جستجوی استاد راهنمای دانشگاهی

Alexandre RUBESAMمشاهده پروفایل

دانشیار

• Finance
• Asset Pricing
• Financial Econometrics
• +۵ مورد دیگر

Alexandre RUBESAM is an Associate Professor at IÉSEG School of Management (France), specializing in Finance with a focus on asset pricing, financial econometrics, and quantitative trading. He holds a Ph.D. in Finance from Cass Business School (UK), an MSc in Statistics from the State University of Campinas (Brazil), and a Bachelor in Statistics from the same university. Education: Ph.D., Finance, Cass Business School, UK (2008) MSc., Statistics, State University of Campinas, Brazil (2004) Bachelor, Statistics, State University of Campinas, Brazil (2001) His research interests span behavioral finance, risk management, machine learning applications in finance, and portfolio optimization. Notably, he explores topics like market herding during crises, volatility forecasting, and the low-beta anomaly through behavioral lenses. Prof. Rubesam has authored influential papers on information transmission in financial markets, risk parity strategies, and the efficacy of linear models in volatility prediction. His work bridges theoretical finance with practical applications, such as developing machine learning-based portfolio construction methods for emerging markets. Awards: 2007 Dimitris N. Chorafas Foundation Prize 2006 Best Paper Award, Cass Business School His professional roles include Chief Risk Officer at Itaú-Unibanco (2013–2017) and Quantitative Researcher/Trader at Principia Capital Management (2009–2011). He is a member of LEM (Laboratory of Economics and Management) and teaches courses on financial programming, risk management, and portfolio analysis.

Chao Liuمشاهده پروفایل

پژوهشگر

• Surgical Robotics
• Teleoperation
• Haptics
• +۲ مورد دیگر

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۴ مورد دیگر

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.

• Theoretical Deep Learning
• Uncertainty Quantification
• Reliable Artificial Intelligence
• +۱۰ مورد دیگر

Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods

Hugues Aschardمشاهده پروفایل

پژوهشگر

• Statistical Genetics
• Genome-Wide Association Studies (GWAS)
• Microbiome Research
• +۷ مورد دیگر

Hugues Aschard is a Principal Investigator and Structure Manager at the Pasteur Institute in Paris, where he leads research in statistical genetics, microbiome analysis, and computational genomics. He is the principal investigator of the MicMat project, the EpiGenCOV Consortium, and several bioinformatics software initiatives including JASS, RAISS, and MGMM. Research Interests: Statistical and computational methods in genetics Genome-wide association studies (GWAS) Gene-environment interactions Microbiome and host genetics in inflammatory bowel disease Genetic epidemiology of infectious diseases like COVID-19 Development of open-source tools for multi-trait and summary-statistic analysis Recent Research Trends: His recent publications and projects emphasize integrative genetic modeling, multi-trait analysis across diverse populations, and the development of novel computational methods to handle missing data and improve SNP discovery. His work bridges statistical innovation with biological and clinical applications in complex diseases. Scientific Contributions: Development of JASS, RAISS, and MGMM software tools Leadership in large-scale consortia like EpiGenCOV Advancing methods for cross-ancestry genetic studies Advising and Collaboration: He supervises multiple PhD students and postdoctoral fellows, including Christophe Boetto, Antoine Auvergne, and Lucas Chataigner. He collaborates with major institutions such as APHP and CNRGH. His team includes research engineers and administrative staff, indicating an active and well-supported research group. Laboratories and Teams: He is a key member of the Biomaterials and Microfluidics team at the Pasteur Institute, where he contributes to interdisciplinary research involving Bayesian decision processes and genetic modeling.

Olivier FARGESمشاهده پروفایل

مدرس ارشد

• Thermal Engineering
• Energy Systems
• Fluid Dynamics
• +۳ مورد دیگر

Olivier FARGES is a Senior Lecturer and HDR (Habilitation à Diriger des Recherches) holder at the University of Lorraine, affiliated with ENSGSI (École Nationale Supérieure de Géologie et Sciences Industrielles) within the Groupe INP. He serves as Director of Industrial Partnerships at ENSGSI and is part of the LEMTA Laboratory (CNRS-University of Lorraine), focusing on multiphysics and multiscale modeling of heat transfer in complex environments. His academic roles include teaching courses such as Heat and Mass Transfer, Fluid Mechanics, Scientific Computing Modeling, and Renewable Energy. Dr. FARGES holds a Ph.D. in Energy and New R&D (2014) and an Engineering degree in Energy Engineering (2010), both from the École de Mines Albi. His research emphasizes coupled conductive-radiative heat transfer in porous media, thermal property characterization of heterogeneous materials, and Monte Carlo-based computational methods for energy systems. He has contributed to advancements in photovoltaic system modeling, solar thermal power optimization, and urban climate studies. His work bridges theoretical and applied thermal engineering, with applications in sustainable energy systems, material science, and industrial partnerships. Key research themes include radiative transfer modeling, multiphysics simulation frameworks, and the development of innovative tools for thermal property measurement and energy performance assessment.

Andrzej Murawskiمشاهده پروفایل

استاد

• Semantics of Programming Languages
• Automata Theory
• Software Verification
• +۴ مورد دیگر

Andrzej Murawski is a Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Worcester College . His research focuses on the semantics of programming languages and software verification, with applications in automata theory, probabilistic computation, and concurrency. Current affiliations: University of Oxford, SIGLOG Vice-Chair, FoSSaCS Steering Committee Research interests: Game semantics, higher-order recursion, probabilistic systems, differential privacy Recent publications address probabilistic verification, equivalence checking, and game semantics for concurrent systems. He has chaired program committees for conferences such as ESOP and PERR, and his work has earned recognition like the POPL 2025 Distinguished Paper Award . Current students : Benedict Bunting, Haoxuan Yin Past students : Conrad Cotton-Barratt, David Hopkins, Guanyan Li, Dominik Wagner, Fabian Zaiser

Xujia ZHUمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Surrogate Modeling
• Stochastic Simulators
• +۴ 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.

Martin Arangurenمشاهده پروفایل

استاد پژوهش

• Social Interaction
• Nonverbal Communication
• Emotions
• +۱۳ مورد دیگر

Martin Aranguren is a CNRS Junior Professor affiliated with the Centre for Research on Social Inequalities (CRIS) at Sciences Po. His research bridges micro-level social interactions and macro-level social inequalities, employing diverse methodologies including field experiments, interviews, and statistical analysis. He holds a PhD from École des Hautes Études en Sciences Sociales and earned his HDR in 2024, qualifying him to supervise doctoral research. His research interests include: Social Interaction Nonverbal Communication Emotions Discrimination (racial, religious, ethnic) Health Disparities and Mental Health Inequalities Field Experiments and Behavioral Measurement Bayesian Statistics His recent publications reveal a strong focus on discrimination in urban public spaces, particularly examining how race, religion (e.g., Islamic headscarves), and health behaviors (e.g., mask-wearing) influence everyday interactions. His work often uses field experiments in cities like Paris, Brussels, and Vienna to measure subtle forms of bias. A recurring theme is risk compensation during the pandemic, where adopting one safety measure (e.g., masks) leads to reduced adherence to others (e.g., distancing), with significant gender differences observed. His scientific contributions include leading major research projects such as: MIDI (2018–2021) : Studied misrecognitive discrimination using field experiments. GERICO (2021–2022) : Funded by ANR, investigated risk compensation during Covid-19. He also contributes to academic discourse through editorial roles and peer reviewing. He serves on the editorial board of Appartenances & Altérités and has reviewed for journals including Annals of Behavioral Medicine , Ethnic and Racial Studies , and PLoS ONE . He teaches quantitative methods, including Research Design and Regression Analysis, in the Sociology Master’s program at Sciences Po. His methodological rigor and interdisciplinary approach position him as a leading figure in contemporary sociological research on inequality.

Yelva Roustanمشاهده پروفایل

مدرس

• Air Quality Modeling
• Atmospheric Chemistry
• Pollutant Deposition
• +۳ مورد دیگر

Yelva Roustan is a Lecturer at CEREA (joint laboratory between École Nationale des Ponts et Chaussées and EDF R&D) since 2005, specializing in air quality modeling . Her research focuses on multiscale modeling approaches, source-concentration relationships, and pollutant deposition processes. She teaches at the National School of Bridges and Roads, sharing knowledge from her research in atmospheric transport and environmental science. Her work addresses urban pollution challenges, including the impact of low-emission vehicles on air quality, urban tree effects, and pollutant dynamics in street networks. She has developed the MUNICH street-network model and contributed to projects like EURODELTA for multi-pollutant analysis across Europe. Her methods integrate data assimilation, inverse modeling, and Bayesian techniques to improve emission source reconstruction and model accuracy. Key contributions include studies on radioactive release scenarios (e.g., Fukushima, 106Ru event) and urban infrastructure impacts on pollution. Her research bridges atmospheric science with engineering solutions for sustainable urban environments. She holds a PhD in Environmental Engineering from École Nationale des Ponts et Chaussées (2005) and an HDR in Numerical Modeling of Atmospheric Pollutants (2020).

• Nonparametric Statistics
• High-Dimensional Statistics
• Inverse Problems
• +۳ مورد دیگر

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

Anna Korbaمشاهده پروفایل

استادیار

• Machine Learning
• Nonparametric Statistics
• Kernel Methods
• +۵ مورد دیگر

Anna Korba is an Assistant Professor at École Polytechnique, specifically affiliated with ENSAE/CREST in the Statistics Department since September 2020. She is also a co-administrator of the Master Data Science program at École Polytechnique. Her academic journey has positioned her as a leading researcher in machine learning, with particular expertise in kernel methods, optimal transport, and statistical optimization. Dr. Korba received her PhD from Telecom ParisTech in 2018 under the supervision of Prof. Stephan Clémençon. Prior to her current position, she was a postdoctoral researcher at University College London's Gatsby Computational Neuroscience Unit working with Arthur Gretton from December 2018 to August 2020. Her academic foundation includes a Master's degree in Machine Learning and Computer Vision (MVA) from ENS Cachan and ENSAE in 2015. Anna Korba's research primarily focuses on machine learning with emphasis on kernel methods, optimal transport, optimization, particle systems, and preference learning. Her work bridges theoretical statistics with practical machine learning applications, particularly in developing novel sampling and optimization methods. She has made significant contributions to understanding Wasserstein gradient flows, density ratio estimation, and variational inference techniques. Her publication record demonstrates a strong trajectory in top-tier machine learning conferences including ICML, NeurIPS, AISTATS, and ICLR. Her research shows a clear evolution from foundational work on ranking and preference learning during her PhD to more recent contributions in Wasserstein-based optimization, sampling methods, and deep probabilistic modeling. The interdisciplinary nature of her work connects statistics, optimization theory, and practical machine learning applications. Top 10% Oral Presentation at AISTATS 2022 Top 15% Long Oral Presentation at ICML 2021 She actively mentors PhD students and postdoctoral researchers, currently advising seven PhD candidates and having successfully guided several alumni to prestigious positions. Dr. Korba also contributes to the academic community through her role in administering the Master Data Science program and collaborating with researchers across institutions worldwide. As part of the CREST research center, Dr. Korba works within a vibrant team of researchers focused on statistics, machine learning, and their applications to economic and social sciences. Her research group includes current PhD students and postdocs working on various aspects of her research interests, creating a dynamic environment for advancing the field of statistical machine learning.

Mehdi Khamassiمشاهده پروفایل

استاد پژوهش

• Decision Making
• Reinforcement Learning
• Cognitive Neuroscience
• +۵ مورد دیگر

Mehdi Khamassi is a Research Director at the French National Center for Scientific Research (CNRS), assigned to the Institute of Intelligent Systems and Robotics (ISIR) at Sorbonne University in Paris, France. He holds an engineering background in computer science (specializing in AI and statistical modeling) from the National School of Computer Science for Industry and Business (2003), a Cogmaster in cognitive science from Pierre and Marie Curie University (2003), and a PhD in cognitive neuroscience from UPMC/Collège de France (2007). Recruited by CNRS in 2010, he co-organizes the Symposium of Biology of Decision-Making (SBDM) and co-directs the modeling major for the Cogmaster program. His research integrates computational modeling , neuroscience experiments , and robotic systems to study decision-making and learning mechanisms. Key interests include: Reinforcement learning in biological and artificial systems Role of social/non-social rewards in adaptive behavior Ethical implications of autonomous decision-making in AI Neuro-robotic models of hippocampal-prefrontal interactions Recent publications (2023-2025) demonstrate strong focus on reinforcement learning paradigms, AI alignment with human values, neurorobotics, and computational neuroscience. Work frequently bridges machine learning theory with empirical validation in biological systems or robotic platforms. He leads research within the ACIDE team at ISIR, exploring adaptive coordination of learning strategies in brains and robots. Current collaborations include NTUA (Greece), University of Oxford, and University of Trento.

• Turbulent Combustion
• Numerical Simulation
• Heat Transfer
• +۳ مورد دیگر

Ronan Vicquelin is a University Professor (1st Class) at CentraleSupélec, Paris-Saclay University, affiliated with the EM2C Laboratory (CNRS). He serves as Head of the Department of Aeronautics, Space and Transport and co-supervises the High Performance Computing Mésocentre. His academic appointments include previous roles as University Professor (2nd class) and Head of Aerospace programs. Education includes Habilitation (University of Rouen Normandy, 2018), PhD in Energetics (École Centrale Paris, 2010), M.Sc. in Mechanical Engineering & Aerospace (École Centrale Paris, 2006), and Engineering Diploma (École Centrale Paris, 2006). Research focuses on turbulent reacting flows with emphasis on: numerical simulation of combustion systems, LES/DNS methodologies, uncertainty quantification, hydrogen combustion dynamics, conjugate heat transfer, and radiative energy transfer. Current investigations explore flame stabilization mechanisms, multi-physics coupling, and high-performance computing applications for aerospace propulsion systems. Publications predominantly address combustion science, with recent works (2021-2025) emphasizing hydrogen flame dynamics, NOx emission control, advanced numerical methods for reactive flows, and experimental validation of turbulent combustion models. Thermal radiation effects and multi-phase flow interactions constitute emerging themes. Advises multiple PhD candidates with projects funded by ANR, EU programs (ACHIEVE, SOPRANO), and industry partnerships (Safran, Air Liquide). Research grants include PEPR OXY3C, ANR HyMaX, and ANR OXYTEC focusing on zero-emission combustion technologies. Leads experimental and computational research at EM2C Laboratory, coordinating teams working on turbulent combustion diagnostics, high-fidelity simulations, and development of the Mésocentre HPC infrastructure for large-scale CFD.

Etienne Méminمشاهده پروفایل

استاد

• Stochastic Modeling
• Geophysical Flows
• Fluid Mechanics
• +۳ مورد دیگر

Etienne Mémin is a Research Director (Full Professor status) at Inria and leads the Odyssey research group, which is affiliated with multiple institutions including University of Rennes, IRMAR, Ifremer, LOPS, UBO, IMT Atlantique, and Lab-STICC. He serves as a Visiting Professor at the Department of Mathematics, Imperial College London (2020–2026) and is the Principal Investigator of the ERC STUOD grant. His research spans the intersection of geophysical sciences, fluid mechanics, computational sciences, and applied mathematics, focusing on stochastic modeling of fluid flows, data assimilation, and uncertainty quantification. He has developed frameworks for stochastic geophysical flows, coarse-scale simulations, and robust motion estimation techniques. Recent publications highlight his work on stochastic Navier-Stokes equations, ensemble forecasting, and data assimilation for ocean and atmospheric models. He has applied these methods to numerical weather prediction, turbulence analysis, and real-time flow reconstruction using sparse measurements. Scientific Awards: ERC STUOD grant PhD Students: Francesco Tucciarone (ERC STUOD, NEMO code) Benjamin Dufée (Ensemble Kalman filters, ATER position) Berenger Hug (Stochastic Navier-Stokes analysis, teaching) Antoine Moneyron (Stochastic ocean models) Collaborations: Imperial College London (D. Crisan, S. Laizet), Zhejiang University (S. Cai, C. Xu), MétéoFrance (P. Arbogast, O. Pannekoucke), Ifremer (B. Chapron), IRSTEA Lyon (L. Pénard), IRMAR (R. Lewandovsky), University of Buenos Aires (G. Artana), ISSI Beijing (T. Corpetti).