جستجوی استاد راهنمای دانشگاهی

Maya Ramanath is an Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology (IIT) Delhi. She joined IIT Delhi in 2011 after a postdoctoral research stint at the Max-Planck Institute for Informatics in Germany. Her research interests focus on database systems, information retrieval, semantic web technologies, and knowledge graph construction and applications. Education: PhD in Computer Science, Indian Institute of Science, Bangalore M.Sc.(Engg.) in Computer Science, Indian Institute of Science, Bangalore B.E. in Computer Science and Engineering, Bangalore University, Bangalore Her recent work emphasizes efficient query processing over large-scale graphs, knowledge graph applications, and natural language interfaces for semantic data. Notable contributions include algorithms for reachability approximation in web-scale graphs, speculative query planning for knowledge graphs, and exploratory querying techniques. She has collaborated extensively on projects like NAGA, ESTHETE, and KlusTree, advancing the state of the art in graph-based data management and semantic search. Publications span conferences such as ICDE, ECIR, EDBT, and VLDB, reflecting a strong focus on database systems, graph algorithms, and semantic web applications. Her work bridges theoretical foundations with practical implementations, addressing scalability and efficiency challenges in modern data management systems. Research and advising activities include supervision of projects on distributed graph processing, query optimization, and knowledge representation. She has contributed to open-source tools like LegoDB and StatiX, and her lab focuses on interdisciplinary approaches to data-centric AI.

• Database Systems
• Information Retrieval
• Semantic Web Data Management
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Jean-François Le Gall is a full Professor at Université Paris-Saclay and a member of the Orsay Mathematics Laboratory (LMO) since 2006. He has held prominent positions at Pierre and Marie Curie University (1988-2006) and École Normale Supérieure (1997-2007). A Senior Member of the University Institute of France (2007-2017) and an elected member of the Academy of Sciences since 2013, he served as Vice-President of Research for the Mathematics Department at Orsay (2020–present) and led the ERC Advanced Grant GeoBrown (2017–2023). Education: Ecole Normale Supérieure (1978–1982), PhD in stochastic differential equations (1982), State Doctorate on Brownian motion (1987) Research Interests focus on probability theory , particularly Brownian motion , superprocesses , random trees , planar maps , and their connections to PDEs and geometric models. His work bridges stochastic analysis , branching processes , and coalescence phenomena . Selected Publications include foundational studies on the Brownian map , random geometry , and spatial branching processes . His 2025 paper on The area of spheres in the Brownian plane explores fractal properties of random metric spaces, while the 2020 Growth-fragmentation processes work links Brownian trees to fragmentation models. Scientific Distinctions : 1986 Rollo Davidson Prize 1997 Loève Prize in Probability 2005 Sophie Germain and Fermat Prizes 2019 Wolf Prize in Mathematics 2022 BBVA Frontiers of Knowledge Award Academic Leadership includes directing the Probability and Statistics Team (2013–2019) and the Master 2 in Probability and Statistics (2007–2015). He chairs editorial roles in Grundlehren der mathematischen Wissenschaften (since 2020) and Probability Theory and Related Fields (2005–2010).

• Probability
• Brownian motion
• Lévy processes
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Igor Kortchemski is a CNRS researcher at the Department of Mathematics and Applications (DMA) at École Normale Supérieure, Paris, and a lecturer in the Department of Applied Mathematics at École Polytechnique. His primary research focuses on the continuous limits of random discrete models, particularly examining how discrete combinatorial structures converge to continuous objects under appropriate scaling. His educational background includes a PhD in Mathematics (2012) under Jean-François Le Gall at École Normale Supérieure and a Habilitation à diriger des recherches (HDR) in Mathematics (2016). Kortchemski's research spans several interconnected areas: Random trees and Galton-Watson processes with heavy-tailed distributions Random planar maps and their geometric properties Growth-fragmentation processes and their connections to Lévy processes Scaling limits of combinatorial structures and their continuous counterparts His publication record shows a consistent focus on the geometric properties of random discrete structures, with recent work (2023-2025) exploring uniform attachment processes with freezing, critical tree phenomena, and the mesoscopic geometry of sparse random maps. His research often involves sophisticated probabilistic analysis combined with combinatorial insights. Scientific recognition includes: prix de thèse solennel Perrissin-Pirasset / Schneider de la chancellerie des Universités de Paris (2012) Kortchemski actively contributes to academic service: Examiner for the minor math exam at École Polytechnique (FUF) since 2023 Member of the mathematics jury for ENS International Selection (2023) Member of the jury for the external mathematics competitive examination (Agrégation) since 2021 Member of the jury for the Arts and Economic and Social Sciences Bank (B/L) mathematics exams (2015-2018) He mentors the next generation of researchers as co-director of Antoine Aurillard's and Vanessa Dan's theses (both since 2023), and previously directed Etienne Bellin's (2020-2023) and Paul Thevenin's (2017-2020) theses.

• Probability Theory
• Random Discrete Structures
• Random Trees
• +۶ 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.

• Database Query Optimization
• Fine-Grained Complexity
• Enumeration Complexity
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Petr Kuznetsov is a Professor at Telecom Paris (Institut Polytechnique de Paris), affiliated with the Department of Computer Science and Networks (INFRES). He leads the Autonomous Critical Embedded Systems (ACES) research team at the Information Processing and Communication Laboratory (LTCI). His work bridges theoretical foundations and practical applications in distributed systems. Research Focus: Kuznetsov specializes in distributed algorithms, synchronization protocols, failure detection mechanisms, and the application of algebraic topology to distributed computing. His recent work explores Byzantine fault tolerance, blockchain consensus, and concurrency in networking infrastructures. Recent Publication Trends (2015-2025): His articles predominantly focus on scalability and resilience in distributed systems, with emerging themes in blockchain technologies, Byzantine fault tolerance, and concurrency optimization. Theoretical contributions include computability theorems and complexity bounds, while applied work targets payment systems and distributed ledgers. Awards & Honors: Best Paper Award at DISC 2019 for Scalable Byzantine Reliable Broadcast Best Student Paper Award at PODC 2018 for An Asynchronous Computability Theorem for Fair Adversaries Projects & Advising: He directs the TrustShare Innovation Chair (large-scale data synchronization) and DISCMAT (mathematical foundations of distributed computing). Actively seeks PhD candidates for projects on distributed algorithms and concurrency. Laboratory & Teams: Heads the ACES team at LTCI, focusing on critical embedded systems and fault-tolerant distributed architectures. Collaborates internationally through workshops like SPTDC.

• Distributed Systems
• Algorithms
• Synchronization
• +۳ مورد دیگر

Ioan Todinca is a Professor of Computer Science at the University of Orléans, France, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades, with significant contributions to theoretical computer science, particularly in graph algorithms and distributed computing. Faculty of Science, University of Orléans LIFO Research Laboratory Member of Institut thématique pluridisciplinaire Modélisation, Systèmes, Langages (since 2014) Former director of MIPTIS doctoral school (2012-2014) Former head of Computer Science degree program (2007-2011) Former leader of LIFO Graphs, Algorithms and Computational Models team (2008-2012) Todinca's research focuses primarily on graph algorithms, with expertise in exact algorithms (moderately exponential), parameterized algorithms, and algorithms for specific graph classes. He has made significant contributions to techniques involving tree decompositions, treewidth, minimal separators, and potential maximal cliques. More recently, his work has expanded into distributed algorithms, especially in communication-constrained models like the broadcast congested clique. His research bridges theoretical foundations with practical algorithmic approaches for NP-hard problems. The analysis of his recent publications reveals a strong trend toward distributed computing problems, particularly in congested network models. His work spans from fundamental graph theory problems (cycle detection, graph modification) to applications in quantum computing and model checking. The consistent focus on communication complexity, verification, and efficient algorithms across different computational models demonstrates his ability to adapt theoretical computer science principles to emerging computational paradigms. Todinca has supervised numerous PhD students and has been actively involved in the theoretical computer science community through conference organization and editorial work. His publications appear consistently in top-tier venues including SIAM Journal on Computing, Algorithmica, and proceedings of major conferences like STACS, WG, and DISC. Teaching responsibilities include algorithms, graph theory, and discrete structures for undergraduate and graduate students, with previous experience teaching databases, programming, and software engineering. His educational materials are hosted on the university's Celene platform.

• Graph Algorithms
• Theoretical Computer Science
• Exact Algorithms
• +۶ مورد دیگر

Stéphan Thomassé is a Professor in the Computer Science Department at Ecole Normale Supérieure de Lyon, where he is affiliated with the Parallel Computing Laboratory (LIP) and leads the Computational Models, Complexity, Combinatorics (MC2) research team (2023-2025). He was a Member of the University Institute of France from 2016-2021 and has held significant administrative roles including Head of M2 Fundamental Computer Science (2013-2015) and Deputy Director of the Computer Science Department (2013-2015). Thomassé's research focuses on graph theory, combinatorics, algorithms, and theoretical computer science. His work spans structural graph theory , parameterized complexity , graph coloring , and combinatorial optimization . He is particularly known for his groundbreaking work on twin-width, a graph parameter that has revolutionized understanding of sparse and dense graph classes and their algorithmic properties. His research connects combinatorial structures with computational complexity, often bridging theoretical insights with practical algorithmic applications. His recent publications demonstrate a strong focus on structural graph theory, with the twin-width series representing a major contribution to the field. These papers have established connections between graph parameters, model theory, and algorithm design, particularly for problems that were previously thought to be intractable on dense graph classes. Thomassé's work on graph coloring, particularly regarding dense triangle-free graphs and the Erdős-Hajnal conjecture, has resolved long-standing open problems. Member of University Institute of France (2016-2021) ANR projects: GRAAL, AGAPE, STINT, COMPA, Digraphs, TWIN-WIDTH, GODASse As an advisor and educator, Thomassé has taught numerous advanced courses including Algorithmics, Optimization and Approximation, Tree Decompositions and FPT Algorithms, and Graph Decompositions. His teaching reflects his research expertise, emphasizing both theoretical foundations and practical applications of discrete mathematics. His administrative leadership in the MC2 team and the Computer Science Department demonstrates his commitment to advancing research and education in theoretical computer science. Thomassé maintains active collaborations with researchers worldwide, as evidenced by his extensive publication record with co-authors from various institutions. His work continues to shape the landscape of structural graph theory and parameterized algorithms, with recent results opening new avenues for research in combinatorics and theoretical computer science.

• Graph Theory
• Combinatorics
• Algorithms
• +۳ مورد دیگر

BEZOUI Madani is a Researcher-Lecturer at CESI, affiliated with the 'Engineering and Numerical Tools' research team. He holds a PhD in Operational Research from the University of Science and Technology Houari Boumediène (2019), with a focus on multi-objective programming in portfolio optimization. His academic roles include serving as a pedagogical tutor for FISA training courses and heading the 'Data Sciences' program for 5th-year Computer Science Engineers at CESI. His research interests center on Industry 4.0/5.0, optimization of complex systems, machine learning, IoT/BIM technologies, and scheduling. Notable work includes integrating human-centricity and sustainability into digital twin models and advancing hybrid metaheuristics for multi-objective manufacturing optimization. Recent publications (2021–2024) address preference-driven optimization methods, tabu search algorithms, and IoT network vulnerability detection. He has authored a book on Euclidean graph boundaries and contributed to frameworks for flexible job shop scheduling. His ongoing research focuses on decision-maker preference integration in dynamic scheduling under Industry 5.0 contexts. Advising and grants: No specific advising roles or grants mentioned in the CV. His educational activities emphasize pedagogical leadership in data science and operational research. Labs/teams: Active member of CESI’s Engineering and Numerical Tools group, collaborating on IoT, digital twins, and optimization projects.

• Operations Research
• Scheduling
• Machine Learning
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Bastien Mallein is a Professor of Mathematics at the University of Toulouse III Paul Sabatier, where he is affiliated with the Institut de Mathématique de Toulouse (IMT). His research focuses on probability theory, particularly branching processes, random walks, and their applications in statistical mechanics and mathematical physics. Education: PhD in Mathematics (2012–2015), Université Pierre et Marie Curie, supervised by Zhan Shi Agrégé-préparateur (2013–2016), École Normale Supérieure Postdoctoral Researcher (2016–2017), Universität Zürich under Jean Bertoin Research Interests: Mallein studies branching processes, including branching random walks, branching Brownian motion, and Lévy processes. His work explores extreme value theory, phase transitions, and the behavior of stochastic systems over time. He has contributed to understanding critical phenomena in random trees, percolation models, and population dynamics. Recent Publications: His recent work addresses reinforced Galton-Watson processes, extremal point processes, and applications to evolutionary models. Key topics include phase transitions, large deviations, and stochastic stability. Students: He advises PhD students such as Simon Delalande (2024–) and Lianghui Luo (2023–), focusing on branching processes and extreme value analysis. Notable co-advised students include Elie Cerf (2020–2024) and Mohamed Ali Belloum (2018–2021). Labs/Teams: Active member of the IMT Probability group. Collaborates internationally on projects involving stochastic processes and mathematical physics.

• Probability Theory
• Stochastic Processes
• Branching Processes
• +۳ مورد دیگر

Patrick Bas serves as Research Director and Thematic Group Facilitator for the Data Intelligence Group (DatInG) at the University of Lille, France, where he leads the Signal and Images team within the CRISTAL laboratory. His office is located at ESPRIT in Lille's Scientific City, and he holds membership on the institution's Scientific Council. Bas earned his Electrical Engineering degree (1997) and Ph.D. in Signal and Image Processing (2000) from the Institut National Polytechnique de Grenoble. His academic journey includes postdoctoral work at Université Catholique de Louvain, CNRS research at Gipsa-Lab (2001-2009), and a visiting position at Helsinki University of Technology (2005-2008). His primary research centers on steganography, steganalysis, and digital watermarking, with significant contributions to image manipulation detection and neural network applications in multimedia security. He has organized major initiatives including the BOSS steganalysis contest (2010) and served as co-coordinator for the Ecrypt European NoE's watermarking virtual lab (2004-2008). While his Google Scholar profile indicates recent publications in graph theory and combinatorics (2021-2025), these appear inconsistent with his documented research focus on multimedia security as evidenced by his thesis supervision and professional activities. Bas has supervised seven doctoral theses including Rony Abecidan's work on robust image manipulation detection (2024) and Solène Bernard's research on neural network steganography (2021). He has held editorial roles for the Eurasip Journal on Information Security (since 2011) and IEEE Transactions on Information Forensics and Security (2013-2016), and co-organized the International Workshop on Information Hiding (IH07).

• Steganography
• Steganalysis
• Digital Watermarking
• +۲ 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.

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

Irène Marcovici is a Professor at the University of Rouen Normandy, affiliated with the Raphaël Salem Mathematics Laboratory (LMRS) and leading the Probability and Dynamic Systems Team. Her research spans probability theory, cellular automata, stochastic processes, and combinatorics, with a focus on ergodicity, percolation, and self-organization phenomena. She collaborates with institutions like the GDR Fundamental Computer Science and its Mathematics and has contributed to journals such as Probability Theory and Related Fields, Annales Henri Lebesgue, and Theoretical Computer Science. Education: Habilitation à Diriger des Recherches (2021, University of Lorraine), PhD in Mathematics (2013, University of Paris Diderot) Research Focus: Marcovici's work explores probabilistic cellular automata, percolation models, and their applications in physics, computer science, and mathematics. Key projects include analyzing stability regions in queueing systems, developing decentralized diagnostics, and studying self-descriptive sequences. Her articles highlight interdisciplinary connections between discrete mathematics and stochastic dynamics. Notable Collaborations: She has co-authored publications with researchers like Jérôme Casse, Régine Marchand, Nazim Fatès, and Mathieu Sablik. Her team participates in the ALEA and SDA2 working groups under GDR Fundamental Computer Science and its Mathematics.

• Probability Theory
• Cellular Automata
• Dynamic Systems
• +۳ مورد دیگر

Jérôme Casse is an Assistant Professor of Mathematics at the University Paris-Saclay, affiliated with the Probability and Statistics team at the Mathematics Department (IMO). He holds teaching roles at the IUT of Sceaux (School of Marketing and Management), NYU Shanghai, and Mines Nancy. His research focuses on probability theory, stochastic dynamical systems, combinatorics, and integrable systems, with particular emphasis on probabilistic cellular automata (PCA), nearest neighbour trees, and models like the 8-vertex system. Research interests span reversible stochastic systems, ergodic theory, spatial stochastic processes, and their applications in statistical mechanics. His work often explores the interplay between discrete and continuous probabilistic structures, with a focus on invariant laws, integrable models, and processes iterated ad libitum. Key contributions include studies on Poisson-Kirchhoff systems, edge correlations in lattice models, and the dynamics of random trees with catastrophes. Teaching responsibilities include calculus, probability, graph theory, and partial differential equations across institutions like IUT GEA (192h/year) and NYU Shanghai. His publications (15+ articles since 2012) reflect expertise in stochastic processes, cellular automata, and their applications in both pure and applied mathematics. Laboratory affiliations include the Laboratoire de Mathématiques d'Orsay (LMO), where he occupies office 3E3, and the IUT of Sceaux (office 401). No specific grants or awards are highlighted in the provided materials.

• Probability
• Stochastic Processes
• Combinatorics
• +۳ مورد دیگر

Giovanni Neglia is a Research Director (DR2) at Inria, affiliated with the NEO team and the 3IA Côte d'Azur Chair, focusing on performance evaluation of distributed systems, cache networks, and large-scale machine learning. He is based in Sophia Antipolis, France. Inria, Sophia Antipolis NEO Team 3IA Côte d'Azur Chair His research interests center on distributed systems, leveraging mathematical tools such as Markov processes, control theory, fluid models, and game theory. Key areas include caching policies, distributed optimization for machine learning, and performance modeling of networks. He has previously worked on P2P networks, delay tolerant networks, smart grids, and complex networks. Recent publications demonstrate a strong focus on similarity caching, federated learning, and distributed optimization. Trends include developing online learning algorithms for caching, improving privacy and efficiency in federated systems, and optimizing content delivery in 5G and small cell networks. Best paper award at ITC-33 IEEE Infocom Distinguished TPC member (2017, 2018, 2022) Best paper award at ITC28 2016 Best paper award at IEEE Online Greencomm 2014 Best paper award at IEEE Infocom NetSciCom 2014 Best paper award at IEEE VTC2013-Spring Best student paper award at VALUETOOLS 2012 Best paper award at BIONETICS 2007 Giovanni Neglia leads the Fed-Malin Inria challenge on federated machine learning and has secured grants including a 3IA Côte d'Azur chair for PERUSALS (Pervasive Sustainable Learning Systems). He advises PhD students such as Othmane Marfoq and Chuan Xu, and collaborates with Accenture Labs, SAP, and Nokia Bell Labs. He has organized winter schools and tutorials on complex networks and similarity caching. He leads the NEO team and previously led the Maestro team. He is involved in the joint Brazilian-French research team Thanes on network science and has served on numerous technical program committees for major conferences including IEEE Infocom, INFOCOM, and SIGMETRICS.

• Performance Evaluation of Distributed Systems
• Cache Networks
• Distributed Machine Learning
• +۷ مورد دیگر

Mr. Nicolas THIBAULT is a Lecturer in Computer Science at Paris-Panthéon-Assas University, affiliated with the Center for Research in Economics and Law (CRED). His academic career combines teaching and research in theoretical computer science, with a focus on algorithms and network optimization. His research interests include: Algorithms (particularly randomized and truthful scheduling) Dynamic graph maintenance and incremental/decremental tree problems Online computation and bicriteria optimization Network interconnection and parallel machine scheduling Recent publications highlight his work on: Truthful mechanisms for weighted completion times Disturbance minimization in connection trees Hardness results for multi-group interconnection Competitive analysis of online scheduling Optimal rebuilding strategies for dynamic trees Scientific awards include the Best Young Researcher Article at AlgoTel 2006 for his work on connection tree updates. He co-heads the Professional License program in Organizational Management, specializing in Network and Information Systems Management.

• Computer Science
• Algorithms
• Parallel Computing
• +۳ مورد دیگر