and Atomic Energy Commission (CEA) , Orano , and Framatome . Research Interests: Actinide-based intermetallic compounds Phase diagrams of nuclear materials Magnetocaloric properties Fuel cycle process optimization Synthesis and thermodynamic behavior of uranium alloys Collaborative industrial nuclear R&D Publications since 2012 focus on: Uranium-molybdenum fuel characterization Germanium/Aluminum substitution in actinide systems Thermal stability of uranyl peroxide nanoclusters Crystallographic analysis of heavy-fermion materials Labs: Directs the joint research laboratories LR4CU and LRC PUMA, which collaborate with Orano and Framatome on nuclear fuel cycle innovations.
Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods
