Find Academic Supervisors

• Complex Networks
• Machine Learning
• Graph Mining
• +4 more

Cécile Bothorel is a full Professor in Computer Science at IMT Atlantique, Brest. She holds a doctorate in computer science and distributed artificial intelligence, with an HDR (2023). Her expertise spans complex networks, machine learning, and graph mining, focusing on community detection in large datasets. She leads the Data Science specialization in IMT Atlantique's engineering program and contributes to the UMR CNRS 6285 Lab-STICC. Education: Engineering degree (1995), PhD (Computer Science), HDR (2023) Research Interests: Complex networks, machine learning, community detection, applications in management, economics, and ecology. Affiliations: IMT Atlantique (since 2009), Lab-STICC, ANR expert, and program committee member at KDD, ECML PKDD, etc. Her research includes projects like Data2Laws (2025-2030), analyzing large online collectives' scaling dynamics. She has advised numerous PhD and postdoc students, focusing on topics like temporal networks and anomaly detection.

• Community Pediatrics
• Social Determinants of Health
• Health Equity
• +4 more

Kevin P. Fiori, M.D., M.P.H., M.S., is a faculty pediatrician at Montefiore Medical Center and Albert Einstein College of Medicine in the Bronx, New York. His scholarly work focuses on integrating social-determinants screening, community-health-worker interventions, and data-driven precision social medicine into pediatric primary-care workflows to advance health equity. Research Interests: Community pediatrics & social determinants of health Implementation science for health-equity interventions AI-enabled precision social medicine Housing & health residency curricula Patient-referral system optimization Across 58 peer-reviewed outputs since 2016, Fiori’s publications exhibit a clear trajectory from descriptive social-risk epidemiology to pragmatic implementation trials and AI-augmented screening tools. His 2024-2025 papers collectively push toward real-time analytics dashboards, cross-hospital referral platforms, and predictive models that embed social-risk signals into everyday clinical decision making. Scientific Awards: No specific honors or prizes are listed in the provided source material. Advising & Grants Narrative: While the text confirms an h-index of 12 and 494 citations, it does not enumerate graduate students, mentees, or awarded grant titles; hence these categories remain undocumented in the current extract. Labs & Teams: Fiori leads or collaborates on multiple Bronx-based implementation projects involving community health workers, medical-legal partnerships, and EHR-integrated social-risk screening initiatives, indicating an active inter-disciplinary team infrastructure within the Montefiore health system.

• Operations Research
• Logistics Optimization
• Supply Chain Management
• +3 more

Yuanyuan Li is a Researcher at LINKS Foundation and currently pursuing a PhD in Operations Research and Management at ESSEC Business School (France) with a visiting PhD at Nanyang Business School (Singapore). She holds a Master’s in Software Engineering and a Bachelor’s in Computer Engineering from the Polytechnic University of Turin (Italy). Her research focuses on optimization techniques for sequential decision-making, large-scale optimization problems, and their applications in last-mile delivery and startup operations. She has presented work at prestigious conferences including INFORMS Annual Meeting, POMS, and EURO, and contributed to EU-funded projects like LEXIS and SYNCHRO-NET. Technical skills include programming languages (Java, Python, OPL, C, SQL, HTML), software tools (CPLEX, Gurobi, Eclipse, MySQL), and operating systems (Windows, macOS, Linux). Language proficiency includes Chinese (native), English (fluent), and Italian/French (basic).

Elisabeth GASSIATView profile

Professor

• Statistics
• Probability
• Hidden Markov Models
• +7 more

Elisabeth GASSIAT is a Professor at the University of Paris-Saclay, affiliated with the Orsay Mathematics Institute and the Probability and Statistics Team. She is a Senior member of the Institut Universitaire de France (IUF) for the period 2020-2025, recognizing her significant contributions to academia. Her primary research interests include: Hidden Markov Models and their theoretical properties Mixture models and nonparametric statistics Deconvolution and inverse problems with unknown noise Machine learning theory with focus on privacy and security Order identification and model selection Professor GASSIAT's recent work shows a strong evolution from classical statistical theory toward modern machine learning challenges, particularly in understanding theoretical limits of learning in complex models and addressing privacy concerns in machine learning systems. Her research spans both theoretical foundations and practical applications across various domains, with a remarkable consistency in methodological rigor throughout her career. She has received notable recognition as a Senior member of the Institut Universitaire de France (2020-2025). Professor GASSIAT is actively involved in teaching, offering courses such as M1 "Statistique" and Mathématiques 2 (Polytech). She has also co-organized the MHC2021 International conference on Mixtures, Hidden Markov models and Clustering with H. Holzmann and P. Vandekerkhove, demonstrating her leadership in the international statistics community.

• Astrophysical data
• Data analysis
• Deep learning
• +1 more

Nicolas AUNAI is a CNRS Research Fellow affiliated with the Laboratoire de Physique des Plasmas (UMR7648). His work focuses on astrophysical data analysis, machine learning applications, and plasma physics simulations. Contact: Email: nicolas.aunai@lpp.polytechnique.fr Research interests include: Interplanetary coronal mass ejections Cold ion dynamics in space plasmas Magnetopause structure analysis Perpendicular current sheet behavior Development of the Smilei open-source simulation tool

Bastien CAZAUXView profile

Lecturer

• Bioinformatics
• Algorithms
• Genome Assembly
• +3 more

Bastien CAZAUX is a Lecturer affiliated with ESPRIT, located at Office S3.49 in Scientific City. His research focuses on bioinformatics algorithms, computational genomics, and data compression techniques. Role: Lecturer at ESPRIT Location: Office S3.49, Scientific City Team: Bonsai His research interests span bioinformatics, algorithms, genome assembly, and graph-theoretic approaches to genomic data processing. Key contributions include work on de Bruijn graphs, k-mer indexing, and efficient compression methods for biological sequences. Recent publications highlight trends in pan-genomic analysis , motif discovery , sequence alignment , and graph-based genome representation . These works often combine algorithmic innovation with practical applications in genomic data processing. He is associated with the Bonsai research team, contributing to foundational work in computational biology and algorithm design.

• Network Science
• Data Science
• Complex Networks
• +3 more

Artur Ziviani is a Senior Researcher at the Data Extreme Lab (DEXL) of the National Laboratory for Scientific Computing (LNCC), a research unit under Brazil's Ministry of Science, Technology, and Innovation (MCTI). He coordinates LNCC's Multidisciplinary Graduate Program in Computational Modeling (M.Sc./Ph.D.). With a Ph.D. from Université Pierre et Marie Curie (Paris 6) and a B.Sc./M.Sc. from Federal University of Rio de Janeiro (UFRJ), he has held visiting positions at INRIA (France) and taught at Paris 6. His research focuses on network science, data science, and interdisciplinary applications, including complex networks, machine learning, and mobility analysis. He is a Senior Member of IEEE and ACM, and an Affiliated Member of the Brazilian Academy of Sciences (2013-2017). His work spans algorithmic information theory, network centrality, and human-aware communication strategies. Recent projects include mobility modeling, protein interaction networks, and biodiversity informatics. Education: Ph.D. (Paris 6, 2003), M.Sc. (UFRJ, 1999), B.Sc. (UFRJ, 1998). Research interests include network characterization, data-driven modeling, and applications in healthcare and biodiversity. His publications analyze network structures, centrality metrics, and spatiotemporal data, with contributions to IEEE, ACM, and Elsevier journals. He also leads initiatives on SDN architectures for high-performance computing and collaborative learning platforms. Awards include recognition as an IEEE Impact Creator and senior fellowships. His team works on projects like SafeCityMap (mobility during pandemics) and MobHet (heterogeneous mobility prediction). He serves on editorial boards for Computer Networks and IEEE Communications Surveys.

Juan A. FraireView profile

Visiting Professor

• Space Networking
• Deep-Space Informatics
• Satellite Communications
• +4 more

Dr. Juan A. Fraire is a researcher at INRIA (France) and serves as a guest professor at Saarland University (Germany) and CONICET-UNC (Argentina). His core research focuses on near-Earth and deep-space networking, with over 100 publications in top journals and conferences. He co-founded and chairs the Space-Terrestrial Internetworking Workshop (STINT) and collaborates with space agencies (NASA, ESA, CONAE) and companies (D3TN, Skyloom). Key projects include STEREO (ANR PRCE coordinator), MISSION (MSCA RISE national coordinator), and AI4LFNM (ESA ITT coordinator). His work spans non-terrestrial networks (NTN), satellite IoT, and interplanetary routing. Research interests emphasize scalable space networking protocols, delay-tolerant networks (DTN), and optical/satellite communication systems. He leads initiatives in autonomous laser link scheduling for Mars exploration, constellation design for global IoT coverage, and hybrid RF/optical terminals for improved pointing accuracy. Over a decade of funding includes European and national grants, focusing on network emulation, edge computing orchestration, and resilient space communication. Dr. Fraire supervises multiple PhD candidates, co-supervising with experts like Prof. H. Hermanns and Prof. H. Rivano. His advising emphasizes interdisciplinary approaches to space informatics challenges. Recent work explores AI-driven congestion control, LoRaWAN scalability for direct-to-satellite IoT, and integrated space-terrestrial cloud latency optimization.

Yezekael HAYELView profile

Researcher

• Complex Systems Optimization
• Network Science
• Applied Mathematics
• +6 more

Yezekael HAYEL is a Researcher at the Laboratoire d'Informatique Avignon (LIA) and Director of the Agrosciences & Sciences Doctoral School at Avignon Université. His research focuses on optimizing complex systems with network structures, bridging computer science, applied mathematics, and social sciences. Key areas include opinion dynamics in social networks, cybersecurity, and infrastructure optimization for electric vehicles. He co-authored several influential articles on crowdfunding dynamics, epidemic control, and smart charging algorithms. As Doctoral School Director, he oversees interdisciplinary programs spanning biology, STAPS, computer science, and agricultural sciences. His leadership in the IMPLANTEUS university research school highlights his commitment to multidisciplinary collaboration. Recent work includes a CNRS-published article on opinion evolution models and a collaboration with the Nancy Automation Research Center. Education: Multidisciplinary background in mathematics and computer science Key Projects: ANR-funded IMPLANTEUS research school Optimization of EV charging ecosystems Cybersecurity strategies against network epidemics His pedagogical contributions include teaching at New York University and advocating for international academic exchange. He emphasizes perseverance and creativity in research, advising students to embrace curiosity amid challenges. Awards: None explicitly mentioned, but his leadership roles reflect institutional recognition. Labs/Teams: Active in Avignon Université's Metaboscope Platform and DARI department, collaborating with industry partners like the Nancy Automation Research Center.

Andrea CozzaView profile

Professor

• Electromagnetic Engineering
• Signal Processing
• Reverberation Chamber Applications
• +3 more

Andrea Cozza is a Professor at the University of Paris, leading research in the Electrical and Electronic Engineering of Paris department. Her work focuses on electromagnetics, signal processing, and reverberation chamber applications. She specializes in time reversal techniques for fault detection, antenna measurement, and field synthesis. Key research areas include improving chamber efficiency through advanced stirring methods and analyzing energy distribution in complex media. Dr. Cozza has authored over 50 publications, including seminal works on time-reversal imaging , soft-fault localization in cables , and statistical modeling of reverberation chambers . Her patents cover novel measurement systems and methods for generating uniform electromagnetic fields. Notable contributions include the development of the TREC (Time-Reversal in Reverberation Chambers) framework for optimizing radiated stress testing. Her research emphasizes practical applications in wireless communications, antenna testing, and material characterization. Collaborations with industry partners highlight her expertise in bridging theoretical electromagnetic principles with real-world engineering challenges.

• Energy-Efficient Buildings
• Building Simulation
• Occupancy Modeling
• +4 more

Abhinandana Boodi is a Researcher-Lecturer at CESI LINEACT, affiliated with the UBO/UMR CNRS 6027 IRDL. They hold a PhD in Energy-Efficient Buildings from the Université de Bretagne Occidentale (2021) and a Master's in Smart Grids and Smart Buildings from Grenoble INP – ENSE3 (2017). Their academic roles include teaching Thermodynamics, Electrical Circuits, and Applied Mathematics to engineering students and master’s programs. Research focuses on building simulation, energy management, and occupancy modeling, with applications in HVAC control, intelligent buildings, and artificial intelligence. Key projects involve hybrid dynamic modeling for energy autonomy and multi-objective optimization in nanogrid buildings. They supervise five PhD students exploring topics like energy autonomy, occupancy prediction, and indoor air quality in healthcare facilities. Publications (2018–2024) emphasize machine learning for occupancy prediction, thermal network models, and reinforcement learning in HVAC control. They are a member of CESI LINEACT and IRDL since 2017, contributing to collaborative research on smart energy systems.

• Predictive Maintenance
• Web Services
• Machine Learning
• +3 more

DAAJI Marwa is a Researcher-lecturer at CESI, based at the Pau campus. She holds a PhD in Computer Science from the Faculty of Sciences of Tunis (2021) and an Engineering degree from the High Institute of Art and Multimedia of Manouba (2015). Her research focuses on predictive maintenance, web service defect prediction, and machine learning applications in software engineering. Her educational activities include undergraduate teaching in Computer Science, specifically Web Development. Key research contributions include work on BPEL process defect prediction using evolutionary algorithms, multi-criteria web service selection, and formal concept analysis for interface decomposition. PhD Thesis: "Web service interfaces decomposition based on formal concept analysis, design anti-pattern detection and correction using machine learning and genetic algorithms" Engineering Degree Thesis: "Method for generating consensual association rules based on Formal Concept Analysis" Her publications span IEEE conferences and ACM Transactions, with emphasis on combining formal methods with machine learning for software quality improvement. She is affiliated with the Engineering and Numerical Tools research team at CESI.

• Robotics Localization
• Signal Processing
• Machine Learning
• +3 more

DING Yuehua is a Researcher-Lecturer at the CESI Strasbourg Campus, part of the Engineering and Numerical Tools research team. They hold a HDR (Habilitation à Diriger des Recherches) from the University of Nantes (2020) and a PhD in 2011 focused on MIMO communication system performance optimization. Their academic roles include teaching Electronics, Signal Processing, and Big Data to engineer students in their first and third years. Research interests span Robotics Localization , Data Processing/AI , and Signal Processing , with a focus on integrating machine learning into robotics and wireless communication systems. Recent work emphasizes indoor robot localization using WiFi-visual fusion and SAR-based ship detection. Key technological skills include Python, C, and MATLAB. Publications reflect a strong emphasis on multi-scale object detection, hybrid modeling in marine science, and advanced signal processing techniques for MIMO systems. Review activities include IEEE TSP and TVT. No notable scientific awards are listed, but ongoing contributions include cross-disciplinary projects in medical robotics and materials science.

• Parallel and Distributed Computing
• High Performance Computing
• Edge-Cloud Computing
• +3 more

Overview Dr. Jean-François DOLLINGER is a Researcher-Lecturer at CESI LINEACT (Strasbourg campus), affiliated with the Engineering and Numerical Tools research team. His academic roles include teaching Computer Science courses (undergraduate/graduate) and supervising student projects in algorithmics, programming, databases, and networks. Education: PhD in Computer Science (2011-2015), University of Strasbourg - ICube Lab MSc in Computer Science (2009-2011), University of Strasbourg (Highest Honors) BSc in Computer Science (2008-2009), University of Strasbourg (Honors) Research Interests: Focuses on edge-cloud computing, high-performance distributed systems, combinatorial optimization in IoT networks, and smart city infrastructure. Specializes in optimizing federated learning, WSN deployment strategies, and hybrid CPU/GPU execution frameworks. Advising & Collaboration: Supervises PhD/Master’s students (e.g., A. BAAHMED on federated learning, K. BOUHOUCH on OpenStack edge deployment) Collaborated with Indonesian universities (Mercu Buana) on RPL protocol extensions Research Team: Leads the Engineering and Numerical Tools group, developing frameworks for edge-cloud infrastructures and BIM-based WSN deployments in smart buildings.

• Data Mining
• Big data
• Fuzzy Clustering
• +10 more

HINDAWI Mohammed is a Researcher-Lecturer at CESI (France) specializing in Computer Science with a focus on Data Mining, Big Data, and Machine Learning. He holds a PhD in Computer Science from INSA Lyon (2013) and teaches at the engineering level. His current research emphasizes Dimensionality Reduction in semi-supervised learning and frugal/embedded AI applications. Education: PhD in Computer Science (2013), INSA Lyon MSc in Knowledge and Decision (2008), INSA Lyon BSc in Software Engineering (2005), Aleppo University Research interests include semi-supervised learning, feature selection, ensemble methods, and clustering techniques. He leads the Engineering and Numerical Tools research team and contributes to the Advanced Automation: AI and Data Science group. His work addresses practical applications like patient monitoring via machine learning and battery state estimation. Publications span over 15 peer-reviewed articles in journals like Knowledge and Information Systems (KAIS) and conferences such as IEEE MMSP and ACM CIKM. He supervises PhD students like Trésor Yao Koffi and collaborates on projects involving embedded systems and healthcare tech.