جست‌وجوی استاد و پژوهشگر

Geoffroy Couteauمشاهده پروفایل

پژوهشگر

• Cryptography
• Secure Multiparty Computation
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Geoffroy Couteau is a CNRS research scientist at IRIF (Institut de Recherche en Informatique Fondamentale), Université Paris Cité, where he conducts research in theoretical and applied cryptography. He obtained his PhD from École Normale Supérieure de Paris in 2017 under the supervision of David Pointcheval and Hoeteck Wee, followed by a postdoctoral position at Karlsruhe Institute of Technology (KIT) from 2017 to 2019. His primary research interests include secure multiparty computation, zero-knowledge proofs, and the theoretical foundations of cryptography, with a particular emphasis on pseudorandom correlation generators and efficiency improvements in cryptographic protocols. He has made significant contributions to fine-grained cryptography, non-interactive zero-knowledge proofs, and post-quantum secure computation. The recent publications reflect a strong trend toward foundational advances in secure computation, with increasing focus on efficiency, practicality, and connections to complexity theory and learning theory. His work often bridges theoretical hardness assumptions with practical protocol design. ERC Starting Grant (2023) for project OBELiSC (Overcoming Barriers and Efficiency Limitations in Secure Computation) Geoffroy Couteau has advised numerous PhD and master’s students, including Dung Bui, Clément Ducros, Eliana Carozza, and Ulysse Léchine. He has also hosted many visiting students and postdocs, fostering a vibrant research group. He has served on the program committees of major conferences such as EUROCRYPT, CRYPTO, TCC, and PKC. He is currently leading research in cryptography at IRIF and is involved in postdoctoral hiring for projects in advanced cryptographic primitives. He maintains a research blog and resource collection for students, including LaTeX templates, a probability cheat sheet, and curated answers to common cryptography questions.

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.

Fahiem Bacchusمشاهده پروفایل

استاد

• Artificial Intelligence
• Constraint Satisfaction Problems
• Satisfiability (SAT)
• +۷ مورد دیگر

Fahiem Bacchus is a Professor in the Department of Computer Science at the University of Toronto, within the Faculty of Arts and Science. His research is centered on foundational problems in Artificial Intelligence, particularly in reasoning, representation, and algorithm design. Institution: University of Toronto School: Faculty of Arts and Science Department: Department of Computer Science Email: fbacchus@cs.toronto.edu His work spans key areas including constraint satisfaction, satisfiability (SAT), automated planning, Bayesian inference, and constraint optimization. He focuses on developing algorithms that exploit domain-specific knowledge and structural properties to improve performance. His research has led to significant contributions such as the TLPlan planning system, which won the AIPS2002 international planning competition, and the 2clseq SAT solver, which demonstrated that extensive binary clause reasoning can dramatically improve solver efficiency. His work on preprocessors like Hypre further advanced formula simplification techniques. The recent articles reflect a strong focus on improving search algorithms through richer reasoning mechanisms, particularly in SAT solving and non-clausal logic. His publications show a consistent trend toward enhancing DPLL-based solvers with advanced inference techniques, reducing search space through preprocessing, and leveraging structural knowledge in logical theories. No scientific awards are explicitly mentioned in the provided text. Bacchus has supervised research and developed educational materials, with involvement in teaching and academic conference organization. While specific grants are not listed, his software releases (2clseq, Hypre, NoClause) suggest externally supported research activity. He has contributed tutorials, talks, and online teaching resources, indicating an active role in academic dissemination and mentoring. His research group has produced several software systems available for non-commercial research use, including 2clseq, Hypre, and NoClause, reflecting a strong applied and experimental component to his work. These tools are used in SAT solving, preprocessing, and non-clausal reasoning, and are documented with detailed technical information and usage instructions.

Jean-Baptiste Alayracمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Multimodal Learning
• +۳ مورد دیگر

Jean-Baptiste Alayrac is a Researcher at DeepMind, focusing on structured learning from video and natural language. His academic background includes a PhD from the Sierra and Willow groups at Ecole Normale Supérieure and Telecom ParisTech, where he explored machine learning and computer vision. He has held teaching roles as a Teaching Assistant at Ecole Normale Supérieure and other universities, contributing to courses in statistical machine learning and mathematics. His research interests span multimodal learning, vision-language models, self-supervised learning, and efficient retrieval systems. Notable projects include the Flamingo model for few-shot learning and the Perceiver IO architecture for structured data processing. He has also contributed to foundational works like HowTo100M, leveraging large-scale video-text embeddings. Alayrac's publications emphasize cross-modal interactions, with key contributions in adversarial robustness, layered video representations, and weakly supervised learning. His work often bridges computer vision and natural language processing, with applications in instructional video analysis and cross-lingual translation.

Xujia ZHUمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Surrogate Modeling
• Stochastic Simulators
• +۴ مورد دیگر

Xujia ZHU is an Associate Professor at CentraleSupélec, Paris-Saclay University, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on uncertainty quantification, surrogate modeling, stochastic simulators, and reliability analysis. He holds an engineer’s degree in Mechanics from École Polytechnique (2015), a Master’s in Computational Mechanics from TU Munich (2017), and a Ph.D. from ETH Zurich (2022). He was a postdoctoral researcher at ETH Zurich until 2023. Education: Ph.D., Chair of Risk, Safety, and Uncertainty Quantification, ETH Zurich, 2022 Master’s (high distinction), Computational Mechanics, Technical University of Munich, 2017 Engineer’s Degree, Mechanics, École Polytechnique, 2015 Research Interests: Xujia’s work bridges numerical simulations and statistics, addressing topics like uncertainty propagation, sensitivity analysis, and surrogate modeling for stochastic systems. Key areas include polynomial chaos expansions, Bayesian active learning, and applications in seismic fragility analysis. Publications: His recent work emphasizes emulation techniques for stochastic simulators, multi-fidelity methodologies, and Bayesian active learning strategies in reliability analysis. Key themes include sparse polynomial chaos expansions and latent variable modeling. Labs/Teams: Affiliated with L2S, he collaborates on transversal projects in energy, industry, and health, leveraging interdisciplinary approaches in uncertainty quantification and computational modeling.

Mohamed Amaraمشاهده پروفایل

استاد

• Numerical Analysis
• Finite Element Methods
• Computational Fluid Dynamics
• +۵ مورد دیگر

Mohamed Amara is a Professor and President of the Université de Pau et des Pays de l'Adour (UPPA). He is a member of the Inria MAGIQUE-3D team, specializing in numerical analysis and mathematical modeling. His research focuses on finite element methods, computational fluid dynamics, and photovoltaic technologies. He has contributed to advancements in solar energy systems, thermal management of photovoltaic cells, and materials science. Amara has supervised PhD students Elvira Shishenina and Izar Azpiroz, who successfully defended their theses. His work bridges applied mathematics with engineering applications, including studies on estuarian river flows and phase change materials. Roles: Professor, President of UPPA, Inria MAGIQUE-3D researcher Key Research Themes: Numerical Analysis, Solar Energy, Fluid Dynamics Amara's recent publications emphasize structural color metasurfaces, radiative cooling of solar modules, and perovskite solar cell optimization. He collaborates internationally on photovoltaic systems and thermal modeling, addressing challenges in energy efficiency and material performance.

Philippe Moireauمشاهده پروفایل

استاد

• Inverse Problems
• Data Assimilation
• Partial Differential Equations
• +۴ مورد دیگر

Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.

Andrei Khodakovمشاهده پروفایل

استاد

• Catalysis
• Heterogeneous Catalysis
• Fischer-Tropsch Synthesis
• +۵ مورد دیگر

Andrei Y. Khodakov is a Research Director (Professor equivalent) at the Unité de Catalyse et de Chimie du Solide (UCCS), UMR CNRS 8181, affiliated with University of Lille. He serves as Coordinator of the CEMOP research team (Catalysis for Energy and Synthesis of Platform Molecules) within the Heterogeneous Catalysis Department. His academic journey began with a Master's in Chemistry from Lomonosov Moscow State University (1987), followed by a PhD from the Zelinsky Institute of Organic Chemistry (1991), and a Dr. Sci. (Habilitation) from University of Sciences and Technologies of Lille (2002). Khodakov's research focuses on heterogeneous catalysis, with particular expertise in Fischer-Tropsch synthesis, syngas conversion to fuels and platform molecules, photocatalysis, CO 2 utilization, and methane valorization. His work bridges fundamental catalyst design with practical applications for sustainable energy and chemical production. He has pioneered research on nanoconfined catalysts, mobile promoters, and single-atom catalytic systems, with significant contributions to understanding reaction mechanisms and kinetics. His publication record spans over 122 papers since 2008, with recent work emphasizing CO 2 hydrogenation, photocatalytic methane conversion, and advanced catalyst design using nanoreactors and single-atom techniques. The research shows a clear trajectory toward sustainable catalytic processes for renewable feedstocks and carbon-neutral chemical production. CNRS Prize of Excellence (2011) CNRS Ph.D. and Research Supervising Bonus (2016) Special Invited Scientist of the Brazilian Government (2013-2016) Khodakov has supervised 27 PhD students and 14 post-doctoral researchers, demonstrating strong commitment to academic training. He teaches at Centrale Lille and University of Lille's Biorefinery Master's program, and organizes international summer schools for Chinese and Brazilian students. His research is supported by 4 ANR projects, 3 European projects, and over 20 industrial contracts, reflecting both academic excellence and industrial relevance. His laboratory focuses on catalyst design for sustainable chemical production, with particular emphasis on reactor engineering, in-situ characterization techniques, and development of catalysts for renewable feedstocks conversion.

Eric TOTELمشاهده پروفایل

استاد

• Network Security
• Intrusion Detection Systems
• Data Confidentiality
• +۳ مورد دیگر

Eric TOTEL is a Professor at Telecom SudParis, specializing in cybersecurity and network security. His research focuses on intrusion detection systems, graph-based anomaly detection, machine learning applications in security, and data confidentiality in distributed systems. He has contributed to projects such as DAMS (DDoS mitigation using deep reinforcement learning), Sec2Graph (novelty detection on graph-structured data), and DAEMON (dynamic autoencoder-based anomaly detection). His work emphasizes scalable solutions for multi-step attack detection and privacy-preserving infrastructure for encrypted DNS logs. Key contributions include developing correlation engines for distributed systems, formalizing invariant-based attack detection in web applications, and exploring static analysis for information flow control. He has authored over 50 peer-reviewed publications and served on program committees for conferences like RAID, CRiSIS, and EuroS&P. His HDR (2012) formalized error-detection techniques applied to intrusion detection. Advising and grants: He collaborates on projects funded by French national research agencies and has mentored students in cybersecurity, AI for defense (CAID conferences), and cloud infrastructure security. His research often bridges theoretical models and practical implementations, with tools like STARLORD for 3D graph visualization of security data.

Alberto Santiniمشاهده پروفایل

دانشیار

• Operational Research
• Transportation Systems
• Logistics Optimization
• +۵ مورد دیگر

Alberto Santini is an Associate Professor of Operational Research and a Ramon y Cajal fellow at Universitat Pompeu Fabra in Barcelona, Spain. He is also an affiliate professor at the Barcelona Graduate School of Mathematics and the Data Science Centre at the Barcelona School of Economics. During 2025-2027, he coordinates the Transportation group of the Spanish O.R. Society. His research focuses on optimization methods applied to transportation, logistics, and sustainability, including scheduling, vehicle routing, and heuristic algorithms. He has contributed to solving complex problems like last-mile delivery integration with public transport, airline flight scheduling, and energy-efficient vertical farming. His work often employs advanced techniques like column generation and decomposition strategies. Notable contributions include decomposition strategies for vehicle routing heuristics and the application of metaheuristics such as Adaptive Large Neighbourhood Search (ALNS). He is the founder of EUROYoung and AIROYoung, youth branches within prominent operational research societies. His GitHub repositories, such as cvrp-decomposition , provide open-source implementations of his algorithms. Santini’s research addresses real-world challenges like epidemic resource allocation and sustainable logistics, reflecting his commitment to both theoretical and applied operational research. Awards: Ramon y Cajal Fellow Labs/Teams: Leads Transportation group (Spanish O.R. Society), Founded EUROYoung/AIROYoung.

• Software Engineering
• Programming Languages
• Systems
• +۴ مورد دیگر

Manuel Rigger is an Assistant Professor at the National University of Singapore (NUS), leading the TEST Lab (Trustworthy Engineering of Software Technologies) within the PL/SE group at the School of Computing. His research focuses on improving the reliability of data-centric systems through automated testing frameworks and formal methods. Education : PhD from Johannes Kepler University Linz (supervised by Hanspeter Mössenböck), postdoctoral work at ETH Zurich (Advanced Software Technologies Lab under Zhendong Su). Research Interests : Automated testing of database systems Programming language design and verification Incremental build systems Formal methods for software reliability Key Contributions : Developed tools like SQLancer (for finding bugs in databases) and CERT (performance issue detection). His work has uncovered over 800 bugs in real-world systems. Awards : Recipient of the ERC Consolidator Grant (2025) for groundbreaking research in software security and testing. Service Roles : Organizer of ICFP/SPLASH 2025 Outdoor Activities, committee member for OOPSLA Review, PLDI Artifact Evaluation, and ICSE Program Committee. Also actively involved in organizing workshops (e.g., Fuzzing & Software Security Summer School 2025).

• Surface Science
• Heterogeneous Catalysis
• CO2 Valorization
• +۵ مورد دیگر

Dr. Laurent Piccolo is a permanent senior researcher (DR CNRS) at the Institute on Catalysis and Environment of Lyon (IRCELYON) , France, since 2022. He specializes in surface science and heterogeneous catalysis , with a focus on CO2 valorization , single-atom catalysis , and hydrogenation reactions . His work bridges fundamental studies of metal-support interactions and applied catalyst design for energy transition. Habilitation à diriger des recherches (University of Lyon, 2007) PhD in Materials Science (Aix-Marseille University, 1999) Master in Materials Science/Surfaces and Interfaces (Aix-Marseille University, 1996) Research Interests: His work investigates metal ultradispersion and single-atom catalysis for reactions like CO2 hydrogenation and CO oxidation. He explores how ceria , MXenes , and intermetallic compounds influence catalytic activity and stability, particularly under hydrogenation and oxidative environments . Scientific Contributions: He leads the ANR ISAC project (2022-2025) on single-atom catalysts and co-leads ANR DuCaCO2 (2021-2025) for CO2 capture and conversion . His 100+ peer-reviewed articles (4300+ citations, H-index 40) cover nanoparticle synthesis , operando characterization , and catalyst degradation mechanisms . Expertise: He employs ultrahigh vacuum techniques , surface analysis (XPS, STM, FTIR), and bulk characterization (XRD, TEM, XAS) to study catalysts. His skills include GCxGC-MS for product analysis and DFT modeling of reaction pathways. Awards & Recognition: ANR project leadership 4300+ citations 3 book chapters 260+ conference communications

Didier Raboissonمشاهده پروفایل

استاد

• Animal Health Economics
• Ruminant Population Medicine
• Bioeconomic Modeling
• +۴ مورد دیگر

Didier Raboisson is a Full Professor in Ruminant Population Medicine and Animal Health Economics at the National Veterinary School of Toulouse (ENVT), France. Holding a DVM, MSc, PhD, and Diplomate status in the European College of Bovine Health Management (ECBHM), his career spans from clinical practice (2004-2006) to academic leadership at ENVT since 2006, progressing from contractual assistant to full professor in 2019. His educational credentials include a Doctor in Veterinary Medicine (2003), ruminant clinical internship (2005), MSc in Agricultural Socio-economics (2007), PhD in Institutional Economics (2011), and HDR accreditation (2017). His research pioneered the DairyHealthSimulator® (DHS®), a stochastic dynamic bioeconomic model optimizing dairy herd management under constraints like antimicrobial use. Key contributions address veterinary workforce shortages, disease cost analytics, and veterinary business models through innovative frameworks like the DODforD herd health approach. Raboisson's publication portfolio exceeds 75 international peer-reviewed papers, with recent work focusing on antimicrobial reduction strategies, lameness economics, and dairy cooperative development. His research demonstrates consistent interdisciplinary integration of veterinary science, economics, and stochastic modeling, particularly evident in the shift from descriptive disease cost analysis to utility-optimized decision frameworks. Scientific recognition includes: ECBHM Diplomate status (2011) HDR accreditation (2017) DHS® and App Qost® patents Academic editor roles at PlosOne and Frontiers in Veterinary Sciences As supervisor of 8 PhD students and 6 post-doctorates, Raboisson leads the VetEconomics research group while directing France's continuous training program in bovine population medicine. His grant portfolio exceeds €1.4 million from INRAE, Ministry for Agricultural Sovereignty, ANR, and EU programs. International collaborations span Cornell, Liverpool, and Indian institutions through CEFIPRA and Prezode initiatives, with policy impact evidenced by DGAL working group contributions on veterinary shortages and biosecurity.

Adrot Anouckمشاهده پروفایل

مدرس

• Crisis Management
• Disaster Risk Reduction
• Organizational Resilience
• +۳ مورد دیگر

Adrot Anouck is a Lecturer in Management Research at PSL University, focusing on crisis management, organizational improvisation, and disaster risk reduction. Her work bridges information systems with emergency response, emphasizing cross-border resilience and digital transformation in extreme situations. Key Research Themes: Crisis management, organizational resilience, information systems in disaster contexts, cross-border cooperation, and digital decision-making under uncertainty. Her publications span journals like Expert Systems with Applications and Information Systems Journal , with recent studies on portfolio management under uncertainties and data sharing barriers in disaster risk reduction. She has contributed to book chapters and conference proceedings at venues such as the Academy of Management and EGOS Colloquium, exploring topics like measurement practices during the COVID-19 pandemic and the role of technology in crisis response. Adrot collaborates extensively with researchers across disciplines, including Moriceau, Karanasios, and Friedrich. While no explicit awards or student advising details are provided in the text, her work highlights the interplay between structure, action, and digital tools in enhancing organizational resilience.

Nofar Carmeliمشاهده پروفایل

پژوهشگر

• Database Query Optimization
• Fine-Grained Complexity
• Enumeration Complexity
• +۲ مورد دیگر

Nofar Carmeli is a researcher at Inria , affiliated with the Boreal joint project-team (LIRMM, Inria, University of Montpellier, CNRS) in Montpellier, France. Her work focuses on theoretical aspects of database query optimization, particularly through the lenses of fine-grained complexity and enumeration complexity. PhD from Technion (2015-2020), advised by Prof. Benny Kimelfeld Postdoctoral Researcher at ENS Paris (2021-2022) and Inria's Valda project-team Holds the Schmidt Postdoctoral Award (2021-2022) Her research explores optimal algorithms for database query answering, including direct access to ranked answers, quantile computation, and handling of conjunctive queries with self-joins or negation. She investigates how structural properties of queries and data constraints like functional dependencies affect computational complexity. Key contributions include: Establishing tractability boundaries for direct access to conjunctive queries Developing efficient algorithms for minimal triangulation enumeration Advancing probabilistic database representations for infinite domains Creating explainable opinion graph frameworks for review summarization Scientific awards: Google PhD Fellowship (2019) Schmidt Postdoctoral Award (2021-2022) PODS Best Student Paper (2019) She has served on program committees for STACS (2025), PODS (2023-2025), and ICDT (2022-2024), and contributed to open-access research dissemination through Arxiv and dblp.