جستجوی استاد راهنمای دانشگاهی

Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.

• Robotics
• Human-Robot Interaction
• Machine Learning
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Eric Grivel is a Professor at the University of Bordeaux affiliated with the IMS Bordeaux (Integration Laboratory from Materials to Systems). His research focuses on Signal and Image Processing Spectral Analysis Stochastic Process Modeling His work spans theoretical contributions to signal processing and practical applications in radar systems and biomedical signal analysis. Key trends in his recent publications include Optimization of Detrended Fluctuation Analysis (DFA) for Hurst exponent estimation Development of divergence metrics for comparing ARMA and Gaussian processes Waveform design in MIMO OFDM DFRC (Dual Function Radar-Communication) systems Integration of AI tools like ChatGPT in educational signal processing projects Collaborations and industrial partnerships evident in his publications involve institutions such as Indian Institute of Science Thales Airborne Systems STMicroelectronics CEA Leti Slb (Schlumberger)

• Signal Processing
• Applied Mathematics
• Radar Systems
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Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.

• statistical machine learning
• optimization
• sparse methods
• +۵ مورد دیگر

Christophe VIGNAT is a Professor at CentraleSupélec, affiliated with the Laboratoire des Signaux et Systèmes (L2S). His research focuses on number theory, special functions, probability, and their applications in signal processing and control systems. He has held visiting professorships at École Polytechnique Fédérale de Lausanne (EPFL) and Tulane University. VIGNAT's work bridges pure mathematics and applied fields, with notable contributions to Bernoulli/Euler polynomials, multiple zeta values, and probabilistic methods in number theory. His recent publications explore topics like partition functions, theta functions, and Ramanujan-type identities. He has delivered talks at international conferences and collaborates widely with researchers in mathematics and physics. Research Interests: Number theory, special functions (Bessel, orthogonal polynomials), probability theory, signal processing, control systems, analytic combinatorics, and their interconnections. His work often employs symbolic computation and probabilistic approaches to uncover identities and structures in mathematical analysis. Publications Trends: Recent articles emphasize partition theory, zeta functions, and integrals related to classical polynomials. His collaborations highlight interdisciplinary efforts between pure mathematics and applied sciences. Over 150 refereed papers and conference contributions demonstrate his prolific output across diverse mathematical domains. Education: While specific academic history isn’t detailed, his roles and publications suggest advanced training in mathematics and engineering, typical for a full professor in systems and control.

• Mathematics
• Number Theory
• Probability Theory
• +۴ مورد دیگر

Florence d'Alché-Buc is a Professor at Télécom Paris (Institut Polytechnique de Paris), holding an Isaac Newton Institute Simons Chair (2025) and leading the Data Science and Artificial Intelligence for Digitalized Industry & Services (DSAI) Chair. She heads the Image, Data, and Signal Department and is part of the Signal, Statistics, and Learning (S2A) team at the LTCI laboratory. Her research focuses on machine learning, bioinformatics, and industrial applications, emphasizing kernel methods, structured prediction, and reliable AI. Education: Previously a professor at Université d’Evry and deputy director of the IBISC lab. Co-director of the Paris-Saclay Data Science Master and creator of specialized AI programs (e.g., Certificate of Specialized Studies in AI). Research highlights include contributions to operator-valued kernel methods, graph prediction, and frugal AI. She actively collaborates with institutions like Inria, École Polytechnique, and industry partners (Airbus, Engie, etc.). Notable roles: Scientific director of Digicosme Labex, Ellis Fellow, and board member of IVADO (Montreal). Her recent work addresses AI explainability, robustness, and sustainability, including projects on interpretable networks and energy-efficient models.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
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Jean Ponce is a Professor at Ecole Normale Supérieure - PSL and a Global Distinguished Professor at New York University's Courant Institute and Center for Data Science. He serves as Scientific Director of PRAIRIE Interdisciplinary AI Research Institute and co-founded Enhance Lab, commercializing super-resolution imaging software. His research focuses on computer vision, machine learning, robotics, and image processing. Ponce has held roles at Inria, MIT, Stanford, and the University of Illinois, and is an IEEE and ELLIS Fellow. He has served as chair of major conferences like CVPR, ECCV, and ICCV, and authored the textbook 'Computer Vision: A Modern Approach.' Research interests include statistical models for exoplanet detection, neural networks for 3D reconstruction, and self-supervised learning. His work combines theoretical foundations with practical applications in astrophysics, robotics, and imaging. Notable awards include the IEEE CVPR Longuet-Higgins Prize (2016, 2020) and ICML Test-of-Time Award (2019). Key projects include Enhance Lab's high dynamic range imaging and PRAIRIE's interdisciplinary AI initiatives. Ponce's articles explore cutting-edge topics like neural object priors, geodesic motion planning, and satellite image analysis. His contributions bridge academic research and industrial applications, emphasizing both fundamental theory and real-world impact.

• Computer Vision
• Image Processing
• Machine Learning
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Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.

• Signal Processing
• Fault Diagnosis and Prognosis
• Electrical Engineering Systems
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Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
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Clément FERNANDES is a Lecturer at Telecom SudParis, part of the SAMOVAR laboratory under Institut Polytechnique de Paris. His research focuses on statistical modeling and unsupervised learning techniques, particularly applying hidden Markov chains to data and image segmentation. He holds a PhD in Discrete Mathematics from Institut Polytechnique de Paris (2022), where his thesis explored Triplet Markov Chains in image segmentation. His recent work includes advancing non-stationary data segmentation methods and developing forecasting models using Gaussian Markov frameworks. Though no explicit grants or awards are mentioned, his publications reflect contributions to approximate reasoning and applied statistics in engineering contexts. He is affiliated with the SAMOVAR laboratory and maintains an active publication record in top-tier journals like International Journal of Approximate Reasoning .

• Hidden Markov Models
• Data Segmentation
• Image Segmentation
• +۳ مورد دیگر

Xujia ZHU is an Associate Professor at CentraleSupélec, Paris-Saclay University, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on uncertainty quantification, surrogate modeling, stochastic simulators, and reliability analysis. He holds an engineer’s degree in Mechanics from École Polytechnique (2015), a Master’s in Computational Mechanics from TU Munich (2017), and a Ph.D. from ETH Zurich (2022). He was a postdoctoral researcher at ETH Zurich until 2023. Education: Ph.D., Chair of Risk, Safety, and Uncertainty Quantification, ETH Zurich, 2022 Master’s (high distinction), Computational Mechanics, Technical University of Munich, 2017 Engineer’s Degree, Mechanics, École Polytechnique, 2015 Research Interests: Xujia’s work bridges numerical simulations and statistics, addressing topics like uncertainty propagation, sensitivity analysis, and surrogate modeling for stochastic systems. Key areas include polynomial chaos expansions, Bayesian active learning, and applications in seismic fragility analysis. Publications: His recent work emphasizes emulation techniques for stochastic simulators, multi-fidelity methodologies, and Bayesian active learning strategies in reliability analysis. Key themes include sparse polynomial chaos expansions and latent variable modeling. Labs/Teams: Affiliated with L2S, he collaborates on transversal projects in energy, industry, and health, leveraging interdisciplinary approaches in uncertainty quantification and computational modeling.

• Uncertainty Quantification
• Surrogate Modeling
• Stochastic Simulators
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Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.

• Inverse Problems
• Data Assimilation
• Partial Differential Equations
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Cristina Butucea is a Professor of Statistics and Machine Learning at ENSAE-CREST, Institut Polytechnique de Paris . She was nominated an IMS Fellow (2019) for her contributions to nonparametric and high-dimensional statistics. She has co-organized major conferences like Fréjus 2018 , Luminy 2019-2020 , and Oberwolfach 2021 , and serves as Associate Editor for ALEA . Fields of interest: Nonparametric statistics, quantum statistics, differential privacy, inverse problems, machine learning. Awards: IMS Fellowship, conference organization leadership. Research trends: Focuses on optimal estimation under privacy constraints, quantum state reconstruction, high-dimensional inference, and adaptive nonparametric methods. Email: Cristina.Butucea@ensea.fr | Cristina.Butucea@ip-paris.fr

• Nonparametric Statistics
• High-Dimensional Statistics
• Inverse Problems
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Basile de Loynes is a Lecturer at the French National School of Statistics and Information Analysis (ENSAI), holding a permanent academic position since at least 2016. He maintains a dual affiliation as a CREST (Center for Research in Economics and Statistics) Affiliated Member, contributing to interdisciplinary economic-statistical research. His academic trajectory includes a postdoctoral position at the University of Neuchâtel (2012), followed by temporary lecturer roles at the University of Burgundy (2013-2014) and University of Strasbourg (2014-2016). His research centers on advanced probability theory with specific expertise in stochastic processes on non-Euclidean structures. Key areas include: Random walks on algebraic structures (groups, groupoids, tilings, graphs) Poisson-Martin boundary theory and potential analysis Long memory processes and invariance principles Graph signal processing with Fourier/wavelet methods His publication record shows consistent output in top-tier journals since 2012, with recent work (2021-2023) focusing on graph-based signal denoising and differential privacy applications. Analysis of his 10 most recent publications reveals a strong methodological thread connecting classical probability theory with modern graph-based signal processing. Approximately 60% of his work since 2016 involves graph-structured stochastic models, demonstrating an evolving research trajectory from theoretical random walk properties toward applied graph signal analysis. The recurring subfields across publications include Markov additive processes, spectral graph theory, and wavelet transforms on non-Euclidean domains. His academic service includes developing comprehensive teaching materials for core probability and measure theory courses at ENSAI, with publicly available lecture notes and examinations dating back to 2016.

• Probability Theory
• Stochastic Processes
• Graph Signal Processing
• +۴ مورد دیگر

Christophe Charrier is a Full Professor in Forensics and AI at Université de Caen Normandie, affiliated with GREYC UMR CNRS 6072 and IUT Grand Ouest Normandie's Multimedia and Internet Department (Dept. MMI). He obtained his PhD in Computer Science from Université Jean Monnet (Saint-Etienne) in 1998, followed by an HDR (Habilitation à Diriger des Recherches) in 2011 from Université de Caen Normandie. His academic journey includes roles as a Postdoctoral Researcher at Université Laval (1998-2001), Associate Professor at IUT Saint-Lô (2001), and Visiting Scholar/Professor positions at University of Texas at Austin (2008) and University of Sherbrooke (2009-2011). His research focuses on Digital Image and Video Forensics (e.g., deepfake detection), Image/Video Quality Assessment , Computational Vision , and Biometrics (fingerprint quality, template update, presentation attack detection). He leads the SAFE research group since 2016 and collaborates with the E-payment & Biometrics team at GREYC. His work integrates machine learning for quality metrics, biometric system evaluation, and forensic analysis. Recent publications highlight advancements in deepfake detection , 3D mesh quality assessment , and biometric security . Articles span journals like Intelligent Service Robotics (2024), IEEE Access (2024), and conferences such as CORESA (2024) and Cyberworlds (2023-2024). His studies on fingerprint systems, behavioral biometrics, and environmental impacts on data quality underscore his interdisciplinary approach. Scientific Awards : Best PhD Paper Award (ASONAM 2022) Best Full Paper Award (CW2022) He has mentored 14 PhD students since 2003, including notable alumni like Xinwei Liu (Zhejiang Wanli University) and Antoine Cabana (ALTEN, Toulouse). His projects span biometric certification, latent space manipulation, and 3D mesh evaluation, often in collaboration with institutions in Canada, Norway, and Morocco.

• Digital Image Forensics
• Video Forensics
• Biometrics
• +۷ مورد دیگر

Vassilis Christophides is a Professor of Computer Science at the University of Crete and holds an advanced research position at Inria Paris, where he leads work in the MiMove team. His research spans databases, web information systems, big data processing, and IoT analytics, with a strong emphasis on entity resolution, data integration, and scalable data mining. He has supervised numerous research projects funded by the European Union and the Greek State, and has published over 130 articles in top-tier conferences and journals. Research Interests: His primary research areas include Databases, Web Information Systems, Big Data Processing and Analytics, and Information Systems for the Internet of Things. He also focuses on entity resolution, knowledge graphs, streaming data, and explainable AI, particularly in the context of anomaly detection and fairness-aware data systems. His recent work explores hybrid attention models for entity alignment and causal analysis in time series classification. Recent Research Trends: Analysis of his recent publications (2021–2025) reveals a strong focus on entity resolution with fairness constraints, explainable anomaly detection, and adaptive scheduling in IoT edge analytics. He also investigates deepfake detection, crop type mapping using satellite data, and structural bias in knowledge graphs, demonstrating a broad and impactful research portfolio at the intersection of data management and machine learning. Scientific Awards: 2004 SIGMOD Test of Time Award Best Paper Award, ISWC 2003 Best Paper Award, ISWC 2007 Advising and Grants: While specific student names are not listed in the provided texts, Christophides has co-authored numerous papers with researchers such as Vasilis Efthymiou, Ioannis Tsamardinos, and Nikolaos Myrtakis, suggesting active mentorship. He has been the scientific coordinator of multiple EU and national research projects, indicating substantial grant leadership and project management experience. Labs and Teams: He is affiliated with the MiMove team at Inria Paris, a research group focused on mobility and data-intensive systems. His work bridges academic and applied research, leveraging Inria’s infrastructure for large-scale data experimentation and innovation in IoT and edge computing environments.

• Databases
• Web Information Systems
• Big Data Processing
• +۹ مورد دیگر