جستجوی استاد راهنمای دانشگاهی

Clément Malletمشاهده پروفایل

پژوهشگر

• Geospatial Computer Vision
• Remote Sensing
• Machine Learning
• +۴ مورد دیگر

Clément Mallet is a Senior Researcher and Director of the LASTIG laboratory at Université Gustave Eiffel, IGN, and École Nationale des Sciences Géographiques (ENSG) in Champs-sur-Marne, France. He leads research in geospatial computer vision, focusing on the intersection of remote sensing, computer vision, and machine learning. His responsibilities include overseeing 75 laboratory members and directing the STRUDEL research team focused on spatio-temporal information modeling. Education: Habilitation (HDR) in Geographical Information Science, Université Paris-Est (2016) PhD in Image and Signal Processing, Télécom ParisTech (2010) Engineering Degree in Geographical Information Science, ENSG (2005) Master's in Remote Sensing, Université Paris 6 (2005) Research Interests: Dr. Mallet specializes in multi-modal land-cover mapping, change detection, geohistorical image analysis, and airborne lidar processing. His work integrates deep learning with geospatial data analysis to solve complex problems in environmental monitoring, urban studies, and historical geography. Current research explores foundation models for earth observation and semantic change detection using hybrid data generation techniques. Publication Trends: Mallet's recent articles (2021-2025) demonstrate strong focus on deep learning applications for geospatial challenges: 40% address land-cover mapping innovations, 30% develop novel change detection methodologies, 20% advance lidar data processing, and 10% explore historical map analysis. His work consistently bridges computer vision theory with operational remote sensing applications. Awards and Recognition: Schwidefsky Medal from ISPRS (2016) 5x Outstanding Reviewer awards (CVPR/ECCV/ICCV 2017-2024) Best Paper Awards at GEOBIA 2016 and ISPRS 2014 Young Researcher Award from GDR ISIS (2010) EuroSDR Best PhD Thesis supervision (2020) Research Leadership: Directs multiple national and international projects including MAESTRIA (ANR-funded multi-modal EO analysis) and HIATUS (historical image analysis). Supervised 14+ PhD students in geospatial AI topics. Secured funding from ANR, CNES, EU H2020 (VOLTA, LandSense), and industrial partners. Leads the STRUDEL team developing cutting-edge methods for territory dynamics analysis. Professional Service: Editor-in-Chief of ISPRS Journal of Photogrammetry and Remote Sensing (2021-present). Organized major conferences including ISPRS Congress (2020-2022 Program Chair) and JURSE events. Active in ISPRS working groups since 2008, currently leading initiatives in large-scale machine learning applications for geospatial data.

• Supply Chain Management
• Operations Management
• Revenue Management
• +۶ مورد دیگر

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

Benoît Sagotمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Computational Linguistics
• Machine Translation
• +۷ مورد دیگر

Benoît Sagot is a Senior Researcher in Natural Language Processing and Computational Linguistics at Inria , currently holding the 2023-2024 Informatics and Digital Sciences Annual Chair at Collège de France. He directs the ALMAnaCH research team and contributes to the PRAIRIE Institute for AI research. Research Focus: His work spans neural language models, machine translation, text simplification, multimodal NLP, and lexical resource development for French and low-resource languages. He explores computational morphology, etymology, and historical linguistics, with applications in opinion mining and computational oenology. Recent Articles emphasize language model interpretability, cross-lingual transfer, and multimodal integration (speech, image). Tools & Resources: He has developed morphological lexicons (Le fff, Alexina), corpora (OSCAR, CAMEMBERT), and parsing pipelines (SxPipe). Projects: Involved in initiatives like ANR BASNUM (Furetière's dictionary digitization) and 3IA PRAIRIE (AI research). His career combines foundational work in syntactic analysis with evolving deep learning approaches.

Nicolas Jolyمشاهده پروفایل

پژوهشگر

• Acoustics
• Vibroacoustics
• Numerical Modeling
• +۳ مورد دیگر

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'Antoniمشاهده پروفایل

دانشیار

• Programming Languages
• Software Verification
• Program Synthesis
• +۴ مورد دیگر

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.

• Mesh generation
• Numerical simulation
• Finite element methods
• +۵ مورد دیگر

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).

Thomas Boddaertمشاهده پروفایل

استادیار

• Organic Photochemistry
• Peptide-based Foldamers
• Organocatalytic Transformations
• +۳ مورد دیگر

Thomas Boddaert is an Assistant Professor at the Institut de Chimie Moléculaire et des Matériaux d'Orsay (ICMMO), Université Paris-Saclay . His research focuses on organic photochemistry , synthesis of constrained β-amino acids , and conformational studies of peptide-based foldamers . He leads the photochemistry theme and instrumental platform in the CP3A Organic Synthesis group and manages collaborations with industry partners like Diverchim. PhD in Organocatalysis (Aix-Marseille University, 2009) Postdoctoral research: Manchester University (2010), Rouen University (2010-2012) HDR (2021) for PhD supervision qualification His research includes: Photochemical domino reactions for sulfur heterocycles Peptide foldamer design with cyclobutane motifs Asymmetric synthesis using N-heterocyclic carbenes (NHCs) Visible-light photocatalysis Recent publications demonstrate expertise in: Light-initiated cascade synthesis of alkylidenecyclobutanes (2024) Thia-Paternò-Büchi reaction applications (2024, 2022) Conformational analysis of oxetin oligomers (2018) Fluorinated β-peptide folding studies (2015) Awards: Thieme Chemistry Journals Award (2019) Emergence@international scholarship (INC/CNRS, 2020) Teaching and administrative roles: Co-responsible for L3 Organic Photochemistry Head of L2 Organic Chemistry courses Manager of the Villebon – Georges Charpak Institute chemistry program Member of Paris-Saclay University advisory commission (2021–) He supervises 6 Paris-Saclay PhD students , 2 foreign PhD students , and 2 postdoctoral researchers , with notable projects on: Photochemical post-functionalization of thietanes (2024–) Domino reactions for sulfur heterocycles (2023–) Light-controlled kinase inhibitors (2021)

Kiwon Umمشاهده پروفایل

استادیار

• Physics-based Simulations
• Deep Learning
• Perceptual Evaluation of Simulations
• +۳ مورد دیگر

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.

Cyril Porielمشاهده پروفایل

استاد

• Organic Electronics
• Material Science
• Organic Chemistry
• +۳ مورد دیگر

Cyril Poriel is a CNRS Research Director (equivalent to Full Professor) at the University of Rennes, affiliated with the Institut des Sciences Chimiques de Rennes (ISCR-UMR 6226). He leads the 'Organic Semiconductors for Electronics' group, focusing on designing advanced materials for organic electronic devices such as OLEDs and organic field-effect transistors. His research emphasizes fluorophore design, high triplet hosts for phosphorescent OLEDs, and nanohoop chemistry. Education: PhD in Chemistry (2003, Rennes 1), Habilitation for Academic Research (2013). Postdoctoral training under Prof. Chris Moody (UK) and Prof. Kevin Smith (USA). Research interests span organic electronics, semiconductor materials, and nanotechnology. Notable awards include the Young Chemist Award (2009), Emerging Investigator (2014), and multiple CNRS excellence grants. He coordinates major grants like the ANR 'HOME-OLED' and 'EVOLUTION', involving collaborations with institutions in France, China, Japan, and Belgium. Publications focus on OLED efficiency, nanohoop structures, and hydrocarbon host materials. His work appears in top journals like J. Am. Chem. Soc. , Angew. Chem. Int. Ed. , and Adv. Mater.

Guido Pintacudaمشاهده پروفایل

استاد پژوهش

• Nuclear Magnetic Resonance Spectroscopy
• Solid-State NMR
• Biomolecular NMR
• +۳ مورد دیگر

Guido Pintacuda is a CNRS Research Director and Head of the Lyon High-Field NMR Center (CRMN) at École Normale Supérieure de Lyon since 2019. His work centers on advancing solid-state NMR methodologies with ultra-fast magic-angle spinning (MAS) to achieve atomic-level resolution in complex biomolecular and materials systems that are intractable to conventional techniques. Educational background: Undergraduate studies (1992-1997) and PhD in Sciences (1998-2002) at Scuola Normale Superiore in Pisa, Italy; postdoctoral research at Karolinska Institutet (2001-2004) and Australian National University (2004). Research interests focus on pushing NMR frontiers through high-field instrumentation and fast MAS (up to 160 kHz), with dual objectives: (i) biomolecular structure determination for membrane proteins, amyloid fibrils, and viral assemblies; (ii) solid-state NMR of paramagnetic materials like battery cathodes and catalysts. His innovations include proton detection in fully protonated proteins and DNP-enhanced sensitivity. Recent publications (2021-2024) show heavy emphasis on proton-detected NMR under fast MAS for structural biology, alongside growing work in paramagnetic materials. Key trends include method development for μs–ms dynamics, miniature rotor protocols for membrane proteins, and collaborations with Bruker for 150+ kHz probe technology. Scientific awards: ERC Consolidator Grant (P-MEM-MAS, 2015-2021) Sackler Prize (2017) ISMAR Fellow (2020) Mentoring and grants: Principal investigator for major projects including ERC (2.5 M€), ANR CTRbyNMR (384 k€), and EU PANACEA (5 M€, co-coordinator). Actively mentors PhD student Clément Ollier and postdocs (Z. Sun, S. Medina-Gomez) at ENS Lyon and international schools. Labs and teams: Directs CRMN (UMR 5082 CNRS/ENS Lyon/UCBL), a world-class NMR facility with unique high-field equipment. Leads a research group developing 150+ kHz MAS probes in partnership with Bruker Biospin and maintains strong ties to the University of Delaware (T. Polenova) and European networks.

Olivier Tougaitمشاهده پروفایل

استاد

• Solid-state chemistry
• Nuclear materials
• Actinide-based intermetallic compounds
• +۳ مورد دیگر

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.

• Theoretical Deep Learning
• Uncertainty Quantification
• Reliable Artificial Intelligence
• +۱۰ مورد دیگر

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

• Portfolio Management
• Fixed Income
• Sustainable Investing
• +۴ مورد دیگر

Marielle De Jong is an Associate Professor at Grenoble Ecole de Management, serving as the Academic Director of the USA DBA program. Her expertise spans portfolio management, fixed income, and sustainable investing, with a focus on bond portfolio construction and liquidity scoring. MSc in Econometrics from Erasmus University of Rotterdam MSc in Operational Research from Cambridge University PhD in Finance from the University of Aix-Marseille Defended HDR in 2022 Her research integrates quantitative finance with sustainability, addressing topics like ESG investing, derivatives in asset management, and risk modeling. She has extensive industry experience in investment management, notably with HSBC Sinopia and Amundi, where she led fixed-income quant research teams. Marielle's publications highlight trends in bond risk assessment, CDS applications, and green finance. She is Editor-in-Chief of the Journal of Asset Management, emphasizing rigorous quantitative methodologies and sustainable investment frameworks.

Julia Chamot-Rookeمشاهده پروفایل

پژوهشگر

• Proteomics
• Structural Biology
• Mass Spectrometry
• +۷ مورد دیگر

Julia Chamot-Rooke is a Principal Investigator and Researcher at the Institut Pasteur in Paris, France, affiliated with the Department of Structural Biology and Chemistry and the Mass Spectrometry for Biology unit (UTechS MSBio), a joint CNRS service and research unit (USR2000). She leads multiple projects in advanced proteomics and is the PI for the Institut Pasteur in the European Proteomics Infrastructure Consortium providing access (EPIC-XS). Her research focuses on developing innovative methods in top-down proteomics , cross-linking mass spectrometry , and structural proteomics to study intact proteins, post-translational modifications, and protein complexes. Her work has applications in microbiology, infectious diseases, and host-pathogen interactions. She has developed the ProteoCombiner software to integrate proteomics data for improved proteoform characterization. The recent publications reflect a strong emphasis on structural and functional proteomics , particularly in microbial systems and immune interactions. Trends include the use of advanced mass spectrometry techniques (HDX-MS, cross-linking MS, top-down MS) to investigate protein structure, dynamics, and interactions in pathogens and host systems. There is also a growing focus on software and tool development to enhance data analysis and reproducibility in proteomics. Principal Investigator, EPIC-XS at Institut Pasteur Coordinator, Joint Research Activity on Future and Emerging Proteomics Technologies Lead Developer, ProteoCombiner software She supervises PhD students and research engineers and collaborates widely on projects involving bacterial pathogenesis, immune evasion, and structural biology. Her lab is equipped with state-of-the-art Orbitrap mass spectrometers and participates in transnational access programs, providing cutting-edge proteomics services to the European research community.

Olivier FARGESمشاهده پروفایل

مدرس ارشد

• Thermal Engineering
• Energy Systems
• Fluid Dynamics
• +۳ مورد دیگر

Olivier FARGES is a Senior Lecturer and HDR (Habilitation à Diriger des Recherches) holder at the University of Lorraine, affiliated with ENSGSI (École Nationale Supérieure de Géologie et Sciences Industrielles) within the Groupe INP. He serves as Director of Industrial Partnerships at ENSGSI and is part of the LEMTA Laboratory (CNRS-University of Lorraine), focusing on multiphysics and multiscale modeling of heat transfer in complex environments. His academic roles include teaching courses such as Heat and Mass Transfer, Fluid Mechanics, Scientific Computing Modeling, and Renewable Energy. Dr. FARGES holds a Ph.D. in Energy and New R&D (2014) and an Engineering degree in Energy Engineering (2010), both from the École de Mines Albi. His research emphasizes coupled conductive-radiative heat transfer in porous media, thermal property characterization of heterogeneous materials, and Monte Carlo-based computational methods for energy systems. He has contributed to advancements in photovoltaic system modeling, solar thermal power optimization, and urban climate studies. His work bridges theoretical and applied thermal engineering, with applications in sustainable energy systems, material science, and industrial partnerships. Key research themes include radiative transfer modeling, multiphysics simulation frameworks, and the development of innovative tools for thermal property measurement and energy performance assessment.