جست‌وجوی استاد و پژوهشگر

Antoine Amarilliمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Data Management
• Probabilistic Computation
• +۷ مورد دیگر

Antoine Amarilli is an Associate Professor in Computer Science at Télécom Paris, part of Institut Polytechnique de Paris. Since September 2024, he has been on leave to work as an advanced researcher in the LINKS team at Inria Lille. His research spans theoretical computer science, focusing on query evaluation, probabilistic data, knowledge compilation, and computational logic. Member of DIG and LINKS research teams Part of LTCI (Information Processing and Communication Laboratory) Education: PhD in Computer Science (Télécom ParisTech, 2016) Habilitation (HDR) in Computer Science (Institut Polytechnique de Paris, 2023) Research Interests: He investigates efficient query evaluation on uncertain data, dynamic data maintenance, knowledge compilation techniques, and the interplay between circuits, automata, and logical languages. His work intersects database theory, graph theory, and computational complexity. Publications Trends: Recent articles address tractability of queries on probabilistic graphs, complexity classifications for MSO and regular path queries, and knowledge compilation frameworks for enumeration algorithms. Key subfields include probabilistic reasoning, graph constraints, and circuit-based optimizations. Awards: Best Paper at ICDT 2020 Best Paper at ICALP 2021 (Track B) Télécom Paris PhD Prize (2017) Beth Dissertation Award (2017) Teaching & Service: He taught competitive programming, algorithms, and data management at Télécom Paris and represented the institution on the MPRI master’s committee. He co-organized Highlights of Logic, Games, and Automata (2022) and served as a program committee member for ICDT, ICALP, and AAAI. Research Activism: Co-founded TCS4F (Theoretical Computer Scientists for Future) and No Free View? No Review! initiatives to address climate impact and open-access advocacy in academia.

Jean-Claude Moissinacمشاهده پروفایل

مدرس

• Semantic Web Technologies
• Knowledge Graphs
• Machine Learning
• +۳ مورد دیگر

Jean-Claude Moissinac is a Lecturer at Télécom Paris, part of the Institut Mines-Télécom, affiliated with the Image, Data, Signal department and Multimedia research group. He serves as the educational manager for the CPD master's degree program (Digital Project Designer). His research focuses on semantic technologies applied to multimedia systems and knowledge representation. His primary research areas include: Semantic representations of knowledge and knowledge graphs Machine learning techniques for graph-based data analysis Advanced multimedia document processing (production, transport, representation) Semantic web technologies across diverse application contexts His publications demonstrate consistent focus on semantic technologies, knowledge graphs, and multimedia systems, with recent work emphasizing cultural heritage applications and graph-based machine learning techniques. Earlier publications focused on multimedia systems optimization and SVG standards development. He has led multiple research projects including: ILOT Project : Intelligent Learning Object for Teaching SOA2M : Multimodal interface research Data&Musée : Semantic applications for cultural heritage data SemBib : Semantic representation of bibliographic data GeoRacing : Interactive mobile TV services for sporting events He contributes to standardization efforts as co-author of the SVG web standard and participates in academic committees including SVG Open, IEEE International Symposium on Multimedia, and project evaluations for the French National Research Agency.

• Machine Learning
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Ahed ALBOODY is a Researcher (IR) working with the SPECIFI team, specializing in Machine Learning and Deep Learning applications for computer vision and remote sensing. With a research focus spanning from 2008 to the present, their work bridges the gap between theoretical computer vision techniques and practical applications in environmental monitoring, agriculture, and disaster response. Their primary research interests include: Machine Learning & Deep Learning for High Level Computer Vision Deep Learning & Big HyperSpectral and Multispectral Satellite Data Fusion Remote Sensing Image Analysis for Ocean Color Extraction Smart Agriculture Applications using Hyperspectral Imaging Qualitative Spatial Reasoning Systems for Change Detection in Satellite Images Recent work has focused on 3D hand gesture recognition using Graph Transformer Mixture-of-Experts, efficient parallel transformers, and applications of hyperspectral imaging for fire detection through smoke. Earlier research concentrated on spatial reasoning systems, particularly enriching the RCC8 model for topological relations in geospatial databases. Scientific contributions span multiple domains: Remote sensing and satellite image processing Computer vision and deep learning architectures Environmental monitoring applications Agricultural technology and precision farming Disaster response systems As a researcher, ALBOODY has collaborated extensively with colleagues across multiple institutions, with notable collaborations with Rim Slama on gesture recognition, Matthieu Puigt and Gilles Roussel on satellite image fusion, and Florence Sèdes and Jordi Inglada on spatial reasoning systems. Their work demonstrates a consistent trajectory from foundational research in spatial reasoning to applied work in remote sensing and computer vision.

Luca Ganassaliمشاهده پروفایل

استادیار

• Algorithmic Fairness
• Optimal Transport
• Statistical Inference in Graphs
• +۴ مورد دیگر

Luca Ganassali is an Assistant Professor in the Department of Mathematics at Université Paris-Saclay and a member of the Inria Celeste team. Previously, he was a postdoctoral researcher at EPFL (BAN Chair) and completed his PhD at Inria Paris under Laurent Massoulié and Marc Lelarge. His research focuses on algorithmic fairness, optimal transport in statistical learning, statistical inference in graphs and matrices, and informational computational thresholds for random instances. Education: PhD in Computer Science from Inria Paris (2022), postdoctoral studies at EPFL (2022). Research interests include causal discovery, graph alignment, and the intersection of machine learning with mathematical statistics. He actively collaborates on projects like the PhD offer on handling unfairness in data through mathematical and machine learning approaches. His work bridges theoretical computer science with applied statistics, addressing challenges in alignment of graph databases, detection of correlations in tree structures, and developing efficient algorithms for sparse graph alignment. Recent contributions include foundational results on Gaussian-weighted graph alignment thresholds and impossibility results in partial graph recovery. Lab affiliations: Inria Celeste team. Advising: Currently seeking a PhD student for a project on mathematical statistics and machine learning.

Gabor BELLAمشاهده پروفایل

دانشیار

• Multilingual Computational Systems
• Healthcare Data Interoperability
• Linguistic Diversity Modeling
• +۴ مورد دیگر

Gabor BELLA is an Associate Professor (maître de conférences) in Data Science at IMT Atlantique's Data Science department in Brest, France. He holds a PhD in Computer Science and focuses on computational systems addressing linguistic diversity and interlingual communication challenges. His research emphasizes healthcare data interoperability and bias mitigation in AI language technologies. Key research areas include multilingual lexical databases, cross-lingual semantic alignment, and ethical considerations in AI. He leads the Universal Knowledge Core (UKC), a large multilingual lexicon emphasizing linguistic diversity, and coordinates the SCROLL NLP platform for semantic text analysis in structured data. Academic roles include Programme Chair at CONTEXT 2019 conference and serving as Associate Editor for Springer Nature Computer Science. Awards include a 2025 LoResLM Best Reviewer Award and 2024 ECAI Outstanding PC Member Award. Notable projects: InteropEHRate (health data interoperability), Diversicon Framework (pluggable lexical resources), and collaborations with organizations like NHS Scotland. His work bridges theoretical linguistics with applied NLP, particularly in minority language preservation and healthcare data integration. Recent publications address linguistic bias in AI, metonymy universality, and cross-lingual medical data standards. Supervises PhD students like Hadi Khalilia and collaborates widely across Europe and the Global South to advance equitable language technologies.

Işıl Dilligمشاهده پروفایل

دانشیار

• Programming Languages
• Static Analysis
• Program Verification
• +۴ مورد دیگر

Işıl Dillig is an Associate Professor in the Department of Computer Science at the University of Texas at Austin, leading the UToPiA research group. She focuses on programming languages, with emphasis on static analysis, verification, and synthesis to enhance software reliability and security. Education: BS, MS, PhD in Computer Science from Stanford University Her research addresses critical challenges in: Program analysis for security and correctness Automated program synthesis for complex tasks Verification of concurrent and database-driven systems Recent publications span neurosymbolic synthesis, database integration, and smart contract optimization. Trends include hybrid AI-formal methods approaches and domain-specific language design. Scientific recognition includes: Sloan Fellowship NSF CAREER award She actively contributes to academic leadership as committee member, program chair, and keynote speaker in top conferences like PLDI, OOPSLA, and POPL.

Pinjia Heمشاهده پروفایل

استادیار

• Software Engineering
• Artificial Intelligence
• Natural Language Processing
• +۵ مورد دیگر

Pinjia He is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen, where he leads research in software engineering, AI systems, and log analysis. He received his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and his B.E. from South China University of Technology. His research focuses on three interconnected areas: AI for Software Engineering : Developing LLM-based tools for automated logging, code repair, and AIOps Software Engineering for AI : Ensuring safety and reliability of AI systems through testing and validation Software Systems : Log analysis, anomaly detection, and performance optimization in distributed systems Recent publications demonstrate strong emphasis on testing methodologies for AI systems (LLMs, vision-language models), automated log analysis techniques, and AI reliability. His work consistently appears in top-tier venues including ICSE, FSE, ASE, and ICLR. He has received significant recognition including: Most Influential Paper Award (ISSRE) IEEE Open Software Services Award IEEE CS TCSE Rising Star Award He leads a substantial research team with 9 PhD and 8 Master's students, focusing on AI/SE integration. The group maintains active industry collaborations and open-source projects with over 5,000 GitHub stars.

Eric SanJuanمشاهده پروفایل

دانشیار

• Natural Language Processing
• Information Retrieval
• Text Mining
• +۵ مورد دیگر

Eric SanJuan is an Associate Professor in Computer Science at Avignon Université, affiliated with the Institute of Technology (IUT) in the Department of Statistics and Decision Support Systems (StID). He specializes in discrete probabilistic models for Knowledge Representation, Natural Language Processing, and Information Retrieval. Habilitation in Computer Science (2018), Avignon Université Ph.D. in Mathematics and Computer Science (2000), Université Claude Bernard - Lyon 1 M.Phil. in Mathematics (1995), Université Claude Bernard - Lyon 1 M.A. in Discrete Mathematics (1994), Université Claude Bernard - Lyon 1 B.A. in Mathematics (1993), Université Claude Bernard - Lyon 1 His research spans Text Mining, Machine Learning, and Discrete Mathematics, focusing on efficient algorithms for textual data processing. He contributed to cultural heritage applications and microblog retrieval through the CLEF MC2 lab. He teaches Computer Science, Information Systems, and Data Mining at undergraduate and postgraduate levels in Avignon and Lyon, covering topics from databases to graph theory and advanced statistics.

• Graph Databases
• Database Systems
• Incomplete Data Management
• +۳ مورد دیگر

Lingchen WANG is a Researcher at the University of Orleans, affiliated with the LIFO laboratory. Their research focuses on graph databases with incomplete information and related areas in computer science. Contact information: Email - lingchen.wang@univ-orleans.fr ; Lab address - Building IIIA, 6 rue Léonard de Vinci, B.P. 6749, 45067 Orléans Cedex 2, France. Thesis director: Sébastien LIMET Thesis start date: September 1, 2022

• Database Systems
• Natural Language Processing
• Knowledge Graphs
• +۱ مورد دیگر

Nicolas HIOT is a Post-doctoral fellow at the University of Orleans affiliated with the LIFO laboratory (Laboratoire d'Informatique Fondamentale d'Orléans) and the Pamda project. His research bridges database systems, natural language processing, and medical informatics with a focus on text-to-database integration and consistency maintenance. His research interests center on: Database Systems for medical applications with emphasis on consistency and evolution Natural Language Processing for clinical text analysis and relation extraction Knowledge Graph construction from unstructured textual data Medical Informatics applications for healthcare data management Analysis of his 15 most recent publications (2020-2024) reveals a cohesive research trajectory at the intersection of databases and NLP. Key thematic clusters include automated medical database construction from clinical texts, consistency management in evolving RDF/property graph systems, and clinical entity/relation extraction for knowledge graphs. His work consistently addresses real-world challenges in healthcare data integration through tools like DataFix and ArchiTXT, demonstrating strong translational potential. Nicolas HIOT actively contributes to the LIFO research laboratory at the University of Orleans, collaborating extensively with Jacques CHABIN, Mirian HALFELD-FERRARI, and Dominique LAURENT. His technical output includes multiple software systems for database evolution management and clinical text processing, reflecting both theoretical contributions and practical implementations in semantic data management.

Matthieu EXBRAYATمشاهده پروفایل

مدرس

• Machine Learning
• Data Analysis
• Data Visualization
• +۷ مورد دیگر

Matthieu EXBRAYAT is a Lecturer in Computer Science at the University of Orleans, affiliated with the Fundamental Computer Science Laboratory of Orléans (LIFO). He holds significant administrative responsibilities including Vice President for Digital and Educational Innovation at the University of Orleans and previously served as Co-head of the IT department (2019-2021) and Head of the IMIS Computer Science Masters (2015-2021). His research spans multiple domains with a primary focus on Machine Learning applications in data analysis and visualization, time series analysis, and computer vision applications in archaeology. His work demonstrates strong interdisciplinary connections between computer science and archaeological documentation, particularly in ceramic sherd classification using deep learning techniques. Earlier in his career, he contributed significantly to high-performance databases and probabilistic relational learning, especially Markov logic networks. Analysis of his publication history reveals an evolving research trajectory: beginning with database systems and parallel join algorithms in the early 2000s, progressing through Markov logic networks and clustering algorithms in the 2010s, and more recently focusing on machine learning applications in archaeological imaging and programming language analysis. His recent publications show a clear shift toward practical applications of machine learning in diverse domains while maintaining theoretical rigor. Throughout his career, EXBRAYAT has maintained extensive collaborative research networks, with Lionel Martin appearing as a co-author on 20 publications, followed by Guillaume Cleuziou (14), Jacques-Henri Sublemontier (9), and Quang-Thang Dinh (7). These collaborations span multiple research domains and demonstrate his ability to work across disciplinary boundaries. As an educator, he teaches distributed information systems, programming languages, databases, AI/data mining, and geographic information systems. He has also been actively involved in scientific mediation, delivering numerous public lectures on artificial intelligence topics since 2017, including conferences such as "Living in harmony with AIs" (2023) and "Artificial Intelligence: My new toaster is an AI!" (2019). His administrative contributions are substantial, including roles as Communications Correspondent for the Computer Science Disciplinary Center, Facilitator of the DataCenters ComUE Centre Val de Loire reflection group, and Vice President of Digital Resources at the Leonardo da Vinci Confederal University. He also served as LIFO website webmaster for over a decade (2002-2014) and was responsible for the STIC degree program (now IT degree) from 2004-2008.

Denis Kuperbergمشاهده پروفایل

پژوهشگر

• automata theory
• synthesis
• verification
• +۶ مورد دیگر

Denis Kuperberg is a CNRS researcher at LIP (Laboratoire de l'Informatique du Parallélisme), ENS Lyon, where he is part of the Plume research team. His work bridges theoretical computer science with interdisciplinary applications, particularly in systems biology. His primary research interests include automata theory, synthesis, verification, games, logics, decidability procedures, complexity, and proof theory. Kuperberg's work often explores the connections between formal methods and practical applications, as evidenced by his recent interdisciplinary publications on thermodynamic consistency of autocatalytic cycles. His research demonstrates a strong focus on both theoretical foundations and practical implementations, with several software tools developed for research purposes. Kuperberg's publication record shows a clear evolution from purely theoretical work in automata and logic toward more interdisciplinary research. His recent papers (2022-2025) demonstrate increasing engagement with biological applications while maintaining strong theoretical foundations. Key themes across his publications include history-determinism in automata, cyclic proof systems, positive fragments of logic, and applications to verification problems. Best Paper Award at ICALP 2025 (with Thomas Colcombet and Amina Doumane) Kuperberg actively supervises multiple students at various levels, including PhD candidates (Iris Magniez–Papillon, Émile Hazard, Laureline Pinault), postdoctorates (Thomas Kosc, Marc Bagnol), and numerous interns. His teaching portfolio includes advanced courses on Graphs, Machines and Logics at ENS Lyon Master 2 program, Theory of Regular Languages at EPITA Lyon, and specialized topics like Mathematical Aspects of Automata Theory. He leads the development of research software including EmergeNS (for simulating chemical reaction networks and tracking autocatalytic dynamics), Stamina and Acme (for solving decision problems from automata theory), and Electrum (a specification language analyzer). These tools demonstrate his commitment to bridging theoretical computer science with practical implementations.

Marie-Christine ROUSSETمشاهده پروفایل

استاد

• Knowledge Representation
• Information Integration
• Ontology-based data access
• +۷ مورد دیگر

Marie-Christine ROUSSET is a Professor of Computer Science at the University of Grenoble Alpes (UGA) in France, where she is a member of the LIG (Laboratoire d'Informatique de Grenoble) in the SLIDE group. Previously affiliated with Paris-Saclay (LRI), she has established herself as a leading researcher in Knowledge Representation and Information Integration. She holds the distinguished position of Senior member of the Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) and serves as co-responsible for the chair Explainable and Responsible AI within MIAI Grenoble Alpes. Her research focuses on ontology-based data access, logic-based mediation between distributed data sources, query rewriting using views, data linkage, and distributed reasoning for the Semantic Web. She skillfully combines artificial intelligence and database techniques to address complex information integration challenges, with applications spanning biomedical informatics, educational technology, and trustworthy AI. Her work demonstrates consistent innovation from foundational research to practical implementations, as evidenced by her co-authorship of the book 'Web Data Management' published by Cambridge University Press. Professor ROUSSET's recent publications (2019-2022) reveal a growing emphasis on data privacy, RDF graph anonymization, and interactive ontology engineering, while maintaining her strong contributions to semantic web technologies and knowledge representation. Her research shows increasing attention to trustworthy AI concerns, aligning with her leadership roles in relevant projects. Scientific Recognition Senior member of Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) Junior member of Institut Universitaire de France (IUF) from 1997 to 2002 Chevalier de l'Ordre National du Merite (July 11, 2011) EurAI Fellow (nominated ECCAI Fellow in 2005) Best Paper Award at AAAI'96 for 'Verification of Knowledge Bases based on Containment Checking' Professor ROUSSET maintains an active role in the scientific community through editorial work and organizational leadership. She serves on the Editorial Board of Communications of the ACM (CACM) and has held significant roles including PC chair of EGC 2019, Workshops co-Chair of WWW 2018, and Area Chair of IJCAI 2017. Her consistent service on program committees of major international conferences demonstrates her standing in the field. Her laboratory, the SLIDE group within LIG, focuses on semantic web technologies, knowledge representation, and data integration. The group maintains strong connections with the international research community and participates in collaborative projects addressing cutting-edge challenges in artificial intelligence and data management, with particular emphasis on trustworthy and explainable AI systems.

Brice Chardinمشاهده پروفایل

دانشیار

• Data Engineering
• Clustering Algorithms
• Query Relaxation
• +۵ مورد دیگر

Brice Chardin is an Associate Professor in Data Engineering at ISAE-ENSMA since 2013, affiliated with the LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) Data and Model Engineering team. His work bridges academic research and industrial applications, focusing on data management solutions for critical systems. His research spans clustering algorithms under dissimilarity constraints , RDF query relaxation for explaining empty/overabundant results, pattern mining through the RQL language, and energy data management . Key projects include Chronos (a NoSQL system for industrial sensor data) and collaborations with energy companies SRD and Nexeya for predictive consumption analysis. Recent publications (2021-2024) emphasize constrained clustering techniques and cooperative query processing for RDF knowledge bases, revealing a strong trend toward practical solutions for industrial data challenges. His work integrates machine learning with database theory to address real-world data imperfections. PhD in Computer Science from INSA Lyon (2011) Postdoctoral position at LIRIS (2012-2013) on ANR DAG project Specialized in industrial data management since 2011 EDF collaboration Chardin actively supervises academic projects including drone simulation with Ardupilot and Smart Data mining initiatives. His industrial partnerships focus on energy sector applications, particularly predictive analysis for electricity distribution and storage systems. Current work involves developing clustering algorithms with error bounds and query relaxation frameworks for semantic web technologies.