جستجوی استاد راهنمای دانشگاهی

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.

Aws Albarghouthiمشاهده پروفایل

پژوهشگر

• Program Synthesis
• Formal Verification
• Quantum Computing
• +۵ مورد دیگر

Aws Albarghouthi is affiliated with the University of Wisconsin-Madison, USA. He is an active researcher with significant contributions to program synthesis, formal verification, and machine learning. Key roles: Author, Session Chair, Committee Member in conferences like PLDI, POPL, VMCAI, SPLASH, and ICFP. Research spans quantum computing, differential privacy, and static analysis. Research Trends include: Quantum Circuit Compilation and Optimization Probabilistic Verification of Fairness and Privacy Synthesis of Datalog and MapReduce Programs Neural-Augmented Static Analysis Bias Detection in Data Security Robustness in Machine Learning

• Big Data Analytics
• Cloud Computing
• Data Stream Processing
• +۴ مورد دیگر

Yanlei Diao is a Professor of Computer Science at École Polytechnique (France) with a joint appointment at the University of Massachusetts Amherst. She received her PhD from UC Berkeley in 2005. Her research focuses on scalable data systems, particularly in big data analytics, cloud computing optimization, and real-time stream processing. Research Interests: Her work spans cloud infrastructure optimization (UDAO project), explainable anomaly detection in data streams (EXAD), interactive data exploration (AIDEme), genomic data analysis (GESALL), and uncertain data management (CLARO). She leads the CEDAR team at Inria/LIX focusing on cloud-scale data exploration. Awards & Honors: ERC Consolidator Grant (2017-2023) CRA-W Borg Early Career Award (2013) NSF CAREER Award (2008) Keynote speaker at ACM DEBS 2021 and SWIFT 2023 AI Forum Best Paper Award at SIGMOD 2011 ACM SIGMOD Dissertation Honorable Mention (2005) Advising & Leadership: Mentored over 20 PhD students and postdocs, currently supervising 7 researchers. Served as PVLDB PC Co-Chair (2025-2026) and ACM SIGMOD Editor-in-Chief (2014-2019). Leads multiple projects with industry partners including Alibaba Cloud.

Allel Hadjaliمشاهده پروفایل

استاد

• Data Science
• Computational Intelligence
• Soft Computing
• +۱۲ مورد دیگر

Allel Hadjali is a Full Professor in Computer Science specializing in Data Engineering at ISAE-ENSMA (École Nationale Supérieure de Mécanique et d'Aérotechnique) in Poitiers, France. He is affiliated with the Laboratory LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) at ISAE-ENSMA. His academic career includes progression from Associate Professor to his current Full Professor position, with extensive teaching experience across multiple computer science domains. Professor Hadjali's research falls within the data science domain, with particular focus on Exploitation, Extraction, and Recommendation (E2R). His work applies Computational Intelligence and Soft Computing techniques to massive data exploitation and analysis, including flexible querying approaches (Skyline, Gradual, and Bipolar queries), modeling and querying uncertain/incomplete data, cooperative answering techniques, and data reduction through linguistic summaries. He also conducts research in recommendation systems (learning-based and group recommendation) and extraction techniques (mining gradual patterns), along with related interests in data quality, intelligent systems, and crowdsourced data management. His publication record demonstrates consistent contributions to top-tier journals and conferences, with recent work focusing on skyline query processing, uncertain data management, RDF knowledge bases, and explainable AI. His research shows a clear trajectory from foundational work in fuzzy logic and uncertain databases toward more applied research in semantic web technologies and machine learning explainability. Professor Hadjali serves on the editorial boards of several prestigious journals including the Journal of Smart Environments and Green Computing, Sensors Journal, and the Universal Journal of Aeronautics and Aerospace Research. He has also organized special issues on topics such as uncertainty in cloud computing and managing uncertain data. At ISAE-ENSMA, Professor Hadjali teaches courses including Formal aspects of software engineering, Language interpretations and compilation, Programming languages, and Data management and exploitation. Previously as an Associate Professor, he taught courses on object modeling, distributed algorithms, operating systems, and advanced databases focusing on preferences and uncertainty. He leads the Data Engineering team within the Laboratory LIAS, which focuses on developing computational intelligence approaches for modern data challenges. His current projects include work on data quality (QDoSSI project funded by CNRS Mastodons 2016-2018) and research actions in GDR MADICS 2018 related to scientific data quality.

Ronald Faginمشاهده پروفایل

پژوهشگر ارشد

• Applications of logic to computer science
• Database theory
• Finite model theory
• +۵ مورد دیگر

Ronald Fagin is a Research Fellow at IBM Research - Almaden, renowned for his groundbreaking contributions to database theory, finite model theory, and reasoning about knowledge. His work bridges logic with practical applications in computer science, including entity resolution, query answering, and uncertainty modeling. Ph.D. in Mathematics, UC Berkeley B.A. in Mathematics, Dartmouth College His research focuses on applying logic to computer science, particularly in database theory, finite model theory, and knowledge representation. His recent work explores quantifier complexity, multi-structural games, and ontology-driven data integration. His publications span database theory, logic, and information extraction, with a strong emphasis on formal frameworks and complexity analysis. Key themes include entity linking, uncertainty reasoning, and algorithmic voting theory. Member of National Academy of Sciences Member of National Academy of Engineering Recipient of Gödel Prize and IEEE Technical Achievement Award Laurea Honoris Causa (Italy) and Docteur Honoris Causa (France) Senior Fellow of ACM, IEEE, and AAAS Ronald Fagin has mentored prominent collaborators in database theory and information extraction. His research includes leading projects on imprecise probabilistic logic and developing frameworks for knowledge representation under uncertainty.

• Algorithms
• Databases
• Programming Languages
• +۳ مورد دیگر

Louis Jachiet is an Assistant Professor in Computer Science at Télécom Paris, affiliated with the Data, Intelligence and Graphs (DIG) team within the Information Processing and Communication Laboratory (LTCI) and the Computer Sciences and Networks (Infres) department. His research focuses on algorithms, databases, programming languages, and logic. His work spans query optimization distributed SPARQL evaluation graph algorithms formal language theory program synthesis data provenance with a strong emphasis on bridging theoretical and applied research. Analysis of his publications reveals expertise in database systems graph processing automata theory query enumeration SPARQL optimization probabilistic databases across both theoretical and practical applications.

• Signal Processing
• Image Processing
• Bayesian Inference
• +۳ مورد دیگر

Audrey Giremus is a Professor at the Institut de Mécanique et d'Ingénierie de Bordeaux (IMS Bordeaux) , affiliated with the Signal and Image Processing research group (SPECTRAL team). Her work bridges signal processing, statistical modeling, and interdisciplinary applications in environmental science and biomedical engineering. Key affiliations: IMS Bordeaux, BRGM (Bureau de Recherches Géologiques et Minières), CNRS (Centre National de la Recherche Scientifique) Collaborations: STMicroelectronics, Stellantis, Thales, CEA, Slb Research Interests focus on: Advanced signal/image processing algorithms Bayesian inference and particle filtering Applications in GPS navigation, fuel cells, and material science Statistical methods for high-dimensional data Thermal source localization and acousto-thermal coupling Coastal erosion detection using Markov chains Recent publications demonstrate expertise in GPS interference compensation , fuel cell degradation modeling , and biomarker validation . Collaborative projects with institutions like BRGM and CEA highlight her cross-disciplinary impact. Grants/Projects include: PEPR Risks (IRiMa) - CNRS OneWater PEPR - CNRS Technical platforms mentioned: Accelerated Aging Nanocomponent Testing (ATLAS, Cacyssee) Thermal Analysis (Elorga, Tamis) Terahertz and Organic Electronics

Xianyi ZENGمشاهده پروفایل

استاد

• Artificial Intelligence
• Textile Digitalization
• Wearable Systems
• +۵ مورد دیگر

Xianyi ZENG is a Full Professor and Research Supervisor (Section CNU 61) at University of Lille, France, affiliated with ENSAIT (École Nationale Supérieure des Arts et Industries Textiles). His research focuses on human-centered intelligent systems in textile and fashion domains, directing the Human Centered Design Group. His primary research interests span Artificial Intelligence for textiles , Wearable healthcare systems , and Sustainable supply chain optimization . Key specialties include fabric hand evaluation through deep learning, 3D garment fitting technology, fetal monitoring garments, and carbon price forecasting. His work bridges textile engineering with computational intelligence, emphasizing practical applications in fashion digitization and sustainable manufacturing. Analysis of his 15 most recent publications reveals strong trends in AI-driven textile innovation (73% of articles), with significant focus on wearable healthcare (27%) and sustainable systems (33%). His research consistently integrates multimodal data fusion, probabilistic modeling, and human factors analysis across domains. Innovation R&D Award from French-China Committee (2021) National Knight’s title in the Order of Academic Palms (2019) Honorary Doctorate from Gheorghe Asachi Technical University (2019) TBIS2020 Plenary Medal Lecture Award (2020) Annual PEDR Award for PhD supervision since 1995 Professor ZENG has secured substantial research funding through 7 major projects as Principal Investigator, including EU Horizon 2020's FBD_BModel and French ANR's IOTFetMov. His collaborative network spans 15+ institutions across Europe and China. Current research integrates edge AI with textile systems for Industry 5.0 applications, while his teaching covers decision support systems and sensory evaluation.

• Corrosion
• Reinforced Concrete
• Structural Reliability
• +۳ مورد دیگر

Emilio Bastidas-Arteaga is an academic researcher in civil engineering at La Rochelle Université, affiliated with the Laboratory of Engineering Sciences for the Environment (LaSIE). He holds a PhD in Engineering Sciences (Civil Engineering) from the University of Nantes (2010). His research focuses on structural durability, corrosion mechanisms, climate change adaptation, and infrastructure resilience. He has served on thesis juries and contributed to international conferences. Key research areas include: Corrosion and durability of reinforced concrete Climate change impacts on infrastructure Structural reliability and probabilistic modeling Maintenance optimization and life-cycle analysis Sustainable materials (e.g., recycled aggregates) His recent work emphasizes Bayesian networks for durability prediction, machine learning applications in structural health monitoring, and climate-resilient design strategies. He has authored/co-authored over 80 peer-reviewed articles and two books. Key projects include: Development of probabilistic frameworks for coastal infrastructure Climate adaptation strategies for tropical environments Optimization of inspection/maintenance protocols for aging infrastructure He collaborates internationally on multi-disciplinary teams addressing challenges in: Offshore wind energy systems Wood utility pole durability under hurricane risks Carbonation and chloride ingress modeling

C.-H. Luke Ongمشاهده پروفایل

استاد

• Probabilistic Programming
• Programming Languages and Verification
• Semantics of Computation
• +۸ مورد دیگر

C.-H. Luke Ong is Professor of Computer Science and Director of Graduate Studies at the Department of Computer Science, University of Oxford, and Tutorial Fellow at Merton College. He holds a BA in Mathematics (1984, Triple First) and a Postgraduate Diploma in Computer Science (1985, Distinction) from University of Cambridge, and PhD in Computer Science (1988) from Imperial College University of London. After positions at National University of Singapore (1991) and Trinity College Cambridge (1992-1993), he joined Oxford in 1994, becoming Reader in 2002 and Professor in 2004. His research spans multiple areas of theoretical computer science with recent focus on probabilistic programming, higher-order model checking, and semantics of computation. His work bridges theoretical foundations with practical applications in program verification and analysis. Ong has made significant contributions to game semantics, lambda calculus, and type theory, with recent work extending into algorithmic game theory and probabilistic computation. Ong's publication record shows a clear evolution from foundational work in semantics toward practical applications in verification and probabilistic programming. His recent articles demonstrate increasing focus on bridging theoretical computer science with practical problems in machine learning, probabilistic inference, and program analysis, particularly through higher-order model checking techniques applied to modern programming paradigms. General Chair of ACM/IEEE Symposium on Logic in Computer Science (LICS) Vice Chair of ACM Special Interest Group in Logic and Computation (SIGLOG) Member of European Association of Theoretical Computer Science (EATCS) Chairman of Singapore's Expert Panel on Mathematics and Informatics (2006-2014) Member of Singapore's Academic Research Council (since 2013) Ong has supervised 20 doctoral students to completion and currently co-supervises 9 doctoral candidates. His research has been supported by numerous grants from EPSRC and international collaborations. He has served as PC Chair for major conferences including LICS 2007, CSL 2005, FoSSaCS 2010, and TLCA 2011, demonstrating significant leadership in the theoretical computer science community. He leads research in the Centre for Metacomputation at Oxford, with active projects in higher-order model checking, algorithmic game semantics, and verification of concurrent systems. His work with the Games for Design and Verification research network has fostered international collaboration across Europe and Asia.

• Web Data Management
• Probabilistic Databases
• Provenance in Databases
• +۴ مورد دیگر

Pierre Senellart is a Professor in the Computer Science Department at École normale supérieure (ENS, Université PSL) and deputy director of the DI ENS laboratory, a joint CNRS/Inria/ENS unit. He leads the Valda team at Inria Paris and holds a chair in the PRAIRIE Paris School of AI. University: École normale supérieure (Université PSL) School: Department of Computer Science (DI ENS) Academic Rank: Professor Research Interests: His work bridges theoretical and practical aspects of web data management, including web crawling, archiving, information extraction, uncertainty management, and intensional data systems. He contributes to probabilistic XML data models, provenance tracking, and knowledge base construction. Recent Article Trends: His Google Scholar publications (2025–2023) focus on provability in probabilistic databases, theorem extraction from PDFs, and AI applications in uncertain data analysis. Topics span game theory, knowledge compilation, and multi-modal learning for scientific documents. Scientific Awards: Junior member of Institut Universitaire de France (2020–2025) Distinguished SIGMOD 2017 Program Committee Member ACM HyperText 2014 Douglas Engelbart Best Paper Award Adviser of Clément Genzmer (SIGMOD Programming Contest 2009 winner) Grants & Projects: He secures major funding (e.g., PRAIRIE Paris School of AI: €575k, DesCartes CNRS@CREATE: SGD50M), with roles in research projects like Dissemin (open science platforms) and ARCOMEM (social web archiving).

• Knowledge Representation and Reasoning
• Description Logic
• Ontology-Mediated Query Answering
• +۳ مورد دیگر

Meghyn Bienvenu is a full-time CNRS Senior Researcher (Directrice de Recherche) at the LaBRI research lab in the University of Bordeaux , France. Since 2023, she co-directs the GDR RADIA (National Research Network on Reasoning, Learning, and Decision in AI). Her research focuses on knowledge representation and reasoning , particularly description logic ontologies and their application to querying data . She spent the 2023-2024 academic year at the Japanese-French Laboratory for Informatics (JFLI) in Tokyo, Japan. Current affiliation: CNRS Senior Researcher at LaBRI, University of Bordeaux Co-director of GDR RADIA since January 2023 Research location: JFLI (2023-2024 academic year) Research Interests Meghyn's work spans knowledge representation, description logics, and ontology-mediated query answering. She explores: Querying inconsistent knowledge bases Paraconsistent reasoning frameworks Shapley value computation in AI Entity resolution and data repair Lightweight description logics (EL, DL-Lite) Connections between data management and AI Recent Publications Her recent publications (2022-2024) focus on: Paraconsistent description logics Cost-based semantics for inconsistent data Shapley value applications in query answering Collective entity resolution frameworks (ASPEn, REPLACE) Counting queries over ontology languages Honors and Awards Best Paper Award at RR 2016 Teaching Activities She has taught courses on knowledge representation, description logics, and ontology-based data access at institutions including: University of Bordeaux (since 2018) Université Montpellier (2015-2016) Universität Bremen (2009-2010) European Summer Schools (Reasoning Web, ESSLLI) Labs and Collaborations Active in the LaBRI research laboratory and the INTENDED AI Chair at University of Bordeaux, she collaborates with institutions in France, Germany, and Japan. Her work intersects AI, databases, and formal reasoning.

• Pattern Mining
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Dominique LI is a Professor in the Department of Computer Science at the University of Tours, affiliated with the Fundamental and Applied Computer Science Laboratory (LIFAT) and the Polytechnic School of the University of Tours (EPU). His research focuses on pattern mining, skyline computation, and data mining applications in domains like lithium-ion battery analysis and fluid mechanics. His recent publications highlight advancements in sequential pattern sampling, dynamic texture classification, and skyline maintenance for data streams. Keywords span Computer Science Machine Learning Database Systems Fluid Dynamics Text Classification Algorithm Design .

Talel Abdessalemمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• Machine Learning
• +۴ مورد دیگر

Talel Abdessalem is a Professor at Télécom Paris , where he has held leadership roles including Director of LTCI Research Laboratory (since 2017) and Dean of Research (since 2018). He currently serves as Deputy Vice-President for Research at Institut Polytechnique de Paris . PhD in Computer Science from Paris-Dauphine University Habilitation (HDR) from UPMC-Sorbonne University His research spans large-scale data management , recommender systems , social network analysis , and uncertain data modeling . He has participated in numerous national (ANR, FEDER) and European (FP7) research projects, with recent work focusing on stream-based learning , graph analytics , and privacy-preserving systems . His publications include diverse contributions to directed graph centrality algorithms (2021), stream recommender frameworks (River, Scikit-Multiflow), and geospatial recommendation models (ALGeoSPF). He has supervised 14 PhD students and collaborates with researchers in France, Brazil, and Indonesia. Co-leads DIG (Data, Intelligence and Graphs) research team Director of LTCI (Information Processing and Communication) laboratory

Louis Mandelمشاهده پروفایل

پژوهشگر

• Reactive Programming
• Probabilistic Languages
• Query Compilers

Louis Mandel is a Researcher at Inria, specializing in reactive programming languages and probabilistic systems. He co-developed ReactiveML and Q*cert (a verified query compiler), with applications in chatbots and cloud log analysis. His work bridges formal methods and practical tools for embedded systems.