جستجوی استاد راهنمای دانشگاهی

Matteo Brunelli is Associate Professor of “Mathematical Methods of Economics and Actuarial and Financial Sciences” at the University of Trento , Department of Industrial Engineering, and Adjunct Professor (docent) at Lappeenranta University of Technology , Finland. He is nationally habilitated as Full Professor in Italy and has held long-term visiting positions at Berkeley, Turku, Auckland, JAIST and Binghamton. Education: Ph.D. (Doctor of Science) in Information Technologies, Åbo Akademi University, Finland, 2011 – graded Eximia cum laude approbatur M.Sc. in Economics, University of Trento, 2007 – grade 110/110 cum laude B.Sc. in Economics, University of Trento, 2005 Research focus: Brunelli’s work sits at the intersection of multi-criteria decision analysis , operations research and computational optimisation . He develops axiomatic foundations and algorithms for pairwise comparison matrices , consistency indices , the best-worst method and fuzzy preference relations , and applies them to energy planning, sustainable inventory, maintenance scheduling, 3-D printer selection, and blockchain governance. His 2023-2025 articles reveal intensified interest in uncertainty modelling (Dempster-Shafer theory), bi-objective optimisation of inventory and maintenance, and group decision protocols that integrate probabilistic or active-learning components, demonstrating both methodological depth and practical relevance. Scientific awards & grants: Academy of Finland Postdoctoral Researcher grant (€254 670, 2014-2017) Claudio Dematté Research Grant (€19 000, 2008) Teacher of the Year Award, Aalto University (2013 – both Spring & Autumn semesters) Bernard Roy Award 2021 for outstanding contribution to Multiple Criteria Decision Aiding (under-40 category) Supervision & funding: While specific doctoral students are not listed, Brunelli currently supervises graduate theses at Trento and has continuously held competitive national grants. His Academy of Finland project “Consistency of valued preference relations for decision analytics methods” financed three years of full-time research and international collaboration. Editorial & community roles: He serves on the editorial boards of International Journal of General Systems and Mathematical and Computational Applications , and acts as area editor for Journal of Multi-Criteria Decision Analysis , positioning him among the key gatekeepers of the MCDA community.

• Multi-criteria decision analysis
• Operations research
• Fuzzy sets and fuzzy numbers
• +۷ مورد دیگر

Leonard Wesley is an Associate Professor in the Department of Computer Science at the College Of Science, San Jose State University. His research spans interdisciplinary domains at the intersection of Bioinformatics , Computational Biology , and Machine Learning , with specific applications in Pharmaceutical Drug Discovery , Genomic Data Analysis , and Autonomous Robotics . Education: Ph.D. in Computer Science, University of Massachusetts M.S. in Computer Science, University of Massachusetts B.A. in Physics and Math, Northeastern University Research Interests include Approximate Reasoning (probabilistic, evidential, and fuzzy logic), Agent-Oriented Systems , and Sensor Fusion . His work applies these methodologies to Drug Portfolio Management , Protein Structure Scoring , and Medical Diagnostics . Publication Trends show a consistent focus on Computational Biology , Robotics , and Uncertainty Quantification over four decades. Early work in Computer Vision evolved into modern applications in Pharmaceutical Analytics and AI in Aerospace . Key Projects include SVM-based drug affinity prediction, evidence-driven decision support systems for biopharma, and real-time agent development frameworks like ROADS. He has contributed to CFD code control and Mobile Network Congestion solutions. Collaborations with institutions like NASA, Los Alamos National Laboratory, and international conferences (WMSCI, ICINCO, AIAA) highlight his cross-disciplinary impact. His teaching includes Artificial Intelligence and Bioinformatics courses.

• Bioinformatics
• Computational Biology
• Pharmaceutical Drug Discovery & Development
• +۱۳ مورد دیگر

Chung-Wei Lin is an Associate Professor and Deputy Director at the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia at National Taiwan University. His research focuses on cyber-physical systems, particularly in the domains of connected and autonomous vehicles, system security, and design methodologies. He maintains active collaborations with industry partners including Toyota and has established himself as a leading researcher in intelligent transportation systems in Taiwan. Education: Ph.D. (2015) from Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (Advisor: Alberto L. Sangiovanni-Vincentelli) M.S. (2007) from Graduate Institute of Electronics Engineering, National Taiwan University (Advisor: Yao-Wen Chang) B.S. (2005) from Department of Computer Science and Information Engineering, National Taiwan University Dr. Lin's research interests center on cyber-physical systems with specific focus on connected and autonomous vehicles, security mechanisms, and system design methodology. Before returning to NTU in 2018, he worked at Toyota InfoTechnology Center, USA, Inc. His recent projects cover diverse topics including systems engineering, formal verification for robustness and compatibility, runtime monitoring, and intelligent intersection management. His work bridges theoretical foundations with practical applications, addressing real-world challenges in transportation systems through innovative technical solutions. Analysis of Dr. Lin's recent publications (2023-2025) reveals a strong emphasis on intelligent transportation systems with particular focus on security challenges for connected vehicles, formal verification techniques for safety-critical systems, and novel control algorithms for vehicle coordination. His research demonstrates increasing integration of machine learning approaches, especially reinforcement learning, to address complex decision-making problems in transportation. The work spans multiple technical domains including control theory, networking, cybersecurity, and formal methods, reflecting the inherently interdisciplinary nature of cyber-physical transportation systems research. Selected Awards: 2016 Best Paper Award, ACM Transactions on Design Automation of Electronic Systems 2015 Most Accessed ESL Paper Best Paper Award, IEEE ISSREW 2016 workshop Best Paper Award, ICCD 2010 Best Paper Nominee, ASP-DAC 2015 Dr. Lin currently advises multiple Ph.D. and M.S. students, with research focusing on various aspects of cyber-physical systems for transportation. His group includes Ph.D. students Pintusorn Suttiponpisarn and I-Ching Tseng, as well as several M.S. students. His extensive publication record and numerous patents (over 20 granted) indicate significant research impact and likely substantial research funding from both government and industry sources. His research program demonstrates strong translational potential, with many concepts moving from theoretical foundations to practical implementations. Dr. Lin leads the Cyber-Physical Systems Laboratory at NTU, which focuses on research related to intelligent transportation systems. The lab conducts research in areas including vehicle control, intersection management, security mechanisms, and formal verification for cyber-physical systems. His team collaborates with researchers from various institutions globally, as evidenced by his extensive publication record with international co-authors from universities and research institutions in the United States, Japan, and Europe.

• Cyber-physical systems
• Connected and Autonomous Vehicles
• System Security
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Dr. Francisco J. Aparicio Navarro serves as Associate Professor in Cyber Security and Programme Leader for BSc/MSc Digital Technology Solutions Degree Apprenticeships at De Montfort University's School of Computer Science and Informatics. An expert in Computer Network Security and Intrusion Detection Systems, his research develops novel unsupervised detection frameworks integrating Contextual Information and Situational Awareness for cyber-attack identification in networked environments. His research spans Network Security, Machine Learning, and Cyber Situational Awareness with specialized expertise in Penetration Testing, Cyber Threat Intelligence, and Incident Response. Key contributions include statistical approaches for multi-stage attack detection, data fusion techniques for anomaly identification, and security solutions for Connected Autonomous Vehicles developed in partnership with Airbus. His work bridges theoretical advances in cryptographic protocols with practical defense mechanisms for critical infrastructure. Recent publications demonstrate evolving focus from foundational wireless intrusion detection (2017-2018) toward sophisticated APT prediction using machine learning correlation analysis (2018-2019), vehicle cybersecurity (2019-2020), and emerging areas like medical imaging watermarking (2023) and lightweight cryptographic solutions (2024-2025). Cross-cutting themes include contextual information integration, IoT privacy compliance, and distributed authentication mechanisms. Scientific recognition includes: Fellowship of the Higher Education Academy (FHEA) He currently supervises PhD candidates Tareq Ahmad Alhajahjeh (Cyber Threats Intelligence using BRNN-LSTM) and Ademola Elijah Owaraye (Zero-Trust Security in Fog Computing), following successful completion of Dr. Raphael Martin Juergen Riebl's thesis on Vehicular AdHoc Networks. Research funding includes Innovate UK projects SACRED (Co-Investigator), INSURE Phases 1-2 (Principal Investigator), and FLOURISH (Co-Investigator with Airbus), alongside EPSRC/Dstl collaborations on networked battlespace security. As Director of the Cyber Security Academic Startup Accelerator Programme, he fosters industry-academia partnerships while maintaining active consultancy in penetration testing and incident response for commercial clients.

• Network Security
• Intrusion Detection Systems
• Cyber Situational Awareness
• +۱۰ مورد دیگر

K.C. Chang is a Professor in the Department of Systems Engineering and Operations Research at George Mason University's Volgenau School of Engineering. He is the Director of the Sensor Fusion Lab and the coordinator of the Financial Engineering Program at GMU, reflecting his interdisciplinary research focus on data fusion and probabilistic reasoning. Education: PhD, Electrical Engineering, University of Connecticut MS, Electrical Engineering, University of Connecticut BS, Communication Engineering, National Chiao-Tung University Dr. Chang's research centers on distributed data fusion, Bayesian networks, and decision-making under uncertainty, with applications in sensor networks, wireless communications, satellite systems, and financial engineering. His work integrates statistical modeling, machine learning, and systems engineering principles to solve complex, real-world problems involving uncertainty and large-scale information processing. The recent publications reflect a strong trend in applying probabilistic reasoning to dynamic spectrum access, satellite communication, and electronic warfare. His research leverages copula functions and information genealogy to enhance fusion accuracy in non-Gaussian and dependent environments. Applications span defense, aerospace, and finance, demonstrating the versatility of his methodological contributions. Scientific Awards: IEEE Fellow (2010) for contributions to sensor data fusion and Bayesian probabilistic inference Dr. Chang has advised numerous graduate students and led major research initiatives funded by the Office of Naval Research (ONR), National Science Foundation (NSF), Army Research Office (ARO), Air Force Office of Scientific Research (AFOSR), and industry partners like Intelligent Fusion Technology Inc. and Impact Technologies, LLC. His editorial roles include serving as Editor for Tracking/Navigation Systems and Large Scale Systems in IEEE Transactions on Aerospace and Electronic Systems, and as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics. He was also Technical Program Co-Chair of the 2009 International Conference on Information Fusion. He leads the Sensor Fusion Lab at George Mason University, a research group focused on advancing the theory and application of multi-sensor, multi-source information fusion. The lab works on foundational algorithms as well as real-world implementations in defense, communication, and financial systems. The lab collaborates with government agencies and private sector organizations to translate theoretical advances into operational technologies.

• Data Fusion
• Bayesian Networks
• Probabilistic Reasoning
• +۹ مورد دیگر

Isabelle Bloch is a full Professor at Télécom Paris (Institut Polytechnique de Paris) and Emeritus Professor at the University of Bordeaux . She heads the Image, Modeling, Analysis, Geometry, Synthesis (IMAGES) research team within the Information Processing and Communication Laboratory (LTCI) in the Image, Data, Signal (IDS) Department . Education & Affiliations Professor – Télécom Paris, Institut Polytechnique de Paris (current) Professeure Émérite – University of Bordeaux (honorary) Research Interests Her work lies at the intersection of computer science, applied mathematics and medicine . Core themes include mathematical morphology , fuzzy and bipolar logics , 3-D image interpretation , discrete 3-D geometry & topology , information fusion , structural pattern recognition , spatial reasoning and medical imaging . Recent projects extend these concepts to explainable AI , argumentation theory , computational musicology and robotic surgery guidance . Scientific Awards Blondel Medal (2008) – awarded by the French Electrical & Electronics Engineering Society to outstanding young scientists. Grants & Collaborative Projects While specific grant numbers are not listed in the text, Prof. Bloch coordinates and participates in large-scale interdisciplinary projects funded by national (ANR) and European programs, spanning medical AI, pediatric oncology imaging, neuro-informatics and ethical AI. Labs & Teams She leads the IMAGES team (≈ 25 researchers) inside the LTCI – a joint research unit of Télécom Paris and CNRS. The group develops theory and open-source software in mathematical morphology, deep learning and symbolic AI, and collaborates closely with clinical partners at Necker–Enfants Malades Hospital, AP-HP and Gustave Roussy.

• Mathematical morphology
• Fuzzy set theory and bipolar fuzzy sets
• 3D image interpretation and computer vision
• +۷ مورد دیگر

Professor Jun Hong holds the chair in Artificial Intelligence at the University of the West of England within the School of Computing and Creative Technologies. He is a key member of the Computer Science Research Centre (CCRC) and has led research initiatives in data science, graph mining, and intelligent autonomous systems. Research focus areas: Data extraction, integration, and linkage; graph mining; social network analysis; AI planning; BDI-based multi-agent systems; privacy preservation Major grants: Administrative Data Research Centre (ESRC, £4.9M), PACES (EPSRC, £623K), DEVELOP (EU Horizon 2020, £113K), and multiple Innovate UK projects His work spans from foundational research in probabilistic BDI agents and multi-agent planning to applied projects in social media analysis, insurance fraud detection, and healthcare communication systems. Recent publications emphasize graph neural networks, social influence prediction, and uncertainty management in autonomous systems. Supervised 12+ PhD/MSc students Acted as PC/OC Chair for IDEAS 2017 , BNCOD 2006 , and AICS 2006 Peer reviewer for top UK councils and international journals

• Artificial Intelligence
• Data Science
• Intelligent Autonomous Systems
• +۷ مورد دیگر

Panos Louvieris serves as Professor of Information Systems at Brunel University London within the Department of Computer Science, College of Engineering, Design and Physical Sciences. He leads the Defence & Cyber Security (DCS) research group and holds co-directorship roles at the Brunel Intelligent Digital Economy and Society (IDEAS) Research Centre and the Trusted Open Models Institute (TOMI) at the Hartree Centre. His academic profile includes committee membership in the EPSRC Digital Personhood Network, EPSRC ITaaU+ Network, and NEMODE+ Network, reflecting his influence in UK research infrastructure. His research spans cybersecurity, data fusion, and computational finance with emphasis on cyber situational awareness, explainable AI, and distributed ledger technologies. Recent work integrates agent-based simulation for pandemic modeling, Markov models for threat prediction, and social media analysis for security awareness. This interdisciplinary approach bridges computer science, operations research, and social sciences to address defense, finance, and public health challenges through advanced computational models. Analysis of his publication trajectory reveals evolving focus from foundational information fusion (2005-2010) to cyber-physical systems (2010-2015), with current emphasis on digital personhood markets (2020-present). His most recent publications demonstrate methodological innovation in blockchain applications for tax compliance and geospatial pandemic modeling, while maintaining core expertise in cyber defense analytics. Louvieris actively supervises research and collaborates with institutions including the Hartree Centre and EPSRC networks. His grant portfolio features the EPSRC-funded Digital Personhood project (2013-2016), Innovate UK's Contact Tracing project (2020-2021), and current leadership of the EPSRC Making Tax Smart initiative (2022-2023) exploring distributed ledger technology for central bank digital currency compliance. He directs the Defence & Cyber Security research group and co-leads the IDEAS Research Centre, fostering cross-disciplinary work on digital economy challenges. Current projects involve HSBC sandbox testing of Open Banking prototypes and development of futures markets for digital personhood data, positioning his team at the intersection of cybersecurity, FinTech, and societal digital transformation.

• Cybersecurity
• Data and Information Fusion
• Defence Analytics
• +۷ مورد دیگر

Jiřina Vejnarová is an Associate Professor and the Director of the Institute of Information Theory and Automation at the Czech Academy of Sciences. She also teaches at the Faculty of Informatics of the University of Economics and the Faculty of Nuclear Sciences and Physical Engineering of the Czech Technical University. Her educational background includes graduation from the Faculty of Mathematics and Physics at Charles University in Prague in 1985, with a focus on probability and mathematical statistics. She received her RNDr. title in 1986 and her CSc. (PhD equivalent) in theoretical cybernetics in 1991. Professor Vejnarová's research focuses on structured multidimensional models within interval probabilities, particularly in possibility theory and evidence theory. Her work spans artificial intelligence, uncertainty modeling, belief functions, and probabilistic reasoning. She has developed compositional models for credal sets and has extensively researched the marginal problem in evidence theory. Her recent publications demonstrate a consistent focus on evidence theory, possibility theory, and related areas of artificial intelligence. She has made significant contributions to the understanding of credal networks, composition operators, and the interpretation of various probability extensions. Professor Vejnarová has been actively involved in the academic community, having organized the 5th International Symposium on Imprecise Probabilities and served on the editorial board of the journal Kybernetika. She was a member of the board of the Czech Society for Cybernetics and Informatics from 2003-2010. As Director of the Institute since 2017 (previously serving as Deputy Director for Research from 2009), she leads research initiatives in information theory and automation. Her teaching responsibilities include courses such as Mathematical Informatics, Information Theory and Inference, Logic and Semantics, and Probabilistic Models of Artificial Intelligence.

• Multidimensional structured models
• Conditional independence
• Artificial intelligence
• +۳ مورد دیگر

Frédéric Pichon is a Full Professor at Artois University's Laboratory of Computer Engineering and Automation of Artois (LGI2A), part of the Faculty of Applied Sciences in Béthune, France. He holds a Ph.D. in Information and Communication Systems (2009) and an H.D.R. (2018) from Artois University. His research focuses on uncertain reasoning, information fusion (particularly belief functions theory), and machine learning applications in decision-making under uncertainty. Education: B.Eng. (Hons) in Computer Networks and Distributed Systems, Edinburgh Napier University, 2003 M.Sc. in Information Technology, Aalborg University Esbjerg, 2005 Ph.D., University of Technology of Compiègne, 2009 H.D.R., Artois University, 2018 Research Interests: Belief functions theory and applications Optimization under severe uncertainty Machine learning with uncertain data Information fusion for decision support Articles Trends: Recent work emphasizes belief function-based solutions for optimization problems (e.g., shortest path, vehicle routing) and deep learning integration with uncertainty quantification. Early-career contributions include foundational work on Dempster-Shafer theory and conflict analysis. Awards: Notable recognitions include the IJAR Early Career Researcher Award (2022), multiple best paper awards at LFA and BELIEF conferences, and distinctions for student research supervision. Advising & Grants: Supervised 9 PhD students (3 in progress). Active in organizing international conferences (BELIEF, LFA) and editorial roles (Area Editor for International Journal of Approximate Reasoning). Research spans theoretical advancements and applied projects in logistics, image processing, and simulation. Labs/Teams: Core member of LGI2A, collaborating with institutions like Heudiasyc and IBISC labs on uncertainty modeling and fusion systems.

• Uncertain Reasoning
• Information Fusion
• Machine Learning
• +۳ مورد دیگر

Vicenç Torra is a Professor at the Department of Computing Science, Umeå University, Sweden. He leads the NAUSICA: PrivAcy-AWare traNSparent deCIsions research group, focusing on artificial intelligence, data privacy, and approximate reasoning. His work bridges computer science and applied mathematics. Research Interests: Data Privacy (differential privacy, k-anonymity, secure multiparty computation) Approximate Reasoning (fuzzy sets, non-additive measures, Choquet integrals) Decision Making and Aggregation Operators Information Fusion and Clustering Recent Article Trends: His publications emphasize data privacy in machine learning, non-additive measure theory applications, and integrating privacy guarantees with mathematical rigor. 2024 works focus on Frechet manifold privacy and LLM knowledge distillation. Scientific Awards: EurAI Fellow (2010) ISI Elected Member (2013) IEEE Fellow (2017) Additional Contributions: Founder of Transactions on Data Privacy and MDAI conference series. Active in editorial boards of Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems, and Information Sciences. Collaborates extensively with institutions in Japan and Spain.

• Data Privacy
• Approximate Reasoning
• Decision Making
• +۷ مورد دیگر

Yaxin Bi is a Professor in the School of Computing at Ulster University and a core member of the Artificial Intelligence Research Centre, with over thirty years of research experience in AI applications. His work bridges theoretical advances in machine learning with real-world problem-solving across diverse domains. His educational background includes a BSc (Hons) in Computing Science, an MSc by research in Computing Application, and a PhD in Text Categorization with ensemble learning approaches. These qualifications established the foundation for his pioneering work in evidence theory and classifier systems. Professor Bi's research centers on machine learning and ensemble learning integrated with Dempster-Shafer theory of evidence, focusing on developing robust data analytics methods for decision-making under uncertainty. Key application areas include satellite data anomaly detection, manufacturing process optimization, energy systems analysis, agricultural monitoring through crop classification and disease detection, digital twin development, sentiment analysis, and multi-sensor activity recognition. His methodology emphasizes practical implementation of theoretical frameworks to address complex real-world challenges. Analysis of recent publications (2024-2025) reveals a strategic focus on high-impact applications of AI in critical infrastructure and environmental monitoring. Key trends include the convergence of deep learning (LSTM, GAN) with evidence theory for seismic prediction, optimization of industrial manufacturing processes, and advancement of information retrieval systems. These works consistently address uncertainty quantification and demonstrate cross-domain applicability from semiconductor fabrication to disaster response. His scientific recognition includes: Senior Fellow of the Higher Education Academy, UK Professor Bi has secured over £5 million in competitive research funding from major international bodies including the European Space Agency, EU Frameworks and Horizon 2020 programs, Knowledge Transfer Partnership, Invest NI, and the Royal Society. He actively shapes the academic community through editorial roles as Associate Editor for the International Journal of Intelligent Systems and editorial board membership for Real-World Data Science (Royal Statistical Society), alongside steering committee positions for KSEM and SAI conferences. His consultancy projects demonstrate direct industry translation of research. As a core member of Ulster University's Artificial Intelligence Research Centre, he leads initiatives in satellite data analytics, digital twin development, and AI-driven solutions for sustainable development goals, directing current projects including Electromagnetism Anomaly Detection and Seismic Deformation Monitoring through generative AI approaches.

• Machine Learning
• Ensemble Learning
• Dempster-Shafer Theory of Evidence
• +۹ مورد دیگر

Benjamin Quost is a Professor at the University of Technology of Compiègne affiliated with the Computer Engineering Department at the Royallieu Research Center. His research focuses on theoretical frameworks for handling uncertainty and imprecision in AI, including belief functions and imprecise probability. Machine learning with imperfect data Safe AI and fairness constraints Model explainability and partial decision analysis Classifier combination and information fusion Probability theory and Dempster-Shafer theory He teaches modules ranging from foundational statistics for engineers (SY02) to advanced data mining and machine learning (SY09/SY19), covering topics like hypothesis testing, linear regression, principal component analysis, and support vector machines.

• Machine Learning
• Safe AI (safe models with imperfect data)
• Fairness Constraints in AI
• +۸ مورد دیگر

BEN AYED Safa is a Researcher-Lecturer at CESI, affiliated with the LINEACT research group and the Department of Engineering and Numerical Tools. Their work focuses on knowledge management, uncertainty modeling under belief function theory, decision support systems, and predictive maintenance. They hold dual PhDs in Computer and Automatic Engineering (Artois University, France, 2020) and Computer Science applied to Management (University of Tunis, Tunisia, 2020), along with advanced degrees in Business Intelligence and Data Mining. Currently collaborating on the ANR SCOPES project (2022-2025), they teach modules in computer science, networks, and electrical engineering at undergraduate levels. Their research emphasizes CBR systems maintenance, evidential fusion methods, and AI-driven predictive analytics. Recent work includes advancements in collaborative perception modeling and uncertainty quantification in industrial systems. Education : PhD in Computer and Automatic Engineering (LGI2A, Artois University, 2020) PhD in Computer Science applied to Management (LARODEC, University of Tunis, 2020) Master of Research in Business Intelligence (Institut Supérieur de Gestion, Tunisia, 2017) Master in Data Mining & Knowledge Management (Polytech'Nantes, France, 2017) Bachelor in Computer Science applied to Management (Institut Supérieur de Gestion, Tunisia, 2015) Research Interests : Belief Function Theory applications in CBR systems Evidential clustering and policy design Predictive maintenance frameworks Machine learning for uncertainty quantification Information fusion in autonomous systems Key Contributions : Recent work bridges belief function theory with industrial AI challenges, including energy forecasting, turbofan prognosis, and ITER divertor modeling. Their methods enhance case-base maintenance through evidential integration, addressing dynamic uncertainty in real-world systems. Grants & Supervision : Participating in the ANR SCOPES project (2022-2025) and planning thesis supervision by 2025. Their research spans academic-industrial collaboration in PHM frameworks and smart building analytics.

• Knowledge Management
• Uncertainty Management
• Belief Function Theory
• +۴ مورد دیگر

Thierry Denoeux is a Full Professor (Exceptional Class) at the Department of Information Processing Engineering, Université de Technologie de Compiègne, France. He holds senior membership in the Institut Universitaire de France (since 2019, reconducted 2024) and serves as Editor-in-Chief of the International Journal of Approximate Reasoning . His research focuses on reasoning and decision-making under uncertainty, with major contributions to the theory of belief functions and its applications in machine learning, statistical inference, and information fusion. Denoeux has authored over 350 publications and supervised 30+ PhD theses. Education : Graduated from École nationale des ponts et chaussées (PhD from same institution). Roles : President of the Belief Functions and Applications Society, Associate Editor of Fuzzy Sets and Systems , and other journals. His research interests emphasize managing uncertainty in intelligent systems, with applications in medical imaging, data mining, and econometrics. Recent work includes evidential machine learning frameworks, uncertainty quantification in neural networks, and fusion of multimodal medical data. Publications span topics like evidential clustering, fusion of uncertain data, and applications in hydrology and materials science. Over 150 journal articles and conference papers since 2020 alone reflect his prolific output in belief functions theory and its practical implementations. Awards : Senior IUF Membership (2019–present). Grants/Advising : Extensive supervisory experience in PhD and postdoctoral research, with notable contributions to interdisciplinary projects involving healthcare and environmental applications. Denoeux collaborates internationally, leading initiatives in evidential AI and uncertainty-aware decision systems through his editorial and society leadership roles.

• Reasoning under Uncertainty
• Decision-Making under Uncertainty
• Belief Functions Theory
• +۵ مورد دیگر