جست‌وجوی استاد و پژوهشگر

Olivier Cappéمشاهده پروفایل

استاد مدعو

• Machine Learning
• Statistical Signal Processing
• Bayesian Methods
• +۳ مورد دیگر

Olivier Cappé is a CNRS Research Director at the Department of Computer Science of École normale supérieure (DI ENS - CNRS/ENS/Inria) and an Adjunct Professor at Université PSL. He is affiliated with the Centre Sciences des Données (CSD) at ENS and serves as a Chair holder and member of the Executive committee of the Pr[AI]rie-PSAI (Paris School of AI) project. Previously, he served as deputy scientific director at INS2I (2017-2023) and headed the Information Processing and Communication Laboratory (LTCI) from 2013 to 2016. Dr. Cappé's research focuses on statistical signal processing and machine learning. His work spans several areas including Bayesian methods, Markov Chain Monte Carlo, online learning, multi-armed bandit models, and differential privacy for machine learning. Starting in speech and audio processing in the 1990s, he contributed to natural language processing applications in the 2000s, and has focused extensively on online learning and bandit algorithms since 2010. His recent work also addresses privacy issues in machine learning systems. Cappé teaches Reinforcement Learning and Differential Privacy for Machine Learning courses in the IASD master program at Université PSL. His publication record shows consistent output across multiple research areas, with recent work focusing on bandit algorithms, online learning, and privacy-preserving machine learning. His research bridges theoretical foundations with practical applications in digital advertising, recommendation systems, and pandemic response analysis. Grand Prize of the EADS Corporate Foundation (Information Sciences) from the French Academy of Sciences (2013) Co-author of 'Tout comprendre (ou presque) sur l'intelligence artificielle' with Claire Marc Dr. Cappé holds a Supélec engineering degree (1990) and a doctorate from ENST (currently Télécom ParisTech, 1993). He joined CNRS as a researcher in 1996 and has maintained a productive research career spanning over 30 years, with significant contributions to both theoretical and applied aspects of statistical signal processing and machine learning.

Jeff Offuttمشاهده پروفایل

استاد

• Software Testing
• Mutation Testing
• Model-Based Testing
• +۸ مورد دیگر

Jeff Offutt is a Professor and Chair of the Department of Computer Science at the University at Albany, part of the College of Nanotechnology, Software, & Engineering. Previously, he was a Full Professor with Tenure in Software Engineering at George Mason University, where he led the MS in Software Engineering program and developed numerous courses in software testing, web engineering, and software analysis. His research has significantly influenced both academia and industry in software testing and engineering education. His research interests span software testing, mutation testing, model-based testing, automatic test data generation, web application testing, and software engineering education. He has pioneered techniques such as bypass testing and model-based testing, with applications embedded in tools like Selenium. His work emphasizes practical, impactful research—what he calls "science fiction"—with a clear path to real-world use by software engineers. His recent publications reflect a strong trend in cost-effective mutation testing, model-based testing strategies, educational innovations like self-paced learning models (e.g., SPARC), and security aspects of web applications. The keywords across his work include software testing, empirical studies, automation, and education, with subfields such as fault localization, test oracles, input partitioning, and scalable pedagogy. John Toups Presidential Medal for Excellence in Teaching (2020) George Mason University Faculty Member of the Year (2020) Virginia Outstanding Faculty Award (2019) George Mason Teaching Excellence Award (2013) 10-year Most Influential Paper Award, MODELS 2020 Best Paper Award, ICST 2021 Most Influential Paper, ISSRE (2019) Jeff Offutt has advised numerous students and collaborated internationally, including at Swedish universities. He has led major educational initiatives such as the NSF-funded SPARC project, aimed at improving scalability and diversity in introductory CS courses. He has also been actively involved in curriculum development, including co-authoring IEEE/ACM guidelines for software engineering programs. His leadership extends to founding and chairing major conferences like IEEE ICST and serving as Editor-in-Chief of Software Testing, Verification and Reliability . He leads research projects on smart test automation, minimal mutation, and K–5 computer science integration. His work has resulted in widely used tools including muJava, Mothra, and SpecTest. He frequently gives keynote talks on testing, education, and research methodology, and is known for his popular talk "How to Get Your Paper Rejected."

• Mathematical morphology
• Fuzzy set theory and bipolar fuzzy sets
• 3D image interpretation and computer vision
• +۷ مورد دیگر

Isabelle Bloch is a full Professor at Télécom Paris (Institut Polytechnique de Paris) and Emeritus Professor at the University of Bordeaux . She heads the Image, Modeling, Analysis, Geometry, Synthesis (IMAGES) research team within the Information Processing and Communication Laboratory (LTCI) in the Image, Data, Signal (IDS) Department . Education & Affiliations Professor – Télécom Paris, Institut Polytechnique de Paris (current) Professeure Émérite – University of Bordeaux (honorary) Research Interests Her work lies at the intersection of computer science, applied mathematics and medicine . Core themes include mathematical morphology , fuzzy and bipolar logics , 3-D image interpretation , discrete 3-D geometry & topology , information fusion , structural pattern recognition , spatial reasoning and medical imaging . Recent projects extend these concepts to explainable AI , argumentation theory , computational musicology and robotic surgery guidance . Scientific Awards Blondel Medal (2008) – awarded by the French Electrical & Electronics Engineering Society to outstanding young scientists. Grants & Collaborative Projects While specific grant numbers are not listed in the text, Prof. Bloch coordinates and participates in large-scale interdisciplinary projects funded by national (ANR) and European programs, spanning medical AI, pediatric oncology imaging, neuro-informatics and ethical AI. Labs & Teams She leads the IMAGES team (≈ 25 researchers) inside the LTCI – a joint research unit of Télécom Paris and CNRS. The group develops theory and open-source software in mathematical morphology, deep learning and symbolic AI, and collaborates closely with clinical partners at Necker–Enfants Malades Hospital, AP-HP and Gustave Roussy.

Alexandre Cabotمشاهده پروفایل

استاد

• Optimization
• Nonlinear Analysis
• Dynamical Systems
• +۵ مورد دیگر

Alexandre Cabot is a Professor at the Institute of Mathematics of Burgundy (IMB - UMR 5584 CNRS) , affiliated with the University of Burgundy and the Faculty of Sciences Mirande . His research focuses on optimization , nonlinear analysis , dynamical systems , and non-regular mechanics . He is part of the Statistics, Probability, Optimization and Control team, contributing to applied mathematics and theoretical analysis. Education : Doctoral thesis (2002) on dissipative dynamical systems and optimization at University of Montpellier II under Hédy Attouch. Habilitation à Diriger des Recherches (2006) on nonlinear and multi-speech analysis at University of Avignon. DEA in Mathematics (1998-99), Bachelor’s and Master’s in Mathematics (1992-94), and Agrégation (1994-95) at University of Paris VI. Research interests include gradient systems , proximal methods , non-smooth analysis , differential inclusions , and nonlinear oscillators . His work explores finite-time stabilization, curvature-dependent optimization, and vibro-impact problems. Recent publications span topics like inertial forward-backward algorithms, asymptotic behavior of damped equations, and regularization techniques. Collaborations include H. Attouch, P. Frankel, L. Thibault, and others. Teaching includes courses in mathematical economics, linear regression, differential calculus, and domain decomposition. He has supervised PhD students such as B. Baji, P. Frankel, and E. Vilches, focusing on optimization, non-regular mechanics, and variational analysis.

• Algorithms on strings
• Small-space algorithms
• Data structures
• +۴ مورد دیگر

Tatiana Starikovskaya is an Assistant Professor (Maître de Conférences) in the Computer Science Department at École normale supérieure (ENS), Paris, France. She leads the PARSe project (ANR-20-CE48-0001) focusing on approximation and randomized string processing, and participates in AlgoriDAM (ANR-19-CE48-0016). Co-supervises PhD students T. El Ghazi and Gabriel Bathie Co-chaired CPM 2021; served on program committees for STACS, ESA, ICALP, and others Organized CPM summer school (2023) and 'New Horizons of Stringology' workshop (2024) Research Focus Her work centers on algorithms on strings , small-space algorithms , and streaming models , with applications in bioinformatics and data security. Recent research explores trade-offs between time/space complexity in pattern matching, wildcards, and error-tolerant string analysis. Publications Trends Her recent work spans streaming algorithms , compressed data processing , and approximate pattern matching , with collaborations on challenges like k-mismatch problems, Dyck edit distances, and language distance estimation. Education PhD in Mathematics, Lomonosov Moscow State University (2013) M.Sc. in Data Science (Moscow Institute of Physics and Technology/Yandex, 2009) M.Sc. in Mathematics, Lomonosov Moscow State University (2009)

• Minimal Surfaces
• Geometric Analysis
• Conformal Geometry
• +۳ مورد دیگر

Samuel Bronstein is a PostDoctoral Research Fellow at the Max Planck Institute for Mathematics in the Sciences (MPI MiS) in Leipzig, Germany. Previously, he completed his PhD at the Département de Mathématiques et Applications (DMA) of École Normale Supérieure (ENS) in Paris under Nicolas Tholozan and remains a member of the GDR Platon research group in Geometry and Groups. Education: PhD in Mathematics, École Normale Supérieure (completed August 2024) His research centers on geometric structures with expertise in Minimal Surfaces, Geometric Analysis, Conformal Geometry, Higgs Bundles, Higher Kleinian Groups, and Negative Curvature. Work integrates representation theory, dynamical systems, and Teichmüller theory to solve problems in geometric analysis. Analysis of his 2023-2025 publications reveals dominant themes: almost-Fuchsian structures in symmetric spaces, asymptotic Plateau problems, and Anosov representations, with cross-disciplinary contributions to mathematical biology via epidemic modeling. He actively shapes the academic landscape through workshop organization (e.g., 2025 Universal Teichmüller Theory Workshop) and participation in major conferences including IHP Program 'Highrags' (2025) and '20 Years of Anosov Representations' (2024).

Christophe Giraudمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• High-Dimensional Statistics
• +۳ مورد دیگر

Christophe Giraud is a Professor of Mathematics at Université Paris-Saclay (Orsay) since 2012, with an adjunct position at Cornell University since 2016. He holds a PhD in Mathematics from Université Paris VI (2001) and a Habilitation from Université Paris XI (2011). His research focuses on high-dimensional statistics, machine learning, and their applications in biology and ecology. He leads the Master's program in Mathematics and Applications at Paris-Saclay and is Deputy Director of the Graduate School of Mathematics there. Giraud has authored Introduction to High-Dimensional Statistics (2015, 2021) and serves as an Action Editor for JMLR and JEMS . His work bridges theoretical statistics and practical algorithms, addressing challenges like clustering, bandit algorithms, and fair online learning. He has supervised numerous PhD students and contributes to software such as GGMselect. His publications span journals like Annals of Statistics , NeurIPS , and Journal of Machine Learning Research .

Alexander Tereninمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Artificial Intelligence
• Decision Theory
• +۷ مورد دیگر

Alexander Terenin is an Assistant Research Professor at Cornell University , specializing in machine learning and artificial intelligence. His work focuses on decision-making under uncertainty, Bayesian optimization, and Gaussian processes, particularly in non-Euclidean spaces. He has contributed to geometric learning, scalable Gaussian process methods, and applications in robotics, plasma science, and legal AI. His research integrates theoretical foundations with practical algorithms, emphasizing principles like the Gittins Index for optimal decision-making. Notable projects include the GeometricKernels software package for manifold learning and the Cambridge Law Corpus for legal AI. His work bridges statistics, geometry, and computer science to address challenges in autonomous systems, energy optimization, and data-driven decision-making. Recent Talks and Contributions: An Adversarial Analysis of Thompson Sampling (INFORMS Applied Probability Society 2025) Cost-aware Bayesian Optimization (NeurIPS 2024) Stochastic Poisson Surface Reconstruction (ICML 2025) Key Research Themes: Bayesian Optimization for multi-objective problems (e.g., plasma-driven energy systems) Geometric Gaussian Processes for robotics and 3D modeling Statistical guarantees for Gaussian processes on manifolds Grants and Collaborations: His work involves interdisciplinary projects with institutions like Carnegie Mellon University, ETH Zürich, and the University of Cambridge, reflecting a global network in AI and statistical learning.

• Operations Research
• Optimization under Uncertainties
• Scheduling Algorithms
• +۴ مورد دیگر

Faicel HNAIEN is a Full Professor at the Université de Technologie de Troyes (UTT), where he leads the Computer Science and Applied Mathematics Academic Department since December 2024. He also heads the Optimization Axis (LIST3N laboratory) with 30 members and oversees the International Master's Track in Logistics and Transport in Togo since 2023. His research focuses on optimization under uncertainties, scheduling algorithms, inventory control, wireless sensor networks, and quantum optimization. His work bridges theoretical advances with practical applications in manufacturing systems, energy management, and logistics. Responsibilities include academic leadership roles, strategic planning for research initiatives, and international program coordination. His recent projects address challenges like energy cost optimization in production scheduling, robust supply chain management under uncertainty, and deployment strategies for sensor networks in hazardous environments. He actively contributes to interdisciplinary research, integrating stochastic modeling, combinatorial optimization, and quantum computing approaches. His publications span high-impact journals such as Annals of Operations Research and European Journal of Industrial Engineering. Research trends emphasize hybrid methods (exact/ heuristic), stochastic programming for inventory systems, and multi-objective optimization in dynamic environments. Current efforts explore quantum formulations for scheduling problems and AI-driven solutions for supply chain resilience.

BENHIDA Soufiaمشاهده پروفایل

پژوهشگر

• Optimization
• Operations Research
• Data Approximation
• +۳ مورد دیگر

Soufia Benhida is a researcher specializing in optimization, operations research, and data approximation methods, with affiliations to both INSA Rouen Normandie and ENSA Agadir . Her doctoral work focused on decision support systems for logistics chains under uncertainty, combining theoretical and applied approaches to problems like the Probabilistic Traveling Salesman Problem (PTSP). She collaborated on the M2NUM project (Normandy and Europe region) and received joint supervision from Ahmed Mir (ENSA Agadir), Christian Gout, and Arnaud Knippel (INSA Rouen). Ph.D. Defense: December 12, 2018 Supervisors: Christian Gout, Ahmed Mir, Arnaud Knippel Projects: M2NUM, Logistics Optimization under Uncertainty Her research integrates optimization techniques for solving complex problems in logistics and industrial engineering, with a focus on exact methods for the Traveling Salesman Problem and data approximation using finite elements and splines. Current work involves probabilistic modeling for market uncertainty applications and wind field approximation considering topographic factors. Key publication trends include combinatorial optimization , subtour elimination constraints , and vector field approximation , reflecting interdisciplinary applications in logistics, environmental modeling, and industrial systems. Collaborations span institutions in Morocco and France. She has presented her work at major conferences including the International Symposium on Combinatorial Optimization (Marrakech) , CESCA Agadir , and LOGISTIQUA 2018 , showcasing methodological comparisons and novel formulations for the TSP.

• Statistical Learning
• Machine Learning
• Probability
• +۶ مورد دیگر

Stephan Clémençon is a Professor at Télécom Paris, working within the Information Processing and Communication Laboratory (LTCI) where he leads the Signal, Statistics and Learning (S2A) research team. His academic career spans multiple prestigious institutions including University of Paris X (2000-2005) and INRA Met@risk research unit (2005-07), with membership in the LPMA (Stochastic Modeling and Probability) laboratory of Paris 6 and Paris 7. Clémençon earned his PhD in Applied Mathematics from University of Paris 7 Denis Diderot with a visiting period at Stanford University's Department of Statistics (1997-1998). He currently serves as the responsible for the Specialized Master's in 'Big Data' at Télécom Paris and previously held the 'Machine Learning for Big Data' industrial chair (2013-2018), now actively involved in the 'Data Science and AI for Digitalized Industry and Services' chair. His research interests focus on statistical learning, probability, statistics, machine learning, stochastic processes, and nonparametric statistics, with applications spanning quantitative finance, biosciences, and signal/image processing. Clémençon has developed an 'immoderate taste for stochastic modeling and statistics applied in various application areas.' His extensive publication record (270 documents in HAL) demonstrates consistent research productivity with 15 recent publications (2020-2024) primarily focused on anomaly detection, ranking algorithms, statistical learning theory, and bias correction in machine learning. His work bridges theoretical statistics with practical applications in computer vision, telecommunications, and ethical AI considerations. Clémençon teaches advanced courses including Martingale Theory, Machine Learning, Advanced Nonparametric Statistics, and Big Data Projects at Télécom Paris, while also contributing to programs at Ensae Paris, Paris Diderot University, ENS Paris Saclay, and other institutions. His research team (S2A) operates within the Image, Data, Signal (IDS) department at Télécom Paris, focusing on the intersection of signal processing, data science, and statistical learning methodologies.

Antonio Casares Santosمشاهده پروفایل

پژوهشگر ارشد

• Logic
• Formal Languages
• Games on Graphs
• +۵ مورد دیگر

Antonio Casares Santos is a postdoctoral researcher in Theoretical Computer Science at the University of Warsaw, working under Mikołaj Bojańczyk. His research spans logic, formal languages, and graph-based infinite games. Doctoral Degree (2023) - University of Bordeaux (supervised by Thomas Colcombet, Nathanaël Fijalkow, Igor Walukiewicz) Master's Degree - Mathematical Logic and Foundations of Computer Science (Université de Paris-Cité, France) His work focuses on ω-automata, transducers, and memory requirements in games, with applications in formal verification and reactive synthesis. Recent publications emphasize efficient algorithms for energy games, automata minimization, and positional strategies in ω-regular languages. Key trends in his research include Optimal transformations between automata models (Muller, Parity, Rabin) Algorithmic complexity analysis for infinite games Positionality and memory characterizations History-deterministic automata Scientific recognition includes: Science and Technology Prize (University of Bordeaux, 2023) Best Student Paper Award (CSL 2022) He supervises students at the University of Warsaw and ENS Rennes, covering topics from history-deterministic automata to value-iteration algorithms. Teaching roles include algorithmic game theory tutorials and imperative programming instruction.

Jean-François CARONمشاهده پروفایل

استاد

• Composite Materials
• Civil Engineering
• Mechanical Engineering
• +۵ مورد دیگر

Jean-François CARON is a Professor and Research Director at the Navier Laboratory affiliated with École des Ponts ParisTech. His primary role involves advancing research in composite materials, damage modeling, and digital construction technologies. He leads the Architected Materials and Structures team at the Navier Laboratory and oversees the development of additive manufacturing and robotic construction platforms. His work integrates mechanical engineering, civil engineering, and advanced materials science to address challenges in sustainable construction and composite applications. Research Interests: His research focuses on composite materials for construction (e.g., wood/concrete hybrids, deployable structures), additive manufacturing (3D printing of concrete), and digital construction methodologies. He also explores material characterization, residual stress control, and low-carbon materials for eco-friendly infrastructure. Articles Trends: His recent publications emphasize experimental and computational studies of 3D-printed concrete properties, material lifecycle assessments, and structural optimization. Key themes include minimizing carbon footprints through multiobjective optimization, residual stress mitigation in additive manufacturing, and quality control across 3D printing scales. Scientific Contributions: He has pioneered studies on composite applications in civil engineering and leads initiatives for advanced construction technologies. His work bridges material science with engineering practices, contributing to both theoretical and applied advancements in sustainable infrastructure. Grants & Advising: While specific grants are not detailed, his roles as a Lab Director and Professor imply involvement in major research programs. He likely advises students on topics related to composite materials and digital construction, though no explicit student list is provided. Labs & Teams: He is responsible for the Architected Materials and Structures team at Navier Laboratory, driving innovation in additive manufacturing and robotic construction techniques. His work supports the deployment of experimental platforms for next-generation construction methods.

• Industrial systems engineering
• Operational research
• Scientific computing
• +۶ مورد دیگر

David Garcia serves as Head of the Research and Innovation Department at CESI South-East region, affiliated with the Digital Innovation Laboratory for Businesses and Learning to Support Territorial Competitiveness (LINEACT). His work focuses on industrial systems engineering, operational research, and numerical simulation with particular expertise in production systems modeling. Dr. Garcia earned his Doctorate from Ecole Supérieure de Chimie, Physique Electronique de Lyon (ESCPE) with his 2002 thesis on polymer injection process optimization (awarded highest honors). He also holds an Advanced Study Diploma in IMAGE from Joseph-Fourier University - Dijon (1998, with honors) specializing in instrumentation and image computing. His research spans production systems engineering, with evolving focus from fundamental polymer injection molding processes to contemporary applications in digital twin technology and Industry 4.0/5.0. Key interests include dynamic scheduling of flexible manufacturing systems, human-system interactions in production environments, and image processing for industrial applications. His work demonstrates a clear trajectory from material science fundamentals to advanced computational approaches for modern manufacturing challenges. Analysis of his publication history reveals consistent contributions to polymer processing optimization that evolved toward digital manufacturing systems. His recent work focuses on digital twin implementation for micro learning factories, affordance characterization in industrial work environments using deep learning, and scheduling frameworks for Industry 5.0 contexts that balance human creativity with machine precision. Dr. Garcia actively supervises research, currently guiding Sarah Ouarab's doctoral work on characterizing affordances in human cyber-physical production systems through multimodal perception of human-system interactions. He serves as Micro Learning Factory (MLF) platform referent and participates in thesis juries for related engineering disciplines. As the regional coordinator for research and innovation activities at CESI South-East, he leads the LINEACT research team focusing on management and decision-making in production systems, human-system interactions, and educational innovation through project-based learning approaches across preparatory and engineering cycles.

Gianluca Pacienzaمشاهده پروفایل

استاد

• Algebraic geometry
• Birational geometry
• Hyperbolicity of complex varieties
• +۳ مورد دیگر

Gianluca Pacienza is a Professor at the Institut Élie Cartan de Lorraine (IECL) , Université de Lorraine, and leads the Geometry team. His research focuses on algebraic and birational geometry of complex varieties, particularly irreducible holomorphic symplectic (IHS) varieties, moduli spaces, and hyperbolicity. He actively collaborates with institutions like the Simons Center and Università di Roma. Education: PhD in Mathematics (2001) from Université Pierre et Marie Curie (advisor: Claire Voisin); postdoctoral research at Ohio State University and La Sapienza di Roma. Research Trends: Recent work addresses cone conjectures for IHS and Enriques manifolds, Zariski density of rational curves, deformations of symplectic varieties, and Noether-Lefschetz loci. His studies intersect birational geometry, minimal model program (MMP), and geometric hyperbolicity. Academic Leadership: Pacienza coordinates the ANR-DFG project 'Positivity on K-trivial varieties' and co-leads a double degree program with Université Libanaise. He advises current PhD students Raphael Hiault and Aurélien Faucher, building on a legacy of mentoring graduates like Enrica Floris and Giosuè Muratore.