جست‌وجوی استاد و پژوهشگر

• 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)

Fadila Bentayebمشاهده پروفایل

دانشیار

• NoSQL Data Warehouses
• Text Warehousing
• Personalization & Recommendation Systems
• +۵ مورد دیگر

Fadila Bentayeb is an Associate Professor (HDR) at the University of Lyon 2, affiliated with the ERIC Laboratory and the Department of Informatics and Statistics (DIS). She leads the Complex Data Warehousing and OLAP research team (SID) and has served as Head of the first year of the Master's degree in Computer Science at the Institute of Communication (ICOM) since 2014. Her work bridges academic research and practical applications in data-intensive systems. HDR (Habilitation to Direct Research), University of Lyon 2 (2011) Ph.D. in Computer Science, University of Orléans (1998) Her research focuses on NoSQL data warehouses, text warehousing, and personalization systems, with emphasis on scalable architectures and context-aware analytics. Recent work explores AI-driven security in IoT networks and temporal modeling for evolving data structures. Key trends in her publications include: (1) Graph-based multidimensional modeling (GAMM framework), (2) Optimization of distributed big data warehouses using machine learning, (3) Medical text classification via hybrid CNN-LSTM models, and (4) Temporal data warehouse evolution. As part of the ERIC Laboratory’s leadership team, she contributes to strategic research directions and mentors students in advanced data systems. Her technical expertise extends to Hadoop/Spark optimization, OLAP cube design, and semantic integration of heterogeneous data sources.

Beatrice Markhoffمشاهده پروفایل

استاد

• Semantic Web
• Knowledge Engineering
• Semantic Interoperability
• +۵ مورد دیگر

Beatrice Markhoff is a Professor of Computer Science (CNU 27) at the University of Tours , affiliated with the Faculty of Science and Technology (Blois site) and the Computer Science Department . She is a member of the UMR CNRS 7324 - CITERES - Archaeology and Territories Laboratory (CITERES-LAT) and the Maison des Sciences de l'Homme Val de Loire (MSH VdL) . Her research focuses on semantic interoperability , Web of data , knowledge representation , and knowledge extraction , with significant collaborations in cultural heritage disciplines . Doctorate in Computer Science, University of Franche-Comté (1995) HDR (Habilitation to Direct Research), François Rabelais University of Tours (2013) Her research themes include semantic interoperability, knowledge engineering, and data mining, with applications in cultural heritage. She co-created the International Workshop on Semantic Web for Cultural Heritage and co-edited special issues for the Semantic Web Journal and JOCCH . She leads projects like ANR SESAMES (2018-2023), H2020 ARIADNEplus (2019-2022), and H2020 4CH (2021-2023). Her academic contributions span XML data management, semantic web technologies, and functional programming. Key projects include carto4CH for cultural heritage mapping and OpenArchaeo for knowledge graph profiling. She has co-supervised PhD theses on topics like LOD querying and knowledge extraction from Wikidata .

• Machine Learning
• Structured Prediction
• Tree Automata
• +۶ مورد دیگر

Marc Tommasi is a Professor in Computer Science at the University of Lille, affiliated with the CRIStAL laboratory where he leads the Magnet research team. Previously, he was a founding member of the Mostrare project team at INRIA Lille. His academic career spans research in machine learning foundations and applications to structured data. His research focuses on: Machine learning theory and algorithms Structured prediction for trees and graphs Tree automata and set constraints Decentralized learning systems Sparse representations for structured data Natural language processing techniques Information extraction from semi-structured data His publication portfolio demonstrates consistent focus on spectral methods for graph learning, probabilistic tree modeling, and conditional random fields, with applications spanning network analysis, XML processing, and natural language tasks. Recent work emphasizes large-scale network modeling and spectral clustering techniques. He has supervised 13 PhD students including ongoing work in decentralized machine learning (Mahsa Asadi), privacy-preserving speech recognition (Brij Srivastava), and multilingual dependency parsing (Mathieu Dehouck), with completed theses covering graph construction, spectral clustering, and tree transducer learning. Significant research projects under his coordination include Pamela (decentralized ML), Lampada (structured data representations), Marmota (statistical ML for trees), Crotal (CRFs for NLP), ATASH (document transformations), and WebContent (information extraction). He maintains active involvement in the Magnet team at CRIStAL focusing on machine learning for graphs and NLP.

Patrick MARCELمشاهده پروفایل

استاد

• Databases
• Exploratory Data Analysis
• OLAP
• +۴ مورد دیگر

Patrick MARCEL is a Full Professor at the Department of Computer Science, University of Orléans, and a member of the LIFO laboratory. He holds a PhD from INSA Lyon (1998) and a Habilitation à Diriger les Recherches from Université de Tours (2012). His research focuses on databases, exploratory data analysis, OLAP, data mining, and recommendation systems. He serves as an associate editor for the DKE journal and leads the Steering Committee of the DOLAP workshop, co-chairing in 2017 and 2021. Education: PhD in Computer Science, INSA Lyon, 1998 Habilitation à Diriger les Recherches, Université de Tours, 2012 Research Interests: Exploratory Data Analysis & OLAP Data Mining and Machine Learning Personalization and Recommendation Systems Context-Aware Data Quality Management Emotional Data Interaction His work emphasizes intention-driven analytics, cube query optimization, and narrative generation in business intelligence. Publications: Over 80 articles spanning OLAP systems, data quality, and recommendation techniques, with recent emphasis on emotional intelligence in query design and matheuristics for optimization problems. His 2024 work explores intentional aspects in data narratives and context-aware methodologies. Awards: None explicitly listed. However, his editorial roles and workshop leadership highlight peer recognition. Advising & Grants: No student names listed, but active in mentoring through research projects. His work is supported by collaborations with industry and academic partners. Labs & Teams: Member of the LIFO lab (Laboratoire d’Informatique Fondamentale d’Orléans), team Pamda. Engaged in interdisciplinary research on data platforms and analytical processing.

KOUNTOURIS Mariosمشاهده پروفایل

استاد

• Communication Theory
• Machine Learning for Wireless Communications
• Low Latency Networking
• +۳ مورد دیگر

Marios Kountouris is a Professor at EURECOM in the Department of Communication Systems. He holds a Ph.D. in Electrical Engineering from Telecom ParisTech (France), an M.S. from Telecom ParisTech, and a Diploma in Electrical & Computer Engineering from the National Technical University of Athens (Greece). His career includes roles as Principal Scientist at Huawei Technologies (France), Assistant/Associate Professor at CentraleSupélec (France), Adjunct Professor at Yonsei University (South Korea), and Post-doctoral researcher at the University of Texas at Austin (USA). His research focuses on communication theory, machine learning for wireless communications, low latency networking, and stochastic modeling. Notable contributions include work on ultra-reliable low-latency communication (URLLC), network densification, and semantic communication frameworks. Distinctions: IEEE Communication Society Young Author Best Paper Award 2020 2016 IEEE ComSoc Communication Theory Technical Committee Early Achievement Award 2013 IEEE ComSoc Outstanding Young Researcher Award (EMEA Region) Recent research trends emphasize semantic-aware resource allocation, federated learning in wireless networks, and next-generation waveforms like AFDM. His articles explore optimization frameworks, real-time systems, and AI-driven telecom innovations.

Jiasi Shenمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Program Analysis
• +۳ مورد دیگر

Jiasi Shen is an Assistant Professor in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology. She leads the HKUST Automated Reasoning and Transformation of Software research group. PhD and Master's from Massachusetts Institute of Technology Bachelor's from Peking University Her research focuses on automating software development through program analysis , program transformation , and active learning . She explores how to systematically introduce safety checks, optimize performance, and enable cross-platform adaptation while maintaining core functionality. Recent work includes: Dynamic graph-based fingerprinting for cryptomining detection Benchmarking LLMs for operating system verification tasks Improving program comprehension via deimplicitization techniques She has received the Distinguished Artifact Award at SLE 2017 and serves on program committees for OOPSLA, Onward!, and SPLASH conferences. Her group supervises multiple PhD and MPhil students while developing systems like Konure (database application modeling) and KumQuat (parallel Unix command synthesis).

Bassem Haidarمشاهده پروفایل

پژوهشگر

• Electric Vehicles
• Energy Storage
• Renewable Energy
• +۳ مورد دیگر

Bassem Haidar is a researcher specializing in energy systems, electric vehicle infrastructure, and techno-economic analysis. His work focuses on optimizing renewable energy integration, evaluating public charging networks, and addressing challenges in electric vehicle adoption. He has collaborated with institutions on projects such as the PEPS5 energy storage study and analyses of French EV market dynamics. His research bridges technical innovation, policy design, and socio-economic factors influencing sustainable transportation. Key research areas include: Electric Vehicle Charging Infrastructure Renewable Energy Transition Data-Driven Policy Analysis Privacy-Preserving Machine Learning Recent publications emphasize the interplay between energy storage technologies, battery lifecycle management, and long-term energy mix planning. His work on federated learning for trajectory generation highlights advancements in privacy-aware data synthesis.

• Machine Learning
• Supervised Learning
• Cluster Analysis
• +۷ مورد دیگر

Marcilio Pereira de Souto is a full professor at the University of Orléans , leading the Fundamental Computer Science Laboratory (LIFO) . His research focuses on machine learning, cluster analysis, and bioinformatics, with a strong emphasis on data complexity and hybrid intelligent systems. He has previously held positions at institutions including the Max Planck Institute of Molecular Genetics and the Federal University of Rio Grande do Norte (UFRN). Education: B.Sc. in Computer Science (UFRN, 1991), M.Sc. in Computer Science (UFPE, 1995), Ph.D. in Electrical Engineering (Imperial College London, 1999) Research Interests Marcilio's work spans machine learning (supervised, unsupervised, and ensemble methods), cluster analysis (multi-objective, constrained, and fuzzy clustering), data complexity (measuring classification difficulty, imbalanced datasets), and bioinformatics (gene expression analysis, cancer data clustering). He also explores hybrid intelligent systems combining multiple techniques for improved performance. Publication Trends His publications (h-index 18) emphasize clustering algorithms, data complexity metrics, and hybrid approaches applied to bioinformatics and computational biology. Recent work focuses on explainable AI for image captioning, while earlier contributions analyze missing data imputation strategies and meta-learning for algorithm selection. Labs & Teams Currently leads research at LIFO , a fundamental computer science laboratory at the University of Orléans. Previously contributed to the Department of Informatics and Applied Mathematics (DIMAp) at UFRN and the Department of Computational Biology at the Max Planck Institute.

Jolan PHILIPPEمشاهده پروفایل

مدرس

• Distributed Systems
• Infrastructure as Code (IaC)
• Model Driven Engineering
• +۵ مورد دیگر

Jolan PHILIPPE is a Lecturer at University of Orléans affiliated with LMV research laboratory, and currently holds a postdoctoral position at Université de Rennes in the DiverSE team under the supervision of Olivier Barais. His research focuses on Infrastructure as Code (IaC) and distributed systems reconfiguration, with particular emphasis on formal methods and model-driven approaches. His primary research interests include: Distributed Systems and Cloud Computing Infrastructure as Code (IaC) and DevOps Model Driven Engineering Formal Methods and Verification Parallel and Distributed Programming Algorithmic Skeletons Dr. PHILIPPE's work demonstrates a strong trajectory from foundational research in parallel programming models (particularly through his work on PySke) to applied research in distributed system reconfiguration. His recent publications show increasing focus on formal approaches to infrastructure management, with significant contributions to languages like Concerto-D and frameworks like Ballet for cross-DevOps reconfiguration. His research bridges theoretical computer science (formal methods, verification) with practical cloud infrastructure concerns. He is actively supervising two PhD candidates: Haitam El Hayani (Taranis project, 2024-2027) - Extending Infrastructure as Code (IaC) languages, focusing on enhancing capabilities and integration with modern cloud deployment practices Olivia Proust (For-Coala project, 2025-2028) - Towards formally verified configuration management languages Dr. PHILIPPE is deeply involved in research coordination: Coordinator of the "Défi GDR GPL" (challenge) ADDYCT (ADaptation DYnamique et ConTinue) for 2026-2030 One of the leaders of the GDR GPL's GT SyLA (Groupe de Travail - Système Logiciel Adaptable) since February 2025 Helped organize the VELVET (Verification and Software Engineering for DevOps and Reconfiguration) days in Nantes, December 2023 His service to the academic community includes: Program Committee Member for DebConf 2025, ICCS 2025, LowCode@MODELS 2025 Publicity Chair and Program Committee Member for UCC 2025 and BDCAT 2025 Reviewer for journals (COLA, SoSyM) and multiple international conferences

Dmitriy Traytelمشاهده پروفایل

دانشیار

• Interactive Theorem Proving
• Runtime Verification
• Logic
• +۱۱ مورد دیگر

Dmitriy Traytel is an Associate Professor at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen, where he leads the Software, Data, People & Society (SDPS) section. He earned his PhD from TU München under the supervision of Tobias Nipkow in 2015 and previously held a senior researcher position at ETH Zürich's Information Security Group. His primary research interests include interactive theorem proving, runtime verification, logic, automata, decision procedures, and coinduction. He works extensively with the Isabelle/HOL proof assistant, developing foundational theories and verified tools. His work bridges theoretical logic and practical system verification, focusing on correctness, expressiveness, and scalability. The recent publications highlight a strong focus on verified runtime monitoring, formalization of logical systems, and efficient query evaluation. Key themes include the development of formally verified monitoring tools (e.g., TimelyMon, WhyMon, VeriMon), foundational work on corecursion and datatypes in Isabelle, and translations of logical formalisms into executable and verifiable code. Several publications have received distinguished paper awards, indicating high impact in the programming languages and verification communities. Distinguished Paper Award, POPL 2025: 'Barendregt Convenes with Knaster and Tarski: Strong Rule Induction for Syntax with Bindings' Distinguished Paper Award, POPL 2023: 'Admissible Types-to-PERs Relativization in Higher-Order Logic' Distinguished Paper Award, ATVA 2018: 'Optimal Proofs for Linear Temporal Logic on Lasso Words' Best Student Paper Award, FSCD 2016: 'Formal Languages, Formally and Coinductively' Traytel has (co)supervised numerous PhD, MSc, and BSc students, many of whose projects contribute directly to his research agenda in verified systems and formal methods. He is actively involved in the academic community, serving on program committees for POPL, ITP, RV, and CPP, and has chaired conferences such as CPP 2023 and 2024. His tools, including TimelyMon, VeriMon, and WhyMon, are practical outcomes of his research, enabling scalable, explainable, and trustworthy runtime verification. He leads a research group focused on trustworthy stream processing, distributed streaming computations, and explainable monitoring. His work often involves collaboration with researchers at ETH Zürich and other institutions, particularly in the areas of security and monitoring.

Gábor Bergmannمشاهده پروفایل

پژوهشگر

• Model-Driven Engineering
• Incremental Computing
• Query Evaluation
• +۵ مورد دیگر

Gábor Bergmann is a researcher at Budapest University of Technology and Economics, Hungary, and a member of the MTA-BME Lendület Research Group on Cyber-Physical Systems. He actively contributes to the fields of model-driven engineering and incremental computing, with a focus on efficient query evaluation and program analysis. His research interests include: Model-Driven Engineering Incremental Computing Query Evaluation Program Analysis using Datalog Static and Whole-Program Analysis Cyber-Physical Systems The recent publications of Gábor Bergmann demonstrate a strong trend in incremental computation techniques applied to program and model analysis, particularly leveraging Datalog and lattice-based frameworks. His work focuses on optimizing performance and scalability of analyses through change-driven evaluation, with applications in model synchronization, inter-procedural analysis, and EMF-based systems. He has served on program committees, particularly in the Foundations Track of the MODELS conference series, indicating active engagement in the academic community. While no formal advising or grant information is available from the provided text, his involvement in a Lendület research group suggests participation in funded research projects. He has not received any explicitly mentioned scientific awards in the provided data.

Aikaterini TZOMPANAKIمشاهده پروفایل

استادیار

• Data Privacy
• Database Systems
• Query Optimization
• +۶ مورد دیگر

Aikaterini TZOMPANAKI is an Assistant Professor and associated member of the ACMES team at Télécom SudParis. Her research focuses on database systems, data privacy, query optimization, and provenance analysis. She has contributed to topics such as differential privacy, SQL debugging, and explainability in data streaming and collaborative filtering. Education: PhD in Database Systems from Université Paris Saclay (2015), titled 'Missing Answers: Debugging and Repairing Queries.' Research Interests: Privacy-preserving data publishing and differential privacy for time series. Why-Not Questions and provenance-based explanations for queries and collaborative systems. SQL query refinement and automatic debugging techniques. Pattern matching and visualization of regular expressions. Key Trends in Articles: Focus on privacy and security in dynamic data environments. Advancement of query explainability and provenance systems. Integration of machine learning in database systems and recommender systems. Awards: No scientific awards explicitly mentioned. Grants & Advising: No grants or student advising details provided in the text. She is affiliated with the SAMOVAR research laboratory at Télécom SudParis.

Mathias Géryمشاهده پروفایل

مدرس

• Structured Information Retrieval
• Social Information Retrieval
• Multimedia Information Retrieval
• +۱ مورد دیگر

Mathias Géry is a Lecturer in Computer Science at Jean Monnet University, affiliated with the Computer Science Department within the Faculty of Science and Technology. He conducts research as a member of the Data Intelligence Group at the H. Curien Laboratory (UMR CNRS 5516) in Saint-Étienne, France. Research Focus: His work centers on advanced Information Retrieval systems, with four core pillars: Structured IR (specializing in textual IR, XML, hypertexts, and structured documents), Social IR (analyzing social networks and relationships), Multimedia IR (developing text-image fusion models and representation frameworks), and Web Mining (including usage analysis, corpus collection, and web analytics). His methodologies consistently integrate user profiles and social context to enhance personalized information access. Publication Trends: Recent publications (2015-2024) reveal a strong trajectory in personalized and social IR, with significant contributions to query expansion, language modeling, and social bookmarking systems. His interdisciplinary reach extends to health informatics (2024 RCT on sedentary adults), sports science (2023 gender-based endurance analysis), quantum physics (2021 turbulence study), and literary analysis (2019 Russian literature), demonstrating exceptional versatility across computational and humanities domains. Collaborative Environment: As an active researcher within the CNRS-affiliated H. Curien Laboratory, he contributes to the Data Intelligence Group's mission of advancing data-driven methodologies through structured, social, and multimodal approaches.

• Semantic Data Integration
• Ontology-based Database Design
• Big Data & Cloud Computing
• +۵ مورد دیگر

Ladjel Bellatreche is a Full Professor at the National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA) in Poitiers, France, where he has been a faculty member since September 2010. He leads the Data and Model Engineering Team of the Laboratory of Computer Science and Automatic Control for Systems (LIAS). Prior to his current position, he spent eight years as Assistant and then Associate Professor at Poitiers University. His academic journey includes visiting positions at Université du Québec en Outaouais (Canada), Purdue University (USA), and Hong Kong University of Science and Technology (China). Professor Bellatreche's research interests span multiple domains in data management and engineering, with a focus on Semantic Data Integration, Ontology-based Database Design, Life Cycle of Extremely Large Database Design, Big Data & Cloud Computing, Green Computing, and Database Deployment. His work bridges theoretical foundations with practical applications in data-intensive systems. His publications reflect a strong trend toward addressing challenges in big data analytics, semantic data integration, and energy-efficient database systems. The research demonstrates a progression from traditional data warehousing techniques to more advanced approaches incorporating semantic web technologies, linked open data, and green computing principles. His work increasingly focuses on scalability, efficiency, and the integration of diverse data sources. Professor Bellatreche has received recognition through his service as an Editorial Board Member for the International Journal of Reasoning-based Intelligent Systems and as subject area editor of the Scalable Computing Journal. He has also served as a reviewer for prestigious journals including IEEE TKDE and Distributed and Parallel Database Journal. His leadership extends to organizing major international conferences such as DAWAK, DOLAP, MEDI, and WISE. With over forty program committee memberships, he plays a significant role in shaping research directions in data management. Additionally, he actively promotes research in Africa and Asia through student supervision and conference organization.