جست‌وجوی استاد و پژوهشگر

Alessio Iovineمشاهده پروفایل

پژوهشگر

• Control Systems
• Power Systems
• Cyber-Physical Systems
• +۴ مورد دیگر

Alessio Iovine is a CNRS researcher at the Laboratory of Signals and Systems (L2S) affiliated with CentraleSupélec (Paris-Saclay University). His research focuses on control systems for cyber-physical applications, including energy systems (e.g., microgrids, renewable integration) and transportation (e.g., autonomous vehicles, traffic control). He holds a European Doctorate in Information Science and Engineering from the University of L’Aquila and CentraleSupélec, with postdoctoral experience at institutions like UC Berkeley and Efficacity. Research Areas: Cyber-Physical Systems DC/AC Microgrid Control Autonomous Vehicles & Adaptive Cruise Control Predictive Grid Management Multi-Agent Systems Publications Trends: Recent works emphasize mesoscopic control approaches for vehicular systems, distributed control for multi-agent networks, and predictive algorithms for power grid congestion management. Over 20 peer-reviewed articles since 2017 reflect his expertise in nonlinear control, hybrid systems, and renewable integration. Opportunities: Actively recruiting PhD/Master students for projects in cyber-physical power systems, machine learning for grid identification, and autonomous traffic control. Hosts seminars on energy systems and signal processing at Paris-Saclay. Labs/Teams: Member of L2S’s Modelling and Estimation team, collaborating with groups like ILOCOS (Information, Learning, Optimization & Communication Sciences).

Pierre Humbertمشاهده پروفایل

پژوهشگر

• Conformal Prediction
• Statistical Learning
• Non-Parametric Statistics
• +۶ مورد دیگر

Pierre Humbert is a postdoctoral researcher at Sorbonne Université's Laboratoire de Probabilités, Statistique et Modélisation (LPSM), affiliated with the MARS project. He previously held postdoctoral positions at Laboratoire de Mathématiques d'Orsay (LMO) and the INRIA Celeste team. Humbert completed his PhD in 2021 at ENS Paris-Saclay, focusing on multivariate analysis with tensors and graphs in neuroscience under Professors Nicolas Vayatis, Laurent Oudre, and Julien Audiffren. His research interests span conformal prediction, statistical learning, non-parametric methods, robust statistics, signal processing, and applications in neuroscience. He contributes to federated learning frameworks, graph signal processing, and tensor-based methodologies for analyzing complex data structures. Humbert's recent work emphasizes federated conformal prediction for privacy-preserving distributed learning and robust statistical techniques. His publications frequently address graph Laplacian estimation, EEG signal processing, and tensor decomposition applications in biomedical contexts. He collaborates actively with teams like INRIA Celeste and maintains open-source implementations of his algorithms.

Guillaume SANDOUمشاهده پروفایل

استاد

• Model Predictive Control
• Optimization
• Autonomous Systems
• +۹ مورد دیگر

Guillaume SANDOU is a Professor at CentraleSupélec, University Paris-Saclay, leading the Control Department and affiliated with the Signals and Systems Laboratory (L2S). He holds a Ph.D. (2005) and HDR (2012) from University Paris-Sud XI. His research focuses on optimization-based control methodologies, including model predictive control (MPC), metaheuristics, and energy systems. He has supervised numerous PhD students, contributing to advancements in autonomous systems, wind farm control, and smart grid technologies. Key research areas include robust control, multi-agent systems, and sensorless control for electric drives. He actively contributes to education, redesigning curricula to integrate modern control trends and industry needs, impacting over 800 students annually. His work spans applications in energy optimization, autonomous driving, and industrial automation. Publications emphasize innovative control strategies, with recent work on game-theoretic autonomous driving frameworks and energy management systems. He has secured patents in wind farm control and electric machine systems. Current and past students reflect his interdisciplinary approach, addressing both theoretical and applied challenges in control engineering.

Stephan Clémençonمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Stochastic Processes
• +۵ مورد دیگر

Stephan Clémençon is a Full Professor at Telecom Paris, part of the Institut Polytechnique de Paris. He holds roles as head of the S2A research team and has led the master program Big Data and industrial chairs such as ML4BGD and DSAI4DIS. His expertise spans Machine Learning, AI, Stochastic Processes, and Nonparametric Statistics, with applications in quantitative finance, biosciences, and signal/image processing. He has advised numerous PhD/Master’s students and collaborated on projects with industry partners like Renault, BNP EXANE, and IDEMIA. Research contributions include anomaly detection, ranking algorithms, and survival analysis. He has published extensively in journals like Electronic Journal of Statistics , TEST , and Bernoulli , with recent work on bipartite ranking, extreme value statistics, and fairness in facial recognition systems. His grants and collaborations highlight interdisciplinary research in AI and industry-driven innovation. He leads the S2A team focusing on statistical learning and signal applications.

Stéphane Mallatمشاهده پروفایل

استاد

• Signal Processing
• Wavelet Theory
• Machine Learning
• +۴ مورد دیگر

Stéphane Mallat is a Professor at the Collège de France, holding the Chair of Data Sciences. He is also affiliated with the École normale supérieure in Paris, where his address is located. His research focuses on signal processing, wavelet theory, and their applications in machine learning and data sciences. Mallat pioneered wavelet transforms for signal decomposition and has contributed to scattering transforms for invariant feature extraction in deep learning. His work bridges applied mathematics, harmonic analysis, and computational methods, with impactful applications in image processing, audio analysis, and quantum chemistry. Publicly listed articles span from foundational wavelet theory (1989) to recent advances in scattering networks (2020). His research trends emphasize geometric signal representations, sparse approximations, and interdisciplinary applications across physics and engineering. His CV and publications are available through institutional links, though specific awards or grants are not explicitly detailed in the provided texts. He collaborates extensively with researchers in mathematics and computer science, advancing theoretical frameworks with practical implementations.

Zoltán Szabóمشاهده پروفایل

استاد

• Statistical Machine Learning
• Kernel Methods
• Information Theoretical Estimators
• +۳ مورد دیگر

Zoltán Szabó is a Professor of Data Science at the Department of Statistics, London School of Economics (LSE). His research focuses on statistical machine learning, particularly kernel methods, information theoretical estimators, and scalable computation. He has held roles such as Programme Director of the MSc Data Science program and is actively involved in academic service, including Area Chair positions at top conferences like NeurIPS and ICML. His work bridges theory and applications, with contributions to fields like safety-critical learning, economics, climate data analysis, and natural language processing. Education and Affiliations: While specific educational details are not explicitly listed, his career trajectory indicates advanced training in statistics and machine learning. He is affiliated with the LSE's Department of Statistics and the Turing Institute, contributing to academic leadership and interdisciplinary projects. Research Interests: His work emphasizes kernel-based methods, including kernel Stein discrepancies, Hilbert-Schmidt independence criteria, and shape-constrained prediction. Applications span finance, economics, robotics, and environmental data analysis. Recent projects include developing outlier-robust estimators and scalable algorithms for high-dimensional data. Publications: His recent work includes advancements in kernel methods for dependency testing, shape-constrained regression, and robust estimation. Key themes include improving computational efficiency, theoretical guarantees for kernel approximations, and practical applications in interdisciplinary domains. Awards and Grants: While no explicit awards are listed, his contributions to NeurIPS (e.g., a Best Paper Award in 2017) and his role in securing grants (e.g., Europlace Institute of Finance) highlight his impactful research. He also serves on editorial boards, including JMLR and ACM Transactions on Probabilistic Machine Learning. Students and Labs: Supervises PhD students in areas like functional data analysis and scalable computation. Collaborates with researchers on projects such as distribution regression and safety-critical learning, contributing to both theoretical and applied outcomes.

Loucas Pillaud-Vivienمشاهده پروفایل

پژوهشگر

• Machine Learning
• Stochastic Processes
• High-Dimensional Statistics
• +۳ مورد دیگر

Loucas Pillaud-Vivien is a researcher at the Applied Probability team within CERMICS (École des Ponts ParisTech), specializing in the high-dimensional stochastic dynamics underlying machine learning optimization algorithms. His work bridges statistical theory and computational methods, focusing on understanding algorithmic behavior in modern ML systems. Education : PhD from ENS/Inria Paris (2020) under Francis Bach; postdoctoral research at EPFL, New York University (Courant Institute), and the Simons Foundation (Flatiron Institute). Research interests include stochastic processes, optimization theory, and high-dimensional statistical learning. Key themes involve analyzing gradient-based methods, implicit regularization effects, and the interplay between computational efficiency and statistical accuracy. Recent publications emphasize topics such as gradient flow dynamics, variational inference, and spectral bias in neural networks. His work often addresses fundamental questions about algorithmic convergence and generalization in overparameterized models. Loucas is affiliated with the CERMICS laboratory, contributing to interdisciplinary projects at the intersection of applied mathematics and machine learning.

Michel KIEFFERمشاهده پروفایل

استاد

• Signal Processing
• Communication Systems
• Networking
• +۵ مورد دیگر

Michel KIEFFER is a Professor in Signal Processing for Communications at Paris-Sud University and a researcher at the Laboratoire des Signaux et Systèmes (L2S), Gif-sur-Yvette, France. He holds a Ph.D. (1999) and Habilitation (2005) from Université Paris-Sud. Previously, he served on part-time leave at Laboratoire de Traitement et Communication de l’Information (CNRS-Télécom ParisTech, 2009–2016) and was a junior member of the Institut Universitaire de France (2011–2016). His research focuses on joint source-channel coding, signal processing for communication networks, and robust control systems. Key contributions include works on multimedia transmission reliability, UAV localization, and fault identification in power grids. He co-authored over 100 publications and books like Applied Interval Analysis (Springer, 2001) and Joint Source-Channel Decoding (Academic Press, 2009). He serves as Associate Editor for Signal Processing (since 2008) and IEEE Transactions on Communications (2012–2016). His work spans interdisciplinary areas such as energy systems, robotics, and telecommunications, addressing challenges in real-time data transmission, network slicing, and distributed control.

Lynda ZITOUNEمشاهده پروفایل

دانشیار

• Systems and Control
• Telecommunications and Networks
• Signal Processing
• +۵ مورد دیگر

Lynda ZITOUNE is an Associate Professor at the Laboratory of Signals and Systems (L2S) within CentraleSupélec, located in Gif-sur-Yvette, France. Her research focuses on systems and control, telecommunications, and signal processing, with applications in energy-efficient networks, Industry 4.0, and health-related technologies. She is affiliated with the COMEDY, MODESTY, and Modelling and Estimation teams, contributing to projects like SYCOMORE. Her work emphasizes innovative solutions for network optimization, including load balancing in WiFi networks, congestion control in IoT systems, and energy efficiency in cellular and mesh architectures. She employs advanced techniques such as deep reinforcement learning, fluid modeling, and hybrid algorithms (e.g., Q-learning with ant systems) to address complex networking challenges. ZITOUNE’s recent publications (2021–2025) highlight contributions to intelligent association protocols, distributed control in large-scale systems, and AI-driven approaches to network management. Her research bridges theoretical modeling with practical applications, aiming to enhance network scalability, reliability, and sustainability. She collaborates actively with international teams and participates in key conferences like ICC, MASCOTS, and GLOBECOM. Her research interests span telecommunications, signal processing, and control systems, with a focus on interdisciplinary applications in energy and healthcare. She leads initiatives in fluid-based modeling for cellular networks and develops protocols for delay-tolerant wireless sensor networks using aerial mobile sinks.

Fadhila TLILIمشاهده پروفایل

پژوهشگر

• UAV Security
• Artificial Intelligence
• Cybersecurity
• +۳ مورد دیگر

Fadhila TLILI is a researcher affiliated with the University of Technology of Troyes (UTT), contributing to the field of UAV security through advanced research integrating artificial intelligence and cybersecurity. Her work focuses on developing robust frameworks for detecting faults, anomalies, and cyberattacks targeting unmanned aerial vehicles, emphasizing real-time monitoring and adaptive security solutions. Academic Discipline: Doctorate in relevant engineering/IT field. Affiliations: UTT Research Directory member as of September 2024. Key research areas include UAV intrusion detection systems, AI-driven security protocols, and resilience against cyber-physical threats. Recent publications address challenges in UAVCAN protocol security, energy resource protection, and hybrid deep learning frameworks for anomaly detection.

Roua Youssefمشاهده پروفایل

استادیار

• UWB Signal Processing
• Compressive Sampling
• Full-Duplex Communications
• +۴ مورد دیگر

Roua Youssef is an Assistant Professor at the University of Brest, France, and a member of the SI3 (Security, Intelligence and Integrity of Information) research team within Lab-STICC (CNRS UMR 6285). Her work focuses on advanced signal processing techniques for telecommunications, including UWB systems, compressive sampling, full-duplex communications, and drone detection. Education: She earned a Telecommunications Engineering diploma from the Lebanese University (Beirut, Lebanon) and an M.Sc. in Electronic Engineering from IMT Atlantique (Brest, France) in 2007. She completed her Ph.D. in Telecommunications at IMT Atlantique in 2011. Research interests include radar-based sensing for Industry 4.0 applications, robust heart rate detection in vehicular environments, and interference cancellation in full-duplex systems. Her work combines machine learning with UWB radar and compressive sensing to address challenges in indoor localization, collision avoidance, and non-cooperative signal detection. Recent contributions include experimental validation of IR-UWB radar systems for people detection in industrial settings and digital self-interference cancellation techniques for full-duplex transceivers. She has also explored low-complexity drone control signal detection and optimal Fourier coefficient selection for UWB channel estimation. Labs/Teams: Active member of Lab-STICC’s SI3 team, focusing on secure and intelligent information systems. Her research integrates hardware prototyping (e.g., USRP platforms) with algorithm development for real-world wireless communication problems.

Nicolas VAYATISمشاهده پروفایل

استاد

• Machine Learning
• Healthcare Technology
• Theoretical Computer Science
• +۳ مورد دیگر

Nicolas VAYATIS is a Professor at the Ecole normale supérieure Paris-Saclay, affiliated with the Centre Borelli and the Department of Mathematics (DER de Mathématiques). He holds an ELLIS Fellowship (Paris unit) and specializes in theoretical foundations of machine learning, with applications to healthcare and industrial systems. He teaches in programs such as the Master M2 MVA, the normalien DER mathématiques, and the AI track at ENS Paris-Saclay. His research focuses on statistical learning, domain adaptation, change-point detection, and graph signal processing. He has advised over 20 PhD/Master’s students, including notable names like Antoine de Mathelin and Charles Truong. His work bridges academic theory and practical applications, collaborating on projects like fall detection using smart floor sensors and gait analysis with inertial measurement units. He contributes to open-source tools like the ruptures library for change-point detection and the ADAPT domain adaptation toolbox. Key collaborations include projects with INSEAD, Université Paris-Saclay, and industry partners like Michelin. His research has been published in venues such as NeurIPS, ICML, and AISTATS, with a focus on robust algorithms and real-world impact.

El Mahdi El Mhamdiمشاهده پروفایل

استادیار

• Robust Distributed Learning
• AI Safety
• Cybersecurity of AI
• +۵ مورد دیگر

El Mahdi El Mhamdi is an Assistant Professor of Mathematics and Data Science at École Polytechnique, where he co-chairs the Master of Science program in Data Science and Machine Learning. His research focuses on the robustness of distributed learning systems and AI safety, with notable contributions to Byzantine-resilient algorithms like Bulyan and Krum. These algorithms are deployed in industrial settings such as WeBank and IBM's Federated Learning Library. He has authored four papers featured in the US NIST 2024 report on Adversarial Machine Learning as state-of-the-art defenses. Education: Grew up in Morocco with a bachelor-level background in physics, mathematics, and philosophy. Pursued studies in France, Germany, and Switzerland through scholarships, including a PhD in Computer Science from EPFL. His thesis on Robust Distributed Learning received the 2020 Best Computer Science PhD distinction and was shortlisted for a cross-departmental award. Research Interests: Robustness of biological networks (e.g., neural, gene-regulatory systems) and distributed machine learning systems under adversarial conditions. Advocates for ethical AI governance and has published a book on AI safety with Lê Nguyên Hoang, translated into multiple languages. His work addresses societal impacts of AI, including misinformation and data poisoning. Awards: Includes France’s top 100 scientific inventors (2025), VentureKick award (2020), and recognition from Global Voices Online, Thomson Reuters, and Google for freedom of speech advocacy. Legal actions ongoing against Moroccan authorities due to threats and harassment. Grants & Service: Co-authored research plans totaling $1.2M in grants. Served as a reviewer/PC member for top conferences (NeurIPS, ICML, ICLR). Supervised multiple master’s theses at EPFL, fostering talent in distributed computing and AI safety. Labs/Teams: Active in collaborative projects like Tournesol (trustworthy human judgments) and Aksel (Byzantine-resilient SGD). Engages in public outreach through podcasts, radio interviews, and science communication initiatives.

Martin-Pierre Schmidtمشاهده پروفایل

مدرس

• Topological Optimization
• Multi-physics Optimization
• Approximation Theory
• +۳ مورد دیگر

Martin-Pierre Schmidt is a Lecturer at the National Institute of Applied Sciences (INSA) Rouen, affiliated with the Department of Mathematical Engineering. His research focuses on topological/multi-physics optimization, approximation theory, and applications in computer-aided geometric design (CAGD). He completed his PhD in March 2020 under the supervision of Christian Gout, with co-supervisors David Bonner and Auxkin Ortuzar from Dassault Systèmes, funded by Dassault Systèmes. His work integrates machine learning and data science into imaging and design processes. He teaches in the GM5 program at INSA Rouen, emphasizing machine learning and data approximation techniques. His research team is part of the Numerical Analysis, Imaging and Approximation group. Notable contributions include interactive topology optimization tools, biomimetic microstructure design, and robust 3D shape detection algorithms. His thesis defense in 2020 involved a jury of international experts in mechanical engineering and computational design. Key collaborations include Dassault Systèmes, with applications in CAD model optimization and manufacturing process design. His work bridges theoretical optimization methods with practical industrial applications in additive manufacturing and structural engineering.

• Federated Learning
• Distributed Artificial Intelligence
• Complex Systems
• +۳ مورد دیگر

Mickaël Bettinelli serves as an Associate Professor in the Computer Science Department of the University Institutes of Technology at Université Savoie Mont Blanc, affiliated with the LISTIC laboratory. Previously, he held a postdoctoral position at the University of Oulu's Center for Ubiquitous Computing (EU Horizon 2020 Fractal project) and completed his PhD at University Grenoble Alpes on collective intelligence for decision support in the IDEX-funded Circular project. His academic credentials include: PhD in Computer Science (Distributed Artificial Intelligence), University Grenoble Alpes, 2021 Master of Computer Science (Artificial Intelligence), 2018 Bettinelli's research centers on federated learning, distributed intelligence, and complex systems, with emphasis on collective behavior emergence and distributed system resilience. Current work applies federated learning to enhance system robustness, extending his doctoral research in multi-agent coalition formation and circular economy applications. His 2019-2021 publications reveal a cohesive trajectory: developing agent-based coalition models (ABSG), cohesion mechanisms for system integrity, and decision frameworks for remanufacturing. These span swarm intelligence, industrial engineering, and sustainable manufacturing, demonstrating interdisciplinary rigor in multi-agent systems and distributed intelligence. Scientific recognition includes: Best Paper Award at Journées Francophones sur les Systèmes Multi-Agents (JFSMA) 2021 Prix Coup de coeur at Concours Conter et Rencontrer les Sciences 2024 As an educator, he leads courses in Machine Learning, Cryptography, and IoT while developing pedagogical tools like DonatelloPyzza—a Python gridworld game for beginner programmers. His science communication extends to a French YouTube channel explaining complex CS concepts. Research is supported by EU Horizon 2020 and IDEX grants through collaborations at LISTIC laboratory and the University of Oulu's Center for Ubiquitous Computing.