جستجوی استاد راهنمای دانشگاهی

Slava Rychkov is a Permanent Professor of Theoretical Physics at the Institut des Hautes Études Scientifiques (IHES), a position he has held since 2017. He specializes in strongly coupled quantum and conformal field theories, with applications across high energy physics, statistical mechanics, and condensed matter physics. His current research focuses on the conformal bootstrap and renormalization group techniques, including both perturbative and nonperturbative methods like tensor network renormalization. Education: Ph.D. in Physics, Princeton University (2002) Master of Science, Moscow Institute of Physics and Technology (1996) Recent research highlights include a groundbreaking connection between Deligne categories and symmetries of probabilistic loop ensembles in statistical physics, and a novel method for analytic continuation of Euclidean CFTs to Lorentzian signature. His work on the 2+ϵ expansion challenges established assumptions about critical exponents in 3D systems. Publications span topics from tensor renormalization group methods to rigorous mathematical approaches in the conformal bootstrap program. Scientific Awards: Jacques Solvay International Chair in Physics (2025) Grand Prix Mergier-Bourdeix, French Academy of Sciences (2019) New Horizons in Physics Prize (2014) As Deputy Director of the Simons Collaboration on the Nonperturbative Bootstrap, Rychkov leads efforts to rigorously analyze conformal field theories. His former advisees include prominent researchers at institutions like EPFL, Princeton, and Università di Genova. Current projects focus on resolving fundamental questions about critical phenomena and phase transitions using advanced mathematical physics tools.

• Conformal Field Theory
• Quantum Field Theory
• Renormalization Group
• +۴ مورد دیگر

Alice Guionnet is a French mathematician and Research Director at the CNRS, affiliated with the Unité de Mathématiques Pures et Appliquées (UMPA) at École Normale Supérieure de Lyon since 2005. Her research focuses on random matrix theory, probability theory, and their applications in statistical mechanics and large deviations principles. She completed her doctorate in 1995 with a thesis on spin glass dynamics, supervised by Gérard Ben Arous. Her work includes groundbreaking contributions such as the Single Ring Theorem (2009) and studies on large deviations for eigenvalues of Wigner and heavy-tailed matrices. She has been honored with the Oberwolfach Prize (1998), Loève Prize (2009), and Blaise Pascal Medal (2018), and was elected to the French Academy of Sciences in 2017. Key research interests include non-linear Wigner spiked models, spectral phase transitions, and free probability. She has authored over 80 publications, including influential papers on matrix models, eigenvector delocalization, and stochastic processes in disordered systems. Her ERC Project LDRAM (Large Deviations in Random Matrices) explores asymptotic behaviors of random matrix ensembles. Education: PhD from École Normale Supérieure (1995), MSc in Mathematics (ENS Paris, 1989). Labs/Teams: UMPA Lyon, collaborations with CNRS and international institutions. Grants/Awards: Simons Investigator (2012–2015), CNRS Silver Medal (2010).

• Random Matrix Theory
• Probability Theory
• Statistical Mechanics
• +۳ مورد دیگر

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +۵ مورد دیگر

Ammar Mian is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC lab and Polytech Annecy-Chambéry. He holds a PhD from CentraleSupélec (2016-2019) and conducted postdoctoral research at Aalto University (2019-2020). His research focuses on statistical signal processing, machine learning, and Riemannian geometry with applications in remote sensing and frugal computations. He leads the Qanat project, an experiment tracking tool for reproducible research. Research interests include covariance-based methods for SAR image analysis, robust detection algorithms for sonar and GPR systems, and optimization on Riemannian manifolds. His work emphasizes reproducibility in ML and efficient computational techniques for resource-constrained environments. Key contributions include real-time SAR time-series change detection, robust classification using second-order deep learning models, and novel methods for handling missing data in EEG signals. His recent articles (2023-2025) explore reproducibility frameworks, GPR-based object classification, and Riemannian geometry applications. No awards listed, but maintains active collaborations through LISTIC and industry partnerships. Advises students via internship programs (e.g., Federated ML energy cost analysis). Lab work involves developing open-source tools like Qanat for experiment management and reproducibility.

• Statistical Signal Processing
• Machine Learning
• Riemannian Geometry
• +۴ مورد دیگر

Maryam Mehri Dehnavi is an Associate Professor in the Department of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing and leads the ParaMathics research group. Research focuses on high-performance computing , machine learning , sparse matrix optimizations , and compiler design for heterogeneous systems. Her work develops domain-specific languages , scalable numerical libraries , and auto-vectorization techniques for cloud and GPU platforms. Recent publications address LLM compression , sparse code translation , GPU kernel synchronization , and control flow optimization . Scientific recognition: Ontario Early Researcher Award (2021), NSF CRII Grant, NSERC New Frontiers in Research Fund. Current students: Mushegh Shahinyan , Martin Phan , Maryam Haghifam , and others. Former advisees: Kazem Cheshmi (NJIT), Zachary Blanco (MIT Lincoln Lab), Yuanxi Li (Amazon).

• High-Performance Computing
• Cloud Computing
• Machine Learning
• +۳ مورد دیگر

Stéphane Nonnenmacher is a Professor of Mathematics at Université Paris-Saclay, where he leads the Ecole Doctorale Mathématique Hadamard. His research focuses on quantum chaos, spectral theory, and dynamical systems, with notable contributions to eigenfunction localization and resonance distribution in chaotic systems. He co-organizes events like the Paris-Saclay conference on Analysis and PDE (2024) and the monthly seminar 'Problèmes spectraux en physique mathématique.' He is affiliated with ANR ADYCT and the Groupement de Recherches Dynamique Quantique. Teaching includes advanced courses on semiclassical analysis and partial differential equations at the undergraduate and graduate levels. Research interests span theoretical and applied mathematical physics, emphasizing spectral properties of quantum systems, scattering theory, and the interplay between classical and quantum dynamics. His work often involves collaboration with leading researchers in the field. Teaching activities in 2024-25 include an analysis course for physics students and a master's-level course on semiclassical analysis. He also taught a course on measure theory and probability at the Université des Sciences et Technologies de Chine (USTC).

• Mathematical Physics
• Quantum Chaos
• Spectral Theory
• +۳ مورد دیگر

Derya Malak is an Assistant Professor at EURECOM in the Communication Systems Department, with an adjunct position at Rensselaer Polytechnic Institute (RPI) in the Department of Electrical, Computer, and Systems Engineering. She leads the ERC-funded SENSIBILITÉ project, focusing on distributed computing of nonlinear functions over communication networks. Education: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin, 2017 M.S. in Telecommunication Engineering, Koç University, 2013 B.S. in Electrical and Electronics Engineering, Middle East Technical University (METU), 2010 Minor in Physics, METU Her research bridges information theory, coding, and distributed computing, with emphasis on reducing communication costs through structured data encoding. She explores distributed matrix multiplication, function computation over networks, caching optimization, and secure computation, leveraging tools from graph theory and stochastic modeling. Her work has significant implications for content delivery networks, distributed machine learning, and efficient data transmission. Her recent publications focus on distributed computation using structured coding schemes and multi-server frameworks, demonstrating substantial gains over classical approaches. These works are unified by a theme of optimizing computation under limited bandwidth and correlated data structures. Scientific Awards: ERC Starting Grant (2022) Best Paper Award, WiOpt 2023 Best Paper Award, WiOpt 2022 Harold N. Trevett Award (student advisee) She advises PhD students, including Aiden Chen, and has secured major grants from the European Research Council (ERC), Agence Nationale de la Recherche (ANR), National Science Foundation (NSF), Rensselaer-IBM AI Research Collaboration, and DARPA Dispersive Computing. She teaches Information Theory and Advanced Wireless Communications at EURECOM, and previously taught Digital Communications and Signal Processing at RPI. She leads the FASS student seminar series and organizes workshops such as the Workshop on Distributed Computing, Optimization & Learning. Her lab focuses on theoretical and practical aspects of distributed computation, with strong ties to computer science and information theory.

• Information Theory
• Distributed Computing
• Communication Theory
• +۱۲ مورد دیگر

Pierre ALQUIER is a Professor at ESSEC Business School (Singapore) since 2023, specializing in statistical learning and machine learning. Previously, he held professorships at ENSAE Paris (2014–2019) and the University of Dublin (2012–2014). He earned his PhD in Mathematical Statistics from Pierre and Marie Curie University in 2006, with a focus on advanced statistical methodologies. His research centers on Bayesian methods, PAC-Bayes bounds, high-dimensional data analysis, and robust estimation, with applications in quantum computing and time series. He has authored over 60 peer-reviewed articles, including influential works on kernel mean embeddings and meta-learning. Alquier has received the 2019 Best Paper Award at the Asian Conference on Machine Learning. He actively contributes to academic leadership, serving as an associate editor for leading journals like the Journal of Machine Learning Research and organizing international workshops. His educational contributions include co-supervising multiple doctoral theses on topics like robust Bayesian inference and non-negative matrix factorization. Education: PhD in Mathematical Statistics (2006), Pierre and Marie Curie University MSc in Probability Theory and Statistics (2003), Pierre and Marie Curie University Diploma in Statistician-Economist (2003), ENSAE Research Focus: Machine learning theory, PAC-Bayes bounds, Bayesian computation, high-dimensional statistics, quantum tomography, and time series forecasting. Grants & Activities: Member of key academic societies (IMS, SFdS), reviewer for top conferences (NeurIPS, ICML), and organizer of workshops on approximate Bayesian inference and high-dimensional data analysis. His recent work emphasizes robust regression, meta-learning, and the theoretical foundations of deep learning, often addressing challenges in dependent data and model misspecification. He has developed R packages like regMMD for robust statistical estimation.

• Machine Learning
• Statistical Learning
• Bayesian Methods
• +۵ مورد دیگر

Charles Bertucci is a CNRS researcher in Mathematics at the Applied Mathematics department of École polytechnique in Palaiseau, France. He also serves as a part-time teacher at École polytechnique since 2020. Bertucci defended his thesis on December 11, 2018, and his Habilitation à Diriger les Recherches (HDR) in June 2022, granting him accreditation to supervise research. His educational background includes being a former student of École Polytechnique (class of 2012) and Paris-Sorbonne University. His doctoral studies were conducted at Paris-Dauphine University under the supervision of Pierre-Louis Lions. Bertucci's research focuses primarily on mean-field game theory , optimization , and the analysis of partial differential equations . He is particularly interested in identifying stability principles for equations posed in infinite dimensions. His work spans theoretical mathematics with applications in economics, finance, and real-world phenomena such as oil markets, cryptocurrency markets, and telecommunications. His interdisciplinary approach bridges pure mathematics with practical applications in various economic and technological domains. Analysis of his recent publications reveals a strong focus on mean field games theory, with extensions to applications in finance (particularly cryptocurrency markets), optimal transport theory, and connections to PDEs. His work often involves collaborations with leading mathematicians including Pierre-Louis Lions, Jean-Michel Lasry, and others. The research demonstrates both theoretical depth in mathematical analysis and practical relevance to economic modeling. His notable scientific achievements include: Recipient of the prestigious Peccot Course for 2022-2023 at Collège de France Bertucci has been actively involved in academic service, including organizing the workshop "Mean Field Games and Applications" in 2022 with Yves Achdou, Jean-Michel Lasry and Pierre-Louis Lions. His teaching activities include delivering the Peccot Course on "Mean-field games and stochastic control in Wasserstein space" in 2023 at Collège de France, consisting of four lectures between March 31 and April 21, 2023. His research is conducted within the vibrant mathematical community at École polytechnique and through collaborations with researchers at CNRS and other institutions, focusing on advancing the theoretical foundations of mean field games while exploring novel applications across various domains.

• Analysis PDEs
• Mean Field Games
• Game theory
• +۴ مورد دیگر

Tatiana Starikovskaya is an Assistant Professor (Maître de Conférences) in the Computer Science Department at École normale supérieure (ENS), Paris, France. She leads the PARSe project (ANR-20-CE48-0001) focusing on approximation and randomized string processing, and participates in AlgoriDAM (ANR-19-CE48-0016). Co-supervises PhD students T. El Ghazi and Gabriel Bathie Co-chaired CPM 2021; served on program committees for STACS, ESA, ICALP, and others Organized CPM summer school (2023) and 'New Horizons of Stringology' workshop (2024) Research Focus Her work centers on algorithms on strings , small-space algorithms , and streaming models , with applications in bioinformatics and data security. Recent research explores trade-offs between time/space complexity in pattern matching, wildcards, and error-tolerant string analysis. Publications Trends Her recent work spans streaming algorithms , compressed data processing , and approximate pattern matching , with collaborations on challenges like k-mismatch problems, Dyck edit distances, and language distance estimation. Education PhD in Mathematics, Lomonosov Moscow State University (2013) M.Sc. in Data Science (Moscow Institute of Physics and Technology/Yandex, 2009) M.Sc. in Mathematics, Lomonosov Moscow State University (2009)

• Algorithms on strings
• Small-space algorithms
• Data structures
• +۴ مورد دیگر

Sylvain Carrozza is a Teacher-Researcher (Maître de Conférences / Associate Professor) at the Institut de Mathématiques de Bourgogne (IMB), University of Burgundy, France. His work bridges gravitational physics, quantum theory, and combinatorics, with a focus on tensor models and group field theory. Education: PhD in Mathematical Physics (2013), Université Paris-Saclay (formerly Paris-Sud 11) Agrégation de Mathématiques (2010), École Normale Supérieure de Lyon Master in Theoretical Physics (2009), École Normale Supérieure de Lyon His research explores the large N limit of random tensors for quantum gravity in dimensions ≥3, non-perturbative quantum field theory , and boundary dynamics in generally covariant theories . Recent work extends these techniques to quantum information, particularly multipartite entanglement and quantum reference frames. Key trends in his publications include: Advancements in tensor models and group field theory for quantum gravity Analysis of edge modes and boundary effects in gauge and gravitational theories Applications to quantum information , such as error correction and entanglement Renormalization and scaling limits in multi-matrix and tensor systems Scientific Awards: Radboud Excellence Fellow (2020–2022) He is affiliated with the Mathematics-Physics team at IMB and contributes to the Parity BFC Mathematics Federation . His collaborations span institutions in France, Netherlands, Canada, and Germany.

• Classical and quantum gravity
• Quantum field theory
• Random tensors
• +۳ مورد دیگر

Arshak Minasyan is an Associate Professor at CentraleSupélec – Université Paris-Saclay and a member of the Laboratoire Signaux et Systèmes (L2S), specifically the Modélisation et Estimation (GME) team. His research focuses on robust statistics and learning theory, particularly addressing computational and statistical complexities in high-dimensional data analysis. Education PhD in Mathematics from Yerevan State University (YSU), supervised by Prof. Victor Ohanian. Master’s in Mathematics from Skoltech. Bachelor’s in Mathematics from Higher School of Economics (HSE). Research Interests His work explores robust statistical methods for high-dimensional data, including outlier-robust algorithms, optimal estimation techniques, and the intersection of learning theory with statistical guarantees. Key areas include feature matching under noise, mean and covariance estimation, and generative modeling with distributional robustness. Key Contributions Notable contributions include the All-in-one Robust Estimator of the Gaussian Mean (Annals of Statistics, 2022) and outlier-tolerant methods for feature matching. His research emphasizes both theoretical foundations and computational tractability, with applications in signal processing and machine learning. Teaching Lecturer of “High-dimensional statistics” at Université Paris-Saclay. Teaching Assistant roles at CREST-ENSAE, HSE, and Skoltech in courses like Probability Theory, Stochastic Processes, and Biostatistics. Labs & Collaborations Affiliated with the L2S lab’s Modélisation et Estimation team, contributing to projects like COMEDY (Control of Dynamical Systems) and SYCOMORE (Signal Processing).

• Robust Statistics
• High-Dimensional Data Analysis
• Learning Theory
• +۳ مورد دیگر

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE Paris, a founding member of the Institut Polytechnique de Paris, and a permanent member of CREST (Center for Research in Economics and Statistics). Since September 2020 he has held this faculty position, after completing a post-doctoral fellowship at the University of Genoa and earning his PhD from École Polytechnique. Education PhD in Statistics, École Polytechnique (2016–2019) MSc in Mathematics, "Probability and Random Models", Université Pierre et Marie Curie (2016) MSc in Mathematics, "Fundamental Mathematics", Université Pierre et Marie Curie (2015) BSc in Mathematics, Université Pierre et Marie Curie & École Normale Supérieure (2013) Student at École Normale Supérieure (2012–2016) Research Interests Mourtada’s work lies at the intersection of statistics and learning theory, with a focus on understanding the fundamental complexity of prediction and estimation tasks. His interests span: High-dimensional statistics and minimax theory Statistical learning theory and generalization bounds Online learning, regret minimization, and expert aggregation Density estimation and robust statistics Random forests, kernel methods, and convex optimization Research Output Trends Across more than fifteen recent publications, Mourtada has systematically advanced the understanding of statistical and computational limits in learning. His contributions range from exact minimax analyses of linear least squares and novel robust regression guarantees to refined PAC-Bayesian bounds for aggregation and sharp asymptotics for ridge regression. A recurrent theme is the development of estimators that achieve optimal or near-optimal rates while remaining computationally tractable and adaptive to unknown parameters. Scientific Awards & Recognition While the provided materials do not list specific awards, his sustained publication record in top venues such as Annals of Statistics , Journal of Machine Learning Research , Journal of the European Mathematical Society , and leading ML conferences (NeurIPS, COLT, AISTATS) attests to significant peer recognition. Teaching & Mentoring Mourtada has extensive teaching experience at both undergraduate and graduate levels, covering probability, statistics, and machine learning. Courses delivered include: Statistical Learning Theory (M2 Data Science, École polytechnique & ENSAE) Probability Theory (ENSAE) Python for Probability, Statistics, and Machine Learning (École polytechnique) Optimization for Data Science (M2 Data Science, École polytechnique) Laboratories & Collaborations He is affiliated with CREST/ENSAE and has previously collaborated with the Laboratory for Computational and Statistical Learning at the University of Genoa, the Center for Applied Mathematics (CMAP) at École Polytechnique, and maintains ongoing research ties with international scholars in statistical learning and optimization.

• High-dimensional statistics
• Statistical learning theory
• Online learning
• +۳ مورد دیگر

Roland Badeau is a Full Professor in the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) Department at Télécom Paris, Institut Polytechnique de Paris. His primary affiliation is with the Information Processing and Communication Laboratory (LTCI). Research Interests: Badeau specializes in statistical modeling of non-stationary signals, with core expertise in adaptive high-resolution spectral analysis and Bayesian extensions to Non-negative Matrix Factorization (NMF). His work spans room acoustics (stochastic reverberation models), data representation (dimensionality reduction, time-frequency analysis), probabilistic latent variable modeling, and algorithm development (Bayesian estimation, optimization methods, fast adaptive algorithms). Applications focus on audio/music processing including source separation, denoising, dereverberation, multipitch estimation, and automatic music transcription, with extensions to biomedical data analysis and digital communications. Key Trends in Publications: Recent work centers on Statistical Wave Field Theory, establishing mathematical frameworks for reverberation modeling using energy-stress tensor formalism and Riemannian geometry. His publications demonstrate a progression from foundational signal processing algorithms (e.g., YAST, ESPRIT) to physics-informed approaches for polyhedral rooms and frequency-dependent attenuation, with strong emphasis on Bayesian and alpha-stable distribution methods for robust audio separation. Academic Leadership: Badeau supervises doctoral and master’s theses while leading teaching units at Télécom Paris. He serves as the TSIA study track supervisor (Signal Processing for Artificial Intelligence) and Master ATIAM correspondent. His team (S2A) develops tools like DESAM for joint source separation and multi-track coding.

• Signal Processing
• Audio Signal Processing
• Machine Learning
• +۷ مورد دیگر

Benjamin Negrevergne is an Associate Professor at the LAMSADE laboratory within Paris Dauphine University (PSL Research University), actively engaged in Machine Learning research. He co-leads the MILES research team and serves as co-director of the IASD Master's Program in AI and Data Science. He also contributes to PSL's computer science graduate program and is launching Nautia, a startup focused on reinforcement learning for sailing automation. His research spans Generative Modeling, Adversarial Machine Learning, and Constraint Programming, with over 15 recent publications in top conferences (NeurIPS, ICML, ACL). He has advised notable Ph.D. students including Alexandre Araujo (postdoc at NYU), Boris Doux (AWS), Alexandre Vérine (École Normale), Lucas Gnecco (ANR project DELCO), and Kunhao Zheng (Meta/FAIR). Key article trends include improving language model diversity, adversarial robustness, diffusion model optimization, and precision-recall metrics for generative systems. His work often combines theoretical rigor with practical applications in cybersecurity, game AI, and parallel computing. Benjamin holds a Ph.D. from the University of Grenoble (2011) with a thesis on parallel pattern mining algorithms. He collaborates with researchers like Yann Chevaleyre, Gabriel Synnaeve, and Tristan Cazenave, with publications in journals such as DMKD and KAIS.

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۳ مورد دیگر