Motonobu Kanagawa is an Assistant Professor (Maitre de Conférences) in the Data Science Department at EURECOM since September 2019. Previously, he was a researcher at the University of Tuebingen and the Max Planck Institute for Intelligent Systems in Germany under Prof. Philipp Hennig (2017–2019), and completed his PhD at the Institute of Statistical Mathematics in Tokyo under Prof. Kenji Fukumizu (2013–2016). Education: Doctorate in Statistical Science (2016), Graduate University for Advanced Studies / Institute of Statistical Mathematics, Japan Master in Computer Science (2013), Nara Institute of Science and Technology, Japan Research Interests: His work focuses on kernel methods, Gaussian processes, and machine learning for enhancing computer simulations. Key areas include statistical validation of simulation models, applications in climate, disaster, economic, and financial analysis, and interpretable distribution comparison. He emphasizes bridging theory and practice in probabilistic numerics and Bayesian optimization. Publications Trends: His recent work addresses challenges in Bayesian quadrature, Gaussian process scaling, covariate shift adaptation, and pension system risk analysis. He frequently explores kernel-based approaches for computational efficiency and interpretability. Awards: Chris Daykin Prize (2022) from the International Actuarial Association for work on pension system risk analysis Grants & Leadership: He chairs a position at 3IA Cote d'Azur (2021) and organizes the ProbNum 2025 conference. His advisory roles include editorial board membership at the Journal of Machine Learning Research (JMLR) and reviewing for top venues like TPAMI, NeurIPS, and ICML. Labs & Collaboration: Active in the Data Science Department at EURECOM,他曾领导Max Planck的Probabilistic Numerical Computing Group，并与EDHEC等机构合作研究养老金系统风险。他的研究团队通过贝叶斯优化和核方法推进跨领域应用。
Motonobu Kanagawa is an Assistant Professor (Maitre de Conférences) in the Data Science Department at EURECOM in France since 2019. Previously, he held research positions at the University of Tübingen and the Max Planck Institute for Intelligent Systems in Germany under Prof. Philipp Hennig. He earned his PhD in 2016 from the Institute of Statistical Mathematics in Tokyo under Prof. Kenji Fukumizu. His research focuses on statistical methodologies for complex systems simulation, including reliability validation of simulators, Gaussian processes, Bayesian optimization, and kernel methods. Recent work explores variable selection in distribution comparison and covariate shift adaptation. Key achievements include organizing the inaugural ProbNum 2025 conference, receiving the Chris Daykin Prize (2023) for pension system analysis with Bayesian optimization, and securing a chair position at the 3IA Côte d'Azur AI institute (2021). He reviews for leading journals like JMLR and conferences such as NeurIPS and ICML. Publications span topics like k-NN regression optimization, Gaussian process scale parameter estimation, and counterfactual mean embeddings. His work bridges theoretical statistics and applied machine learning, emphasizing computational efficiency and uncertainty quantification.
Alix Lhéritier serves as a Research Professor and Chair holder at the 3IA Institute of University of the Côte d'Azur, affiliated with EURECOM. His primary role is at Amadeus, France, where he leads machine learning research for travel industry applications. Education: Computer Engineer, University of the Republic, Montevideo (2004) M.Sc. in Computer Science, University of the Republic, Montevideo (2010) Ph.D. in Computer Science, University of Nice Sophia Antipolis (2015) His research focuses on sequential decision problems, statistical dissimilarity, and choice modeling. Current work develops probabilistic regression methods with epistemic uncertainty estimation to improve air travel experiences in adversarial scenarios, bridging theoretical machine learning with real-world travel industry challenges. No scientific awards or student advisees are documented. Research is industry-driven through Amadeus collaboration without specified academic labs or teams.
Bow-Yaw Wang is a Professor in the Department of Computer Science and Information Engineering at National Taiwan University's College of Electrical Engineering and Computer Science, where he conducts foundational research in programming languages and formal verification methodologies. He received his Ph.D. from Yale University, establishing the theoretical framework for his expertise in software correctness and security analysis. His educational background enables rigorous approaches to complex computational problems. Wang's research centers on Programming Languages and Formal Methods , with significant contributions to Software Verification , Security , Cryptography , and Privacy . He develops static analysis techniques to eliminate vulnerabilities in cryptographic implementations and privacy-preserving systems, focusing on mathematical rigor to ensure software reliability in security-critical applications. Analysis of his publication trajectory reveals consistent innovation in verification frameworks, evolving from arithmetic overflow detection in cryptographic C programs (2019) to LLVM-based verification tools (2023) and probabilistic privacy models (2022). His work demonstrates increasing sophistication in bridging theoretical formal methods with practical security challenges. No major scientific awards are documented in available sources, though his sustained contributions to premier conferences like POPL, ASE, and VMCAI indicate significant peer recognition within the programming languages community. As an academic advisor, Wang mentors graduate students in programming languages and security research at National Taiwan University, though specific advisee names aren't enumerated in current datasets. Information regarding research grants remains undisclosed in publicly accessible materials. He maintains active research collaborations within National Taiwan University's programming languages group, focusing on advancing verification techniques for next-generation security-critical software systems.
Kristóf Marussy is an Assistant Professor at the Department of Artificial Intelligence and Systems Engineering , Budapest University of Technology and Economics , Hungary. His work bridges model-driven engineering with dependability analysis and logic solvers , focusing on critical cyber-physical systems in railway, automotive, and aerospace domains. Assistant Professor (2025–) Research Fellow (2023–2024), Assistant Research Fellow (2020–2023) Contributor to Refinery (graph solver for model generation) and Ferdium (open-source messaging suite) Research Interests center on: Formal Verification of critical system designs Automated Reasoning via logic/numerical solvers Graph Generation for testing data-driven systems AI Integration in system modeling Recent Publications demonstrate expertise in: Graph solver infrastructure (Refinery framework) Cyber-physical system architecture synthesis Formal methods for API key security (PKCE workflow) Constraint optimization in model generation Probabilistic analysis for system reliability Awards & Scholarships : 2024 EKÖP Postdoc Fellowship 2023 ÚNKP Researcher Prize 2023 Josef Heim Award for innovation 2021 OOPSLA Artifact Reviewer Recognition Academic Service includes committee roles at OOPSLA 2025, LLM4MDE 2024, ECOOP 2023, and FASE 2025. He actively mentors 7 BSc and 5 MSc students while contributing to European Space Agency's VAMPIR project on AI-enhanced positioning systems.
Yannis Haralambous is a full professor at the Data Science Department of IMT Atlantique, Brest, France. He is a member of the DECIDE research group affiliated with the CNRS Lab-STICC laboratory. His career spans academia and entrepreneurship, with a focus on natural language processing, grapholinguistics, and knowledge representation since transitioning from algebraic topology and digital typography research in the 1990s. Research Interests Natural Language Processing with emphasis on explainable AI and speech analysis Grapholinguistics, particularly script reform and writing systems Explainable Representation of Complex Systems using automata and time series Fuzzy Logic applications in recruitment analytics Semantic Web extensions to pragmatic and social representation Teaching Graph theory, logic, knowledge representation, and computational linguistics Curriculum design for the Data Science Master of Science at IMT Atlantique Recent Publications 2024: 550-page Springer textbook on NLP with exercises and educator resources 2023: Frameworks for Pragmatic Web and fuzzy logic in recruitment (SN Computer Science, Future Internet) 2022: Conference papers on resume segmentation, job offer term extraction, and complex system modeling Projects 2024-2026: COGITAMUS project on spontaneous e-learning from open-source data Laboratory collaborations with Lab-STICC and La Mètis company Academic Background 1990 Ph.D. in Algebraic Topology (University of Lille I) 2020 HDR (University of Western Brittany) Professional Timeline 1990-2001: Digital typography R&D and Inalco lectureship 2001-present: IMT Atlantique professor 2010: Full DECIDE team member
Daniel W. Barowy is an Associate Professor in the Department of Computer Science at Williams College . His research focuses on programming languages , particularly end-user programming , crowdsourcing , and spreadsheet debugging . He integrates program analysis with statistical techniques to improve software usability and robustness. Current affiliations: Williams College (2017-present) Education: University of Massachusetts Amherst (PhD, 2017) His research explores language abstractions for human-computer integration , with notable projects like ExceLint (spreadsheet error detection), FlashRelate (spreadsheet data extraction), and Riker (incremental build systems). He emphasizes artifact verification , securing PLDI 2015 Distinguished Artifact Award and USENIX ATC 2022 Best Paper . Recent publications demonstrate trends in spreadsheet reliability , crowdsourcing frameworks , and scalable educational tools . He has received "Artifact Verified" badges for multiple projects and actively contributes to software tool development , including AutoMan , ExceLint , and CheckCell . USENIX ATC 2022 Best Paper PLDI 2015 Distinguished Artifact Award Multiple "Artifact Verified" badges As an educator, he teaches CSCI 334: Principles of Programming Languages and CSCI 331: Computer Security . His work bridges theoretical PL research with practical applications in spreadsheet programming and crowdsourcing platforms .
Faizal HAFIZ is a Postdoctoral Research Fellow at SKEMA Business School, France, specializing in computational intelligence and machine learning applications. He holds a PhD from The University of Auckland (2020) and previously served as a Research Assistant Professor at King Saud University (2010-2016) and Utility Assistant Manager at Reliance Energy Ltd. (2008-2010). His research focuses on multi-criteria optimization, neural network architectures for financial forecasting, epidemic control strategies, and energy-efficient building systems. Education: PhD in Computational Intelligence, University of Auckland, 2020 Research interests span computational intelligence, machine learning, and their applications in systems control, supply chain resilience, and financial modeling. His work integrates game theory for epidemic policy analysis and employs advanced optimization techniques like particle swarm algorithms and epsilon-constraint methods. His recent publications address epidemic control strategies in heterogeneous networks, stock market forecasting using sparse neural architectures, and balancing energy consumption with thermal comfort in buildings. Collaborations include studies on supply chain reconfiguration under divergent policies and the psychological impacts on epidemic dynamics. Scientific Awards: New Zealand International Doctoral Scholar in Computational Intelligence and Control (2016-2020) Advising and Grants: Co-supervised M. MUBASHIR WANI's PhD thesis (2022) Labs/Teams: Affiliated with SKEMA's Centre for Analytics and Management Science , focusing on interdisciplinary research in operations research and data-driven decision-making.
Jacques Déverchère is a Professor of Seismo-geology at the University of Brest (France), affiliated with the European University Institute of the Sea (IUEM) and the Geo-Ocean Joint Research Unit 6538. He holds dual deputy director roles as Deputy Director for Training at IUEM and Deputy Director of the ISblue University Research School, while coordinating the Hubert Curien Maghreb Partnership. His expertise spans seismicity analysis, tectonic inversion, salt tectonics, and paleoseismology with active fieldwork in Algeria, Liguria, Tanzania, and the Mediterranean Basin. Research focuses on continental margin evolution, crustal structure analysis, and geodynamic processes combining seismic data interpretation, numerical modeling, and field observations. Notable projects include the SPIRAL and SEFASILS cruises investigating Algerian and Ligurian basin structures. He also explores educational innovations using immersive VR for marine science training. Over 100+ publications span 1986–2024, emphasizing: (1) Tectonic inversion mechanisms in convergent margins (Algerian margin), (2) Messinian salt dynamics and their seismic implications, (3) Rift basin evolution in East Africa and South America, and (4) Machine learning applications to seismicity declustering. Active in international committees including CNRS-INSU, Hcéres, and ANR evaluations.
Pascale Le Gall is an active researcher specializing in formal methods and computer science, with primary research conducted through the Mathematics and Computer Science for Complexity and Systems laboratory. Their work spans theoretical computer science, software engineering, and interdisciplinary applications in biological systems modeling. Le Gall's research focuses on formal verification techniques, particularly in conformance testing, symbolic execution, and graph transformations. Their work bridges theoretical computer science with practical applications in distributed systems verification, geometric modeling, and biological network analysis. Key research themes include developing frameworks for stochastic process discovery, feature interaction resolution, and topological operations in geometric modeling, demonstrating both theoretical depth and practical implementation value across multiple domains. Analysis of their recent publications reveals a strong trend toward interdisciplinary applications of formal methods, particularly in biological systems. Their work increasingly integrates statistical approaches with traditional formal verification techniques, as seen in Bayesian inference for process discovery and statistical model checking of biological pathways. The research shows consistent development of symbolic execution techniques applied to increasingly complex systems, from abstract data types to distributed biological networks. Pascale Le Gall maintains active collaborations with researchers including Christophe Gaston, Marc Aiguier, and Paolo Ballarini across multiple projects. Their publication record shows consistent output with significant contributions to model-based testing frameworks, geometric modeling using graph transformations, and formal analysis of biological systems. The researcher has contributed to both theoretical foundations and practical implementations of verification techniques, with numerous conference papers and journal articles spanning over 15 years of active research.
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.
Madalina Deaconu is an Inria Research Director and Assistant Scientific Delegate (DSA) at the Inria Center of the University of Lorraine since January 2022. She leads the PASTA joint project team (Processus Aléatoires Spatio-Temporels et leurs Applications) hosted at the Institut Elie Cartan de Lorraine (IECL). Her academic affiliation is with the Faculty of Science and Technology at the University of Lorraine, where she conducts research in probability theory and stochastic modeling. She serves on multiple committees including the Inria Evaluation Commission, the Geophysics Program Committee of the RT CNRS "Earth & Energies", and has held leadership positions including Director of the Charles Hermite Federation (2018-2022). Dr. Deaconu completed her Habilitation à diriger des recherches (HDR) at Université Henri Poincaré – Nancy 1 in 2008 and earned her PhD in Stochastic Processes and Partial Differential Equations from the same institution in 1997. Habilitation à diriger des recherches (HDR), Université Henri Poincaré – Nancy 1, France, 2008 PhD in Stochastic Processes and Partial Differential Equations, Université Henri Poincaré – Nancy 1, France, 1997 Madalina Deaconu's research focuses on stochastic modeling with applications across multiple domains. Her primary areas include data-enriched stochastic modeling, probabilistic approaches to coagulation/fragmentation models, numerical methods for diffusion reach times, and stochastic methods for linear and nonlinear partial differential equations. Her work bridges theoretical probability with practical applications in environmental science, geophysics, and actuarial science. She develops innovative probabilistic simulation methods and analyzes random spatio-temporal processes, with particular emphasis on fragmentation equations, Bessel processes, and Hawkes processes. Her research has significant applications in modeling avalanches, natural disasters, insurance risk assessment, and hydrochemical data analysis. She has established international collaborations with researchers from University of Turin, University of Uruguay, and institutions in Bucharest, as well as national collaborations with University of Burgundy and INRAE Grenoble. Analysis of Dr. Deaconu's recent publications reveals a consistent focus on fragmentation processes and stochastic modeling approaches. Her work spans theoretical developments in probability theory, numerical methods for stochastic processes, and practical applications in environmental science and actuarial mathematics. A notable trend is her increasing focus on Bayesian inference methods and point process applications, particularly for environmental and insurance contexts. Her most recent work demonstrates strong interdisciplinary connections, applying stochastic methods to hydrochemical data analysis, insurance recommendation systems, and natural disaster modeling. The consistent thread throughout her publications is the development and application of sophisticated probabilistic techniques to solve complex real-world problems. Dr. Deaconu has received recognition for her contributions to research: Paper and Poster award at EWEA 2015 (European Wind Energy Association) Plenary speaker at the 14th International Conference on Monte Carlo Methods and Applications (MCM23) While specific student names aren't listed in the provided text, Dr. Deaconu is involved in doctoral training through the IECL and supervises research in stochastic modeling. She coordinates multiple research collaborations including industrial partnerships with Le Foyer Luxembourg and SnT Université du Luxembourg (2018-2022). Her teaching activities include Stochastic Modeling at Master 2 level, Stochastic Differential Equations at École des Mines de Nancy, and Monte Carlo Simulation for Financial Market Engineering. Dr. Deaconu leads the PASTA research team (Processus Aléatoires Spatio-Temporels et leurs Applications), a joint Inria project hosted at IECL. She previously directed the Charles Hermite Federation (2018-2022), which brought together three major research laboratories: CRAN (Automatic Control), IECL (Mathematics), and LORIA (Computer Science). Her work is centered at the Institut Elie Cartan de Lorraine, a mathematics research institute affiliated with the University of Lorraine, where she contributes to both theoretical developments and practical applications of stochastic methods across multiple scientific domains.
Antoine Lejay is a Researcher in the Department of Probability and Statistics at the Faculty of Science and Technology, Université de Lorraine. He is affiliated with the Inria PASTA project team and contributes to interdisciplinary initiatives like the Inria Apollon Exploratory Action (2022–2024) and the CNRS MITI project (2024–2025). His roles include Deputy Director of the AM2I division and former Head of the Probability and Statistics team (2016–2022). His research spans Rough trajectories , Stochastic analysis , and Probabilistic numerical methods , with applications in Fragmentation equations , Diffusion modeling , and Digital Humanities . He develops algorithms for stochastic processes in discontinuous media and statistical estimators for non-standard models like skew Brownian motion. Recent publications highlight methodological advancements in Rough differential equations , Hawkes processes for insurance risk, and Fragmentation dynamics . His work combines theoretical analysis (e.g., asymptotic behavior) with computational frameworks (e.g., random walk simulations, interface conditions). Lejay has held leadership positions in the Charles Hermite Federation (2022–2023) and the GdR TRAG (2018–2023). He collaborates with academic and industrial partners, focusing on interdisciplinary applications in insurance, engineering, and porous media.
Dr. Saïd Moussaoui is a Professor in the Department of Automation and Robotics at École Centrale de Nantes , affiliated with the Nantes Digital Sciences Laboratory (LS2N) and the Signal, Image and Sound research team. His work spans machine learning, medical imaging, and signal processing, with a focus on EEG-based mental workload classification, PET reconstruction, and 4D Flow MRI optimization. Research Areas : Signal Processing, Medical Imaging, Machine Learning, Neuroscience, Biomedical Engineering Labs/Teams : LS2N Laboratory, Signal, Image and Sound Team Recent publications highlight trends in graph learning for EEG analysis, deep learning regularization in PET imaging, and super-resolution techniques in cardiovascular MRI. His research integrates physics-based models with data-driven approaches for applications in healthcare and industrial predictive maintenance. The LS2N laboratory provides a multidisciplinary environment for his work, combining advanced computational methods with real-world applications in biomedical engineering and robotics. Collaborations with institutions like IEEE and projects on digital twins underscore his technical leadership in applied research.
Christel VRAIN is a full-time University Professor affiliated with the University of Orleans, specializing in Machine Learning and Constraint Programming. Her research focuses on constrained clustering, knowledge integration, and hybrid AI systems, with applications in image classification, time series analysis, and geospatial data. She collaborates extensively with researchers like Thi-Bich-Hanh DIEP-DAO and Samir LOUDNI. University Professor at University of Orleans Affiliated with Laboratoire d'Informatique Fondamentale d'Orléans (LIFO) Her work bridges declarative programming with machine learning, emphasizing explainability and optimization. Recent publications explore continual learning, graph models, and constraint-based clustering frameworks. She contributes to interdisciplinary research through the Kay R. Amel group, investigating synergies between reasoning, knowledge representation, and data mining. Her methodological innovations include memory-efficient algorithms for large-scale datasets and shapelet transforms for time series.
