جستجوی استاد راهنمای دانشگاهی

Anna Tykhonenkoمشاهده پروفایل

استاد

• Macroeconomics
• Applied Economics
• Econometrics
• +۲ مورد دیگر

Anna Tykhonenko is a Full Professor of Economic Sciences at Université Côte d'Azur, affiliated with the University School of Economics and Management and the Research Group in Law, Economics, Management (GREDEG). She serves as Co-head of the Master's in Expertise and Analysis of Economic Data (EADE), Co-head of the MIASHS Degree, and Head of the 'European Project Engineering' DU program. Her research spans Macroeconomics, Applied Economics, and Econometrics with focus areas including: Prudential supervision and domestic credit dynamics Economic convergence and business cycle synchronization in the EU Environmental Kuznets curves and trade-environment nexus Public debt impacts on twin imbalances Nuclear energy-GDP relationships Her methodological expertise includes Bayesian shrinkage estimation, panel data analysis, and distance-based approaches for heterogeneous economies. Analysis of her recent publications reveals consistent focus on European economic integration challenges, particularly how global crises impact convergence processes. Her work frequently employs advanced econometric techniques to address cross-country heterogeneity in environmental, financial, and macroeconomic contexts. Scientific recognition includes: INFER Young Economist Award (2023) She maintains active research collaborations across international institutions with notable co-authors including Thomas Jobert, Fatih Karanfil, and Veronika Šuliková. Her work has accumulated over 3,283 reads and 384 citations on ResearchGate, reflecting significant scholarly impact in applied economic research.

Mathieu Fontaineمشاهده پروفایل

دانشیار

• Machine Listening
• Speech Enhancement
• Speaker Separation
• +۹ مورد دیگر

Mathieu Fontaine is an Associate Professor in Machine Listening at Télécom Paris , affiliated with the LTCI Lab within the IDS Department (Information, Data, Signal). His research focuses on machine listening for speech and audio signal processing. PhD in Informatics (2019), Lorraine University Master in Applied and Fundamental Mathematics (2015), Poitiers University BSc in Fundamental Mathematics (2013), Rennes University Fontaine's research spans speech enhancement , speaker separation , source localization , and music source separation using heavy-tailed probabilistic models and deep Bayesian networks , with applications in augmented reality . He has expertise in Python , signal processing , and machine learning (80% proficiency). His recent publications (2024) include work on diffusion models for speech synthesis , room acoustics estimation from 3D meshes , robust audio scene analysis , and direction-aware speech processing . Earlier publications (2022-2023) explore flow-based NMF , alpha-stable representations , and adaptive beamforming in multiparty environments. Fontaine collaborates with the S2A team and ADASP group at LTCI Lab. His work integrates probabilistic modeling with deep learning to address challenges in real-world audio processing, including reverberation, noise, and complex acoustic environments.

• Bayesian Statistics
• Bayesian Machine Learning
• Environmental Science
• +۸ مورد دیگر

Julyan Arbel is an Associate Researcher (chargé de recherche) at Inria Grenoble in the Statify team and a member of Laboratoire Jean Kuntzmann at Université Grenoble Alpes. He holds a PhD in Applied Mathematics from Université Paris-Dauphine and graduated from École Polytechnique and ENSAE. He defended his Habilitation à Diriger des Recherches (HDR) in 2019. Research Interests His work spans Bayesian Statistics (approximations, computation, nonparametrics, extreme value theory, objective Bayes) and Bayesian Machine Learning (Bayesian neural networks, variational inference), with applications in environmental science and neuroscience. Key focus areas include: Statistical methods for ecological modeling and species distributions Integration of biotic interactions in predictive frameworks Bayesian approaches to machine learning challenges Publications Overview Recent articles (2022-2025) demonstrate a strong focus on statistical ecology, including species distribution modeling, biotic interactions, community ecology, and biodiversity conservation. Methodological innovations center on joint species distribution models and Bayesian computational techniques applied to environmental datasets. Academic Service Associate Editor: Statistics and Computing (2025–present) Associate Editor: Bayesian Analysis (2019–present) Associate Editor: Australian and New Zealand Journal of Statistics (2019–present) Associate Editor: Statistics & Probability Letters (2019–present) Associate Editor: Statistical Methods & Applications (2024–present) Former Associate Editor: Computational Statistics & Data Analysis (2020–2023) Students Supervised PhD students: Giovanni Poggiato Daria Bystrova Minh Tri Lê Théo Moins Teams Statify Research Team, Inria Grenoble Laboratoire Jean Kuntzmann, Université Grenoble Alpes

Tâm Le Minhمشاهده پروفایل

پژوهشگر

• Statistics
• Ecological Networks
• Nonparametric Statistics
• +۲ مورد دیگر

Tâm Le Minh is a postdoctoral researcher in statistics at Inria Grenoble Rhône-Alpes, focusing on model-based statistical methods and nonparametric models with latent variables. His work bridges statistical theory with applications in life sciences, particularly ecological network analysis. PhD in Applied Mathematics (2023), Université Paris-Saclay Master in Applied Mathematics (2020), Institut Polytechnique de Paris Background in aeronautics and software engineering His research spans U-statistics, row-column exchangeable matrices, and Bayesian nonparametric approaches, with applications to ecological networks and high-throughput sequencing data. Recent publications emphasize bipartite network analysis and variational annealing for optimization. He has presented at major conferences including the International Conference on Bayesian Nonparametrics, Bernoulli-IMS World Congress, and ISBA World Meeting. His work appears in journals like ESAIM: Probability and Statistics and the Electronic Journal of Statistics.

• Financial Econometrics
• Time Series Analysis
• Volatility Modeling
• +۴ مورد دیگر

Jeroen ROMBOUTS is a Professor at ESSEC Business School (France) and holds the Full Professor position of the Accenture Strategic Business Analytics Chair since 2017. He joined ESSEC in 2013, previously serving as Associate Professor at HEC Montreal (2004–2012). His research focuses on financial econometrics, volatility modeling, and machine learning applications in financial markets. He holds a Ph.D. in Econometrics from the Catholic University of Louvain (2004) and has held visiting professorships at numerous institutions, including the University of Melbourne, Aarhus University, and Tilburg University. Education: PhD in Econometrics (2004), Catholic University of Louvain; Master's degrees in Statistics (2001), Econometrics (2000), and Economics (1999), all from the same institution. He is also a Researcher at the Finance and Insurance Lab (CREST) since 2014 and serves on editorial boards of journals like Quantitative Finance and International Journal of Forecasting . Research Interests: His work emphasizes volatility modeling, time series analysis, and applications of machine learning to forecast financial markets. Key areas include GARCH models, structural breaks, and cross-temporal forecasting for digital platforms. He has published extensively in top journals such as Journal of Econometrics and International Journal of Forecasting . Articles Overview: Recent contributions include novel methods for cross-temporal forecast reconciliation using machine learning and sparse change-point VAR models. His work bridges econometric theory with practical applications in asset pricing and risk management. Awards: Recipient of the 2024 Risk-Shift award in France. His research has been recognized for advancing methodologies in volatility modeling and financial econometrics. Advising & Grants: While no specific grants are listed, his roles as a researcher and editor highlight significant contributions to the academic community. He advises on policy and industry applications of his models through consulting roles in financial econometrics and macroeconomic forecasting. Labs & Teams: Affiliated with the Finance and Insurance Lab (CREST) and leads the Information Systems, Data Analytics, and Operations department at ESSEC. Collaborates with global institutions on projects involving high-frequency data and platform economics.

José PICHERALمشاهده پروفایل

استاد

• Signal Processing
• Acoustic Imaging
• Array Processing
• +۵ مورد دیگر

José Picheral is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). He holds a PhD (2003) and HDR (2017) in high-resolution signal processing methods and inverse problems. His research focuses on array processing, source localization, acoustic imaging, and vibration analysis, with applications in aeroacoustics, automotive systems, and industrial monitoring. He has supervised multiple PhD students and contributed to projects like Valeo’s smartphone-based car key replacement system. Education: Engineering Degree: Supélec (1999) and Politecnico di Milano (1999, Erasmus-TIME) PhD: Paris Sud University (2003) Habilitation (HDR): Université Paris Sud (2017) Research Interests: High-resolution methods for distributed sources, sparse signal processing, acoustic imaging, asynchronous measurements, and sensor array design. Current projects include spatial source covariance estimation, EEG spectrum analysis, and automotive applications using smartphone localization. Key Contributions: Over 50 publications in top journals/conferences (e.g., IEEE Transactions, ICASSP). Notable work on MUSIC algorithm robustness, DAMAS optimization, and sparse approaches for tip-timing signals. Advising & Collaboration: Supervised 7 PhD students. Collaborations with SAFRAN, Valeo, and academic teams in Bayesian inference and inverse problems. Active in L2S’s Inverse Problems Group and SYCOMORE team. Labs/Teams: Member of L2S’s Signal Processing and Statistics group, leading research in systems and control, telecommunications, and energy systems.

• Machine Learning
• Statistical Learning
• Bayesian Methods
• +۵ 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.

Anna Korbaمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Inference
• Sampling Methods
• +۴ مورد دیگر

Anna Korba is an Assistant Professor at CREST-ENSAE Paris within the Statistics Department. She holds an ENSAE Engineering degree in Data Science and a Master's in Mathematics, Vision & Learning (MVA) from ENSAE Paris. Her career includes a Ph.D. in Machine Learning at Télécom ParisTech, followed by a postdoctoral position at UCL's Gatsby Unit. Her research focuses on sampling techniques, Bayesian inference, optimal transport, and generative modeling, with recent work on constrained sampling and fairness integration. She contributes to collaborative efforts at the intersection of machine learning, dynamical systems, and PDEs. Notably, she co-presented tutorials on Wasserstein gradient flows at ICML 2022. Her work addresses unsolved challenges in sampling efficiency and fairness constraints. She is actively involved in CREST research initiatives and academic mentorship.

Simon Lacoste-Julienمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Deep Learning
• +۵ مورد دیگر

Simon Lacoste-Julien is an Associate Professor at Université de Montréal, affiliated with the Department of Computer Science and Operations Research (DIRO). He also serves as the Associate Scientific Director of Mila – Quebec Institute of Artificial Intelligence and holds the position of Vice President Lab Director at Samsung SAIT AI Lab Montreal (SAIL). His research focuses on machine learning, optimization, and their applications in areas like deep learning, generative models, causality, and computer vision. Lacoste-Julien has held academic positions at INRIA in Paris and has a PhD from UC Berkeley, with postdoctoral work at the University of Cambridge. He teaches advanced graduate courses on probabilistic graphical models and structured prediction. His work includes contributions to optimization algorithms (e.g., Frank-Wolfe methods), causal discovery, and generative models. Lacoste-Julien has supervised numerous students and postdocs, and his awards include being a CIFAR Fellow and Canada CIFAR AI Chair. His research spans theoretical foundations and practical applications, with a strong emphasis on scalable and efficient machine learning techniques.

Pierre-Alexandre Matteiمشاهده پروفایل

پژوهشگر

• Statistical Machine Learning
• Generative Modeling
• Model Uncertainty
• +۷ مورد دیگر

Pierre-Alexandre Mattei is a research scientist at Inria, affiliated with the Maasai team in Sophia Antipolis and the J.A. Dieudonné Laboratory at Université Côte d'Azur. He holds a chair at the 3IA Côte d'Azur institute and has a strong academic background in applied mathematics, having earned his Ph.D. from Université Paris Descartes (now Université Paris Cité) under Charles Bouveyron and Pierre Latouche, followed by a postdoc at the IT University of Copenhagen with Jes Frellsen. Ph.D. in Applied Mathematics, Université Paris Descartes (2017) Postdoctoral Researcher, IT University of Copenhagen His research lies at the intersection of statistical machine learning, generative modeling, and uncertainty quantification, with a focus on hidden variables, missing data, and model interpretability. He has co-organized major workshops such as Artemiss, GenU, SophI.A Summit, and Statlearn, and teaches at the Generative Modeling Summer School (GeMSS). His recent work spans energy-based models, clustering, semi-supervised learning, and medical AI applications. The 15 most recent publications reflect a consistent trend in developing statistically principled methods for generative modeling, with emphasis on likelihood-based inference, missing data, and information-theoretic approaches to clustering and representation learning. His work frequently appears in top venues like NeurIPS, ICML, ICLR, and AISTATS, as well as in journals such as Statistics and Computing and JACC: Advances. Co-organizer, Generative Modeling Summer School (GeMSS) Co-organizer, Workshop on Generative Models and Uncertainty Quantification (GenU) Co-organizer, SophI.A Summit Co-organizer, Statlearn Mattei has advised several PhD students and postdocs, including Raphaël Razafindralambo, Hugo Senetaire, Louis Ohl, Federico Bergamin, and Hugo Schmutz, many of whom have gone on to research positions in Copenhagen, Grenoble, Linköping, and Marseille. He collaborates extensively with researchers across France and Denmark, particularly with Jes Frellsen, Frédéric Precioso, and Charles Bouveyron. He is actively involved in the development of open-source tools and libraries such as the GemClus Python library for discriminative clustering. He is a key member of the Maasai team at Inria Sophia Antipolis, which focuses on models and algorithms for artificial intelligence, and contributes to the broader 3IA Côte d'Azur initiative aimed at advancing AI research through interdisciplinary collaboration.

• Fuzzy Logic
• Artificial Intelligence
• Neural Networks
• +۶ مورد دیگر

Professor Mahdi Mahfouf holds a Chair in the School of Electrical and Electronic Engineering at the University of Sheffield. He has held academic roles since 1997, progressing from Lecturer to Professor in 2005. His research focuses on Fuzzy Logic, Control Systems, and their applications in biomedical and industrial contexts. Mahfouf leads the Intelligent Systems Research Laboratory and has contributed over 370 publications, including influential work on fuzzy modeling and predictive control. Education: Ing.Dipl. (Hons) in Control Systems MPhil in Control Systems (University of Sheffield, 1988) PhD in Control Systems (University of Sheffield, 1991) Research Interests: Fuzzy Logic applications, Artificial Intelligence, Neural Networks, Model-Based Predictive Control, Biomedical Engineering (e.g., ICU Decision Support Systems), and Manufacturing Systems (e.g., granulation processes, surface metrology). Key Achievements: Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award. His work integrates fuzzy logic into real-time systems for aviation, healthcare, and robotics. Grants & Labs: Leads the Intelligent Systems Research Lab. Active in collaborative projects with industry (e.g., pharmaceuticals, aerospace). His research bridges theory and practice, emphasizing data-driven solutions for complex systems.

Mohammed Nabil EL KORSOمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Detection and Estimation Theory
• +۵ مورد دیگر

Mohammed Nabil EL KORSO is a Professor at CentraleSupélec, part of the University of Paris-Saclay. He is affiliated with the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, detection/estimation theory, and robust signal processing, with applications in radioastronomy, radar systems, and source localization. He has contributed extensively to methodologies like Kalman filtering, covariance estimation, and array processing. His work emphasizes robust techniques for handling non-Gaussian noise and interference, particularly in radio interferometry and SAR imaging. Key contributions include algorithms for array calibration, RFI mitigation, and subspace estimation. His research also addresses challenges in distributed and adaptive signal processing, with applications to vital signs monitoring and robotic systems. He has published over 50 journal articles and conference papers, focusing on performance bounds (Cramér-Rao, Weiss-Weinstein), Bayesian methods, and practical implementations for large-scale systems. His recent work includes advancements in low-cost interferometric imaging and phase estimation for SAR time series. EL KORSO collaborates with international projects like the Square Kilometre Array (SKA), contributing to technological developments in radio astronomy. He supervises research in signal processing labs and actively participates in academic conferences such as EUSIPCO and ICASSP.

Frédéric BERTRANDمشاهده پروفایل

استاد

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +۵ مورد دیگر

Frédéric BERTRAND is a Full Professor at the Université de Technologie de Troyes (UTT), part of the Computer Laboratory and Digital Society (LIST3N). He previously held positions at the Université de Strasbourg and Institut de Recherche Mathématique Avancée (IRMA). His academic qualifications include a habilitation to direct research (HDR) from the Université de Strasbourg, a Ph.D. from Université Louis Pasteur, and membership in the École Normale Supérieure de Lyon. He specializes in statistical modeling, machine learning, and artificial intelligence, with applications in bioinformatics, biostatistics, and medical data analysis. Roles and Affiliations: Full Professor at UTT Director of the Mastère Spécialisé in Big Data Analytical and Decisional Aspects Responsible for the OSS ED361 SPI specialty Responsible for the PEA Impact program Member of the LIST3N laboratory Research Interests: His work focuses on statistical and machine learning methods applied to complex systems, including bioinformatics, medical data analysis, and process mining. Key areas include Bayesian networks, partial least squares regression (PLS), variable selection algorithms, and handling missing data in large-scale datasets. He has developed multiple R packages, such as selectBoost , bootPLS , and Patterns , which are widely used in statistical modeling and data analysis. Publications and Contributions: He has authored/co-authored over 38 scientific articles, 14 books, and 14 R packages. His research emphasizes methodological advancements in statistics and their practical applications in biology, healthcare, and industry. Notable contributions include developing algorithms for high-dimensional data analysis and collaborative projects on cancer genomics and proteomics. Teaching: He teaches advanced courses in statistics, mathematics, and data analysis at the graduate and postgraduate levels, including modules on uncertainty analysis and complex systems at UTT and the Université de Strasbourg.

• Biophysics
• Gene Regulatory Networks
• Immune System Dynamics
• +۶ مورد دیگر

Aleksandra Walczak is a Professor of Biophysics at the École normale supérieure , leading research at the Laboratoire de Physique Théorique (LPENS). Her work bridges statistical physics and biology, focusing on understanding complex living systems through gene regulatory networks, immune system dynamics, and population genetics. Research Interests : Non-equilibrium biological systems, T-cell/B-cell receptor repertoires, stochastic molecular systems, and evolutionary dynamics. Funding & Collaborations : Supported by the Fondation Bettencourt Schueller, she co-organizes interdisciplinary workshops like the Paris Workshop on Immunology and contributes to GDRI Evolution, Regulation and Signaling network. Recent Publications explore gene regulatory principles, immune repertoire modeling, viral-immune coevolution, and collective behavior. She has secured competitive grants and mentors students in theoretical biophysics. Awards : Habilitation à diriger des recherches (2012), Princeton Center for Theoretical Physics Postdoctoral Fellowship (2007-2010). Students & Positions : Actively supervises PhD/Master projects; offers internships in biophysics and quantum engineering.

Marcelo Pereyraمشاهده پروفایل

دانشیار

• Mathematical Imaging
• Bayesian Analysis
• Computational Imaging
• +۳ مورد دیگر

Marcelo Pereyra is an Associate Professor in the School of Mathematics and Computer Science at Heriot-Watt University and the Maxwell Institute for Mathematical Sciences. He is a visiting professor at the Physics Laboratory of École Normale Supérieure de Lyon (ENS de Lyon) from April 8–29, 2023, hosted by Julián Tachella. His research focuses on Bayesian analysis, computational imaging, and inverse problems, with applications in signal processing and machine learning. Education: Electronic engineering degrees from universities in Buenos Aires and Toulouse, followed by a PhD in signal processing from the Université de Toulouse (2012). Postdoctoral fellowships included roles at the University of Bristol (2012–2016), funded by Marie Curie, Brunel, and French Ministry of Defense grants. In 2019, he held a visiting professorship at the Institut Henri Poincaré. Current collaboration with ENS de Lyon’s SiSyPh team involves developing Bayesian-deep learning methods for blind/semi-blind inverse imaging problems and uncertainty quantification in pandemic modeling (e.g., COVID-19 reproduction number estimation). His work bridges Bayesian inference, convex optimization, and Monte Carlo sampling techniques. Awards: Marie Curie Fellowship, Brunel Postdoctoral Fellowship, French Ministry of Defense Fellowship. Key Projects: Bayesian imaging with Plug-and-Play priors, empirical Bayesian regularization estimation, sparse Bayesian mass-mapping in astronomy. He delivered a seminar on April 26, 2023, titled “Machine Learning and Signal Processing.”