جست‌وجوی استاد و پژوهشگر

Luca Ganassaliمشاهده پروفایل

استادیار

• Algorithmic Fairness
• Optimal Transport
• Statistical Inference in Graphs
• +۴ مورد دیگر

Luca Ganassali is an Assistant Professor in the Department of Mathematics at Université Paris-Saclay and a member of the Inria Celeste team. Previously, he was a postdoctoral researcher at EPFL (BAN Chair) and completed his PhD at Inria Paris under Laurent Massoulié and Marc Lelarge. His research focuses on algorithmic fairness, optimal transport in statistical learning, statistical inference in graphs and matrices, and informational computational thresholds for random instances. Education: PhD in Computer Science from Inria Paris (2022), postdoctoral studies at EPFL (2022). Research interests include causal discovery, graph alignment, and the intersection of machine learning with mathematical statistics. He actively collaborates on projects like the PhD offer on handling unfairness in data through mathematical and machine learning approaches. His work bridges theoretical computer science with applied statistics, addressing challenges in alignment of graph databases, detection of correlations in tree structures, and developing efficient algorithms for sparse graph alignment. Recent contributions include foundational results on Gaussian-weighted graph alignment thresholds and impossibility results in partial graph recovery. Lab affiliations: Inria Celeste team. Advising: Currently seeking a PhD student for a project on mathematical statistics and machine learning.

• Data Analytics
• Statistics
• Structural Equation Modeling
• +۵ مورد دیگر

Vincenzo Esposito Vinzi is a Professor and Director General of ESSEC Business School, France. He holds a PhD in Statistics and Computer Science from the University of Naples Federico II. His academic career includes roles as a visiting professor at institutions worldwide and leadership in international statistical societies such as ISBIS and the European Council of IASC. His research focuses on advanced statistical methodologies like Partial Least Squares (PLS) Path Modeling, Data Analytics, and their applications in business and environmental studies. He has authored over 80 journal articles and edited several books for publishers like Wiley and Springer. Education: PhD in Statistics and Computer Science (1997), University of Naples Federico II; Master in Economics and Management (1993), same institution. Research Interests: PLS-based methodologies, composite indicator modeling, environmental action analysis, and business sustainability. He has pioneered advancements in component-based structural equation modeling and their use in multidisciplinary fields such as tourism, healthcare, and climate policy. Notable Achievements: Winner of the 2005 Pierre Vernimmen Teaching Award, recipient of Top Cited Article Awards (2010), and Honorable Mention at the 2014 PLS Conference. He has served on editorial boards of journals like Computational Statistics and Advances in Data Analysis and Classification . Leadership Roles: President of the Diversity Commission at the Conférence des Grandes Écoles (CGE), interim President of the Conference of Directors of French Management Schools (CDEFM), and board member of institutions like Ecole CentraleSupélec and France Digitale. He also advises organizations including the OECD’s Business for Inclusive Growth program. Grants & Projects: Led EU-funded initiatives like the European Customer Satisfaction Index (ESIS) and collaborated on projects such as TESS (seasonal adjustment systems). His work bridges academic research with industrial applications, particularly in sensory data analysis and consumer behavior modeling. Labs/Teams: Core contributor to ESSEC’s Data Analytics and Operations department, co-organizer of international research workshops on PLS developments, and mentor for doctoral students in statistics and business analytics.

• Mathematical Statistics
• Applied Probability
• Machine Learning
• +۵ مورد دیگر

Mikolaj Kasprzak is an Assistant Professor at ESSEC Business School, specializing in mathematical statistics and applied probability. He holds a DPhil (PhD) in Statistics from the University of Oxford, following a Master’s degree in Mathematics, Operational Research, Statistics, and Economics from the University of Warwick. His career includes postdoctoral research at the University of Luxembourg and a Marie Skłodowska-Curie Individual Fellowship (2021–2023), with research stints at MIT and UCL. He joined ESSEC in 2024 as part of the Information Systems, Data Analytics, and Operations department. Research Interests: Mikolaj focuses on rigorously quantifying the accuracy of approximations in applied probability, statistics, and machine learning. His work involves developing mathematical tools to bound distances between probability distributions, with applications in Stein’s method, U-statistics, and stochastic processes. He emphasizes theoretical foundations while addressing real-world challenges in statistical inference and computational methods. Key Contributions: His research spans functional central limit theorems, kernel Stein discrepancies, and validated variational inference. Recent work includes advancements in goodness-of-fit tests for high-dimensional measures and error bounds for Bayesian posterior approximations. Awards: Marie Skłodowska-Curie Fellowship (2021), New Researcher Travel Award (2019), EUTOPIA Young Leaders Academy Fellowship (2024). Grants: Junior Chair of Excellence in Data Analytics (CY Initiative, 2024). Academic Roles: He also holds the Chaired Professorship in Data Science at ESSEC (2024–2028). His research has been published in top journals like Bernoulli , Annals of Applied Probability , and Probability Theory and Related Fields .

• Financial Econometrics
• Asset Pricing
• Credit Derivatives
• +۵ مورد دیگر

Andras FULOP is a Professor of Finance at ESSEC Business School in France, specializing in financial econometrics, asset pricing, and credit derivatives. He holds a Ph.D. in Finance from the Rotman School of Management (2006), an M.A. in Economics from the University of Toronto (2000), and an M.Sc. in Economics from Budapest University of Economic Sciences (1999). His career includes roles as Co-Head and Head of the Finance Department at ESSEC (2016–2019), Visiting Researcher at the Bundesbank (2016–present), and Research Consultant at the Hungarian National Bank (2012–2013). Research interests focus on Bayesian methods, structural credit risk modeling, market liquidity, and macrofinance. Notable works include studies on transparency initiatives in CDS markets, long-run risk models, and self-exciting jumps in asset prices. FULOP has received awards such as the 2023 EUROFIDAI-ESSEC Best Paper Award and the 2019 ESSEC Foundation White Project Award. He serves as an Associate Editor for the Journal of Financial Econometrics and Subject Editor for the Journal of Multinational Financial Management . His research articles span topics like real-time Bayesian learning in bond markets, news indices for country fundamentals, and computational methods for filtering diffusions. FULOP has advised multiple students on empirical asset pricing and chairs thesis juries at ESSEC. He has organized numerous conferences, including workshops on Bayesian econometrics and empirical finance.

• Machine Learning
• Statistical Inference
• Bayesian Methods
• +۳ مورد دیگر

Kamélia DAUDEL is an Assistant Professor at the Department of Information Systems, Data Analytics and Operations at ESSEC Business School (France). She holds a PhD in Applied Mathematics from Télécom Paris (2021), an MSc in Mathematical and Computational Finance from the University of Oxford (2018), and an Engineering Diploma from Télécom Paris (2018). Her research focuses on variational inference, Bayesian methods, and machine learning, particularly in optimizing gradient estimators and divergence minimization techniques. She has been awarded the First Prize of the 2022 Institut Polytechnique de Paris Best Thesis Award. Research Interests: Her work bridges theoretical statistics and applied machine learning, emphasizing methods like alpha-divergence minimization, importance weighted autoencoders, and gradient-based optimization. She explores asymptotic properties of variational inference algorithms and their applications in high-dimensional data analysis. Teaching: At ESSEC Business School, she teaches Statistical Inference and Business Statistics & Introduction to Analytics . Her courses integrate modern data analysis techniques with foundational statistical theory. Recent Contributions: Her publications include studies on combining alpha-divergence with importance sampling, monotonic optimization frameworks, and infinite-dimensional gradient methods. Ongoing work focuses on improving gradient estimators for complex Bayesian models.

Botond Szaboمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Bayesian Statistics
• +۳ مورد دیگر

Botond Szabo is a Professor of Statistics at the Department of Decision Sciences at Bocconi University. He is a Fellow of the Bocconi Institute for Data Science and Analytics (BIDSA) and a Faculty member of the Bayesian Learning Laboratory (BayesLab) . As Project Leader of the ERC Stg “BigBayesUQ” (2022–2027), he focuses on advancing Bayesian methodologies. He serves as an Associate Editor for the Annals of Statistics (2022–present) and Bayesian Analysis (2016–present). His work integrates statistical theory with data-driven applications, emphasizing Bayesian inference and computational statistics. Affiliations: Bocconi University, BIDSA, BayesLab Key Roles: ERC Project Leader, Editorial Roles in Top Journals His research bridges theoretical foundations and practical challenges in modern statistical learning, with a focus on scalable Bayesian methods for complex data structures.

Chris Oatesمشاهده پروفایل

استاد

• Computational Statistics
• Probabilistic Machine Learning
• Bayesian Inference
• +۳ مورد دیگر

Chris Oates leads a research team in Computational Statistics and Probabilistic Machine Learning at Newcastle University's School of Mathematics, Statistics and Physics. He serves on the Programme Committee of the ICMS (2025–2028) and as an Associate Editor of the SIAM/ASA Journal of Uncertainty Quantification (2025–2027). His work focuses on advancing Bayesian computation, Monte Carlo methods, and uncertainty quantification, supported by grants such as a £0.8M EPSRC award (2022–2026) for Stein Discrepancy methodologies. Research interests include robust Bayesian inference for intractable likelihoods, optimal thinning of MCMC outputs, and reinforcement learning for adaptive sampling. His team develops software like Stein Thinning (stein-thinning.org) and has published breakthroughs in probabilistic Richardson extrapolation and prediction-centric uncertainty quantification using MMD. Awarded the Guy Medal in Bronze (2024) and a Leverhulme Prize (2024), he has advised over a dozen PhD students and postdocs, including co-supervised projects at King’s College London and Imperial College. Current funding includes a Heilbronn Institute grant for next-generation extrapolation methods and a Turing Fellowship (2024–2026). Labs/Teams: Active in the Alan Turing Institute collaboration network, leading a multidisciplinary group spanning computational statistics, machine learning, and interdisciplinary applications in geospatial systems and cardiovascular data science.

Guillaume SANDOUمشاهده پروفایل

استاد

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

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

Radu Patrice Horaudمشاهده پروفایل

پژوهشگر

• Computer Vision
• Audio Signal Processing
• Robotics
• +۵ مورد دیگر

Radu Patrice Horaud is a Researcher at Inria Grenoble Rhône-Alpes and a member of the RobotLearn team at Laboratoire Jean Kuntzmann, Université Grenoble Alpes. He holds a PhD from 1981 and has been a director of research since 1998. His work focuses on computational vision, audio-visual fusion, robotics, and machine learning, with applications in human-robot interaction and multimodal perception. Notable achievements include coordinating European projects, receiving two ERC grants (Advanced and Proof of Concept), and founding the Perception team. His research spans over 200 publications and includes contributions to 3D sensors, embodied audition, and Bayesian methods in inverse problems. Education: PhD (1981), postdoctoral work at SRI International (1982-1984), and CNRS researcher (1984-1998). Research Interests: Audio-visual scene analysis, robotics, machine learning, and multimodal fusion. His work emphasizes integrating audio and visual data for robotics and healthcare applications, such as the SPRING project in gerontological healthcare. Key Awards: ERC Advanced Grant (2014-2019), ERC Proof of Concept (2018-2019). His lab has also secured funding for projects like Socially Pertinent Robots in Gerontological Healthcare (SPRING). Labs/Teams: Formerly led the Perception team (2007-2021), now part of RobotLearn. Active in the Multidisciplinary Institute in Artificial Intelligence (MIAI) since 2019.

• Tourism and Hospitality Marketing
• Fair Consumption
• Social Media Marketing
• +۳ مورد دیگر

Aikaterini MANTHIOU is a Professor of Marketing at NEOMA Business School, specializing in tourism and hospitality marketing, fair consumption, and social media strategies. She leads the Marketing Specialization for Apprentices and has extensive industry experience in hospitality, banking, and China-based investment projects. Her research focuses on digital transformation in tourism (e.g., metaverse applications), sustainable tourism practices, and consumer behavior in luxury and slow tourism contexts. Key research areas include: Regenerative tourism and consumer ethics Metaverse-driven service consumption Social media influence on brand equity Luxury industry sustainability Recent articles highlight innovative methodologies like Bayesian analysis and machine learning. MANTHIOU has edited special journal issues and serves as a peer reviewer. She collaborates internationally on projects such as the Monaco Symposium on Luxury and the Global Fashion Management Conference.

Zoltán Szabóمشاهده پروفایل

استاد

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

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

Loucas Pillaud-Vivienمشاهده پروفایل

پژوهشگر

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

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

Nicolas VAYATISمشاهده پروفایل

استاد

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

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

Julien Chiquetمشاهده پروفایل

مدرس پاره‌وقت

• Statistics
• Machine Learning
• Sparse Methods
• +۱۰ مورد دیگر

Julien Chiquet is a researcher and part-time lecturer at École Polytechnique (2020-2024), and the head of the UMR MIA Paris-Saclay research unit. He is affiliated with AgroParisTech on the Paris-Saclay campus and leads the SOLsTIS team. He serves as the chief editor of the journal Computo , focusing on computational statistics and data science. Education: Habilitation (2015) Research Interests: His work bridges statistical methodology and life sciences, with a focus on developing and applying advanced statistical methods such as sparse models, multivariate analysis, and optimization techniques. Key areas include dimension reduction in high-dimensional count data, probabilistic graphical models, and applications in ecology, genomics, and metagenomics. He emphasizes reproducible research practices, recently developing a Quarto template for HCERES reports integrating LaTeX and Pandoc. Recent Contributions: Recent work includes advancements in the Poisson lognormal model, stability selection via the sharp R package, and Python tools like pyPLNmodels for analyzing multivariate count data. His research often addresses challenges in ecological network inference, scRNA-seq analysis, and genomic prediction. Labs & Teams: Based at UMR MIA Paris-Saclay, he contributes to the SOLsTIS team, advancing computational methods for biological data analysis. The lab relocated to the new Palaiseau campus in 2022, fostering interdisciplinary collaborations.

• Cognitive Sciences
• Evolutionary Biology
• Linguistics
• +۳ مورد دیگر

Jean-Louis Dessalles is a Professor at the Institut Polytechnique de Paris, affiliated with the School of Computer Science and the Department of Cognitive Sciences. His research focuses on the intersection of cognitive science, evolutionary biology, and artificial intelligence, particularly the evolutionary origins of language, computational models of human cognition, and algorithmic information theory. He is a co-founder of the Simplicity Theory framework, which explains cognitive processes through principles of complexity minimization. Key contributions include books such as Why We Talk: The Evolutionary Origins of Language and Des Intelligences Très Artificielles , which explore language evolution and AI limitations. His work spans interdisciplinary areas like social signaling, causal reasoning in cyber-physical systems, and the philosophical implications of consciousness and responsibility. Recent research emphasizes applications of simplicity theory to AI, causal learning in smart environments, and the biological foundations of communication. Dessalles has pioneered methods for explainable AI in complex systems, focusing on decentralized architectures for smart homes and urban traffic management.