جست‌وجوی استاد و پژوهشگر

Martin Schäferمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Harmonic Analysis
• Approximation Theory
• +۳ مورد دیگر

Dr. Martin Schäfer is a Researcher at the Chair of Applied Analysis within the Faculty of Mathematics at Chemnitz University of Technology. His research focuses on advanced mathematical frameworks such as α-molecules, coorbit space theory, and approximation methods in harmonic analysis. He earned his Diploma in Mathematics from the University of Bonn (2012) and completed his PhD at the Technical University of Berlin (2018) with a thesis on α-molecules. His work bridges pure and applied mathematics, addressing challenges in signal processing, image analysis, and high-dimensional approximation. Key research interests include approximation theory, Fourier analysis, function spaces, and the development of adaptive representation systems like shearlets and curvelets. Schäfer has contributed to conferences such as the International Conference on Multivariate Approximation (2016) and the GAMM Annual Meeting (2014), presenting on topics like phase retrieval and sparse recovery. His publications explore theoretical foundations of wavelet systems and their applications in optimal function recovery and sampling problems. Notable achievements include establishing new upper bounds for sampling numbers and developing subsampling techniques for finite frames. While no specific awards are listed, his prolific publication record in top journals like Applied and Computational Harmonic Analysis and Journal of Fourier Analysis and Applications highlights his academic impact. Schäfer collaborates internationally, evidenced by his participation in workshops in Germany, Canada, and the USA.

Zheng Zhaoمشاهده پروفایل

استادیار

• Computational Statistics
• Machine Learning
• Generative Diffusion Models
• +۴ مورد دیگر

Zheng Zhao is an Assistant Professor in computational statistics and machine learning at Linköping University, leading a research team within the Division of Statistics and Machine Learning (STIMA) under the Department of Computer and Information Science (IDA). His work focuses on developing computationally and statistically efficient models for machine learning applications, particularly generative diffusion models and their use in signal/image processing. He also applies these methods to fields like astronomy (e.g., stellar magnetic field analysis) and climate science. His research team emphasizes practical applications of diffusion models and Bayesian methods, contributing to advancements in image restoration, sensor array analysis, and high-dimensional data modeling. Zhao collaborates with STIMA colleagues on interdisciplinary projects, leveraging statistical theory and computational techniques. Publications highlight his exploration of diffusion models, Bayesian neural networks, and probabilistic methods across domains like astrophysics and environmental science. His work bridges theory and application, addressing challenges in uncertainty quantification and overfitting mitigation. Zhao is actively involved in academic initiatives such as the international master's program in Statistics and Machine Learning (hosted by STIMA). His research has been applied in competitions like the NTIRE challenge, demonstrating real-world impact in image restoration.

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Katharina Prokschمشاهده پروفایل

پژوهشگر

• Statistics
• Biomedical Imaging
• Data Analysis
• +۴ مورد دیگر

Katharina Proksch is a researcher specializing in advanced statistical methodologies and their applications across diverse scientific domains. Her work focuses on regression models, simultaneous inference, and transforms, with notable contributions to nonparametric testing, inverse problems, and quantitative imaging techniques. She explores interdisciplinary applications in biomedical imaging, astrophysics, and biomechanical analysis, emphasizing rigorous statistical frameworks to ensure methodological robustness. Her research interests include developing statistical tools for super-resolution microscopy, fatigue detection through biomechanical data, and analyzing complex datasets from fluorescence microscopy and X-ray astronomy. Proksch's methodologies often address challenges in handling measurement errors, outliers, and multiscale data structures. Key contributions include a two-sample test using Wilcoxon rank sums, quantitative super-resolution microscopy techniques with statistical guarantees, and fatigue detection algorithms leveraging sequential testing. Her work bridges theoretical statistics with practical applications in health monitoring and astrophysical phenomena modeling.

Bodhisattva Senمشاهده پروفایل

استاد

• Nonparametric Statistics
• Large Sample Theory
• Shape-Constrained Estimation
• +۴ مورد دیگر

Bodhisattva Sen is a Professor of Statistics at Columbia University, New York. His research focuses on nonparametric statistics, large sample theory, optimal transportation, and statistical applications in astronomy. He completed his Ph.D. in Statistics at the University of Michigan (2008) and holds degrees from the Indian Statistical Institute, Kolkata (B.Stat., M.Stat.). His work spans shape-constrained estimation, bootstrap inference, and interdisciplinary projects in astronomy. Sen’s research emphasizes distribution-free testing, high-dimensional models, and computational methods. Education: Ph.D. in Statistics, University of Michigan, Ann Arbor (2008) M.Stat., Indian Statistical Institute, Kolkata B.Stat., Indian Statistical Institute, Kolkata Research Interests: Nonparametric function estimation Optimal transport applications in statistics Empirical Bayes and multiple testing High-dimensional statistical inference Statistical methods in astronomy Key Contributions: Developed multivariate distribution-free tests using optimal transport Advanced convex regression methods in multidimensions Contributed to nonparametric maximum likelihood estimation in mixture models Explored statistical applications in stellar abundance clustering His work bridges theoretical statistics with practical applications, emphasizing robust and computationally efficient methods. Sen has also contributed to methodological advancements in astronomy through statistical modeling of stellar data.

Jakob Zechمشاهده پروفایل

استاد

• Uncertainty Quantification
• High-Dimensional Approximation
• Sparse-Grid Techniques
• +۴ مورد دیگر

Jakob Zech is a Professor at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University since April 2020. Before this, he held postdoctoral positions at MIT (2019-2020) and ETH Zürich (2018-2019). He earned his PhD in Mathematics from ETH Zürich in 2018, focusing on Sparse-Grid Approximation of High-Dimensional Parametric PDEs, followed by a Master’s (2014) and Bachelor’s (2012) in Applied Mathematics from ETH Zürich and TU Wien, respectively. Research Interests : Zech’s work bridges Uncertainty Quantification (UQ), high-dimensional approximation, and computational mathematics. Key areas include sparse-grid techniques, neural networks, transport methods, Bayesian inverse problems, and the theoretical foundations of deep learning. His research emphasizes developing algorithms for stochastic modeling and analyzing their mathematical properties. Teaching : He has taught advanced courses such as High Dimensional Approximation, Theory of Deep Learning, and Numerical Methods for Bayesian Inverse Problems at Heidelberg University. He also served as a teaching assistant for numerous courses at ETH Zürich, covering numerical analysis, partial differential equations, and linear algebra. Publications : His recent work explores quantum computing applications in polynomial chaos expansions, statistical learning theory for neural operators, and multilevel optimization strategies. His articles reflect a strong focus on combining classical numerical methods with modern machine learning techniques. Labs/Teams : While no specific lab is mentioned, his research group is active in computational UQ and deep learning, collaborating internationally with institutions like MIT and ETH Zürich.

• Bayesian Statistics
• Environmental Science
• Climate Change
• +۵ مورد دیگر

Jonathan Rougier is a Visiting Professor at Imperial College London's Faculty of Natural Sciences (Department of Mathematics) with an honorary appointment at the University of Bristol. He specializes in statistics, focusing on applications in environmental science, climate change, and geohazards. His work bridges academia and industry, emphasizing probabilistic modeling, uncertainty quantification, and decision-support systems for natural hazards. Education & Affiliations: Honorary staff at Imperial's Mathematics and Statistics departments. Collaborates with institutions on global geophysical and climate projects. Research Interests: Bayesian methods, spatio-temporal modeling, volcanic eruption prediction, climate change impacts, and risk assessment. His research addresses challenges in environmental data analysis, including unreliable historical records and complex simulation outputs. Articles Trends: Recent work focuses on volcanic hazard modeling (e.g., Aso volcano likelihoods), Antarctic ice mass evolution, and Kalman filter applications in environmental monitoring. Themes include uncertainty quantification, multi-scale computational methods, and interdisciplinary data integration. Advising & Grants: No formal advisees listed. Contributions include methodological advancements for climate projections and hazard analysis, supported by collaborative research networks. Labs/Teams: Engages in global initiatives like the VOLCORE database and Antarctic mass balance studies, emphasizing interdisciplinary teams for geophysical and environmental challenges.

• AI Alignment
• Reinforcement Learning
• Optimal Transport
• +۴ مورد دیگر

Francesco Quinzan is a Researcher at the University of Oxford's Department of Computer Science. His work focuses on advancing AI alignment, causal machine learning, and combinatorial optimization with applications in medical imaging, reinforcement learning, and fair algorithm design. He leads the ELSA project under Prof. Marta Kwiatkowska's supervision. Research interests include: Safe AI development through causal representation learning and doubly robust methods Optimization techniques for submodular functions and evolutionary algorithms Counterfactual analysis for bias detection in medical AI systems Reinforcement learning frameworks incorporating human feedback Recent publications (2020-2025) demonstrate contributions to: Causal feature selection and invariant predictors Scalable optimization methods for large-scale problems Robustness in multi-agent systems and diffusion-based models Algorithmic fairness in constrained feature selection Current projects involve: ELSA: Developing explainable and safe AI systems Optimal transport applications for domain correction Causal discovery from temporal data streams

François Orieuxمشاهده پروفایل

استادیار

• Inverse Problems
• Bayesian Methods
• Signal Processing
• +۵ مورد دیگر

François Orieux is an Assistant Professor at the University Paris-Saclay, affiliated with the Laboratoire des Signaux et Systèmes (L2S) within CentraleSupélec. His research focuses on Bayesian methods for inverse problems, computational imaging, and signal processing, with applications in astronomy, microscopy, and embedded AI systems. He holds a PhD in Astrophysical Instrumentation (2009) and an HDR in Computational Imaging (2024). Key research areas include: Development of optimization algorithms for hyperspectral and multispectral fusion High-dimensional sampling techniques for large-scale data GPU-accelerated convolution methods for real-time processing Instrument modeling for radio astronomy and terahertz spectroscopy Recent work emphasizes hardware-aware AI implementations on embedded systems and radio interferometric imaging pipelines. He collaborates with institutions like the Institut d'Astrophysique de Paris and leads projects funded by ANR and EU initiatives. Teaching activities include computational imaging and signal processing courses at CentraleSupélec. His open-source contributions include the Q-MM Python toolbox for optimization algorithms.

Clémentine Prieurمشاهده پروفایل

استاد

• Uncertainty Quantification
• Sensitivity Analysis
• Model reduction
• +۶ مورد دیگر

Clémentine Prieur is a Professor at the University of Grenoble Alpes, affiliated with the Jean Kuntzmann Laboratory (LJK - CNRS / Inria / UGA - Grenoble INP-UGA) and the Inria AIRSEA Project Team. She holds significant leadership positions including Head of the Applied Mathematics specialty at the MSTII Doctoral School, Vice-President of the French Statistical Society, and President of the SAMO (Sensitivity Analysis of Model Output) board. Her educational background includes a Master's degree, teaching qualification, and mathematics thesis. She pursued her academic career after expressing interest in mathematics as early as sixth grade, eventually specializing in probability and statistics after initially being drawn to abstract mathematics. Prieur's research focuses on uncertainty quantification, sensitivity analysis, model and dimension reduction, robust inversion, multivariate risk analysis, and nonparametric estimation for dependent processes. Her work bridges theoretical mathematics with practical applications in climatology, health, energy, and environmental science. She has developed numerous methodologies for analyzing complex systems and extracting meaningful information from data. Her publications demonstrate consistent contributions to uncertainty quantification and sensitivity analysis, with recent work spanning epidemic modeling, climate science, renewable energy systems, and machine learning. Her research shows increasing interdisciplinary applications while maintaining strong mathematical foundations, particularly in developing computational methods for high-dimensional problems. Vice-President of the French Statistical Society President of SAMO (Sensitivity Analysis of Model Output) board Local coordinator of MATH-AmSud project SMILE Coordinator of Inria associate team UNQUESTIONABLE Member of CNRS thematic networks for Uncertainty Quantification and Earth and Energies Prieur actively supervises doctoral students and postdocs, guiding them from master's internships through thesis completion. Her research is supported through multiple national and international projects including CIROQUO (Research and Industry Consortium), MIAI chair BALTEEC, and various CNRS networks. She frequently travels internationally for research collaborations, with recent visits to institutions in Uruguay, Italy, and Chile. She leads the Inria AIRSEA project team and participates in several research groups focusing on uncertainty quantification, including the CNRS thematic network Quantification of Uncertainties RT2172 and the thematic network Earth and Energies RT2166. Her work with CIROQUO connects academic research with industrial applications in uncertainty quantification for expensive data.

Avetik Karagulyanمشاهده پروفایل

پژوهشگر

• Statistical Sampling
• Optimization
• Machine Learning
• +۳ مورد دیگر

Avetik Karagulyan is a CNRS Research Scientist at L2S (Laboratory of Signals and Systems) affiliated with CentraleSupélec in France. He holds a PhD from CREST (Center for Research in Economics and Statistics), Paris, under Prof. Arnak Dalalyan, and an MSc in Mathematics, Vision, Learning from ENS Paris-Saclay. His research focuses on statistical sampling methods, optimization, and their applications in machine learning, particularly federated learning and non-convex optimization. He has contributed to advancements in Langevin Monte Carlo algorithms, variance reduction techniques, and distributed optimization frameworks. Education: PhD, Statistics, CREST, Paris (2021) MSc, Mathematics, Vision, Learning, ENS Paris-Saclay (2018) BSc, Mathematics and Mechanics, Yerevan State University (2017) Research Interests: He investigates sampling algorithms (e.g., Langevin Monte Carlo), federated learning, non-convex optimization, and their theoretical guarantees. His work bridges stochastic processes, optimization theory, and machine learning applications. Labs/Teams: He is part of the L2S lab, contributing to groups like MODESTY (Modeling for Control of Dynamic Systems) and SYCOMORE (Robust Control of Complex Systems). His research aligns with transversal axes in energy and industry innovation at Paris-Saclay. Grants & Advising: No explicit grants or advisees listed, though his collaborative projects suggest involvement in funded research initiatives.

Dmitriy Bilykمشاهده پروفایل

استاد

• Harmonic Analysis
• Functional Analysis
• Discrepancy Theory
• +۲ مورد دیگر

Dmitriy Bilyk is a Professor in the School of Mathematics at the University of Minnesota, based in Vincent Hall. His research explores harmonic analysis, functional analysis, and discrepancy theory, with applications to geometric inequalities and optimization problems on spheres. Research focuses on: Energy minimization and measure discreteness on spheres Discrepancy theory in arbitrary dimensions Geometric inequalities and spherical optimization Publications address mathematical structures in discrepancy theory, energy optimization, and geometric inequalities, with recent emphasis on sphere packing and measure theory applications. No awards are documented in the provided text. Research is supported by NSF grants and the Simons Foundation Collaboration Grant.

Natalia Baileyمشاهده پروفایل

مدرس ارشد

• Cross-sectional dependence
• Factor models
• Spatial models
• +۴ مورد دیگر

Natalia Bailey is a Senior Lecturer at the Department of Econometrics and Business Statistics at Monash University. She holds a PhD from Queen Mary, University of London, and previously served as a Research Associate at the University of Cambridge. Before joining Monash in 2017, she was a Lecturer at Queen Mary, University of London, and worked in the private sector in London for eight years as an economist/strategist at institutions including Citigroup, Lehman Brothers, and Merrill Lynch. Her research focuses on cross-sectional dependence, factor models, and spatial models, with applications to finance, macroeconomics, and climate change impacts on agriculture. Bailey has contributed to projects like the 'Big Data Analytic Approach to Assessing Impacts of Harmful Temperatures on Wheat Crops in Northern Victoria,' where she collaborated with experts to model agricultural resilience under climate change. Her recent work includes analyzing US house prices using spatial models with heterogeneous coefficients and developing the RMT-based LM test for cross-sectional independence in panel data. Bailey’s research bridges theoretical econometrics with practical applications, addressing issues such as climate change, agricultural productivity, and economic forecasting.

• Machine Learning Theory
• Optimization
• Differential Geometry
• +۳ مورد دیگر

Hong Ye Tan is currently a Hedrick Assistant Adjunct Professor in Computational and Applied Mathematics at the University of California, Los Angeles (UCLA), hosted by Professor Stanley Osher. Previously, he completed his PhD at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics as a member of the Cambridge Image Analysis group and the Cantab Capital Institute for the Mathematics of Information, supervised by Professors Carola-Bibiane Schönlieb, Subhadip Mukherjee, and Junqi Tang with funding from GSK.ai. His educational trajectory is exceptional: admitted to the University of Hong Kong at age 11 in 2015 (youngest in recent history) and to Cambridge at age 13 for doctoral studies. He passed his PhD thesis with no corrections, focusing on provably convergent algorithms leveraging geometric structures in data. Tan's research centers on machine learning theory, specifically investigating why learning succeeds through interactions between problem structure, data distributions, optimizers, and network architectures. His work bridges differential geometry (manifold hypothesis, intrinsic complexity), optimization (convex learning-to-optimize, Plug-and-Play inverse problems), and sampling theory (noise-free MCMC methods). He develops theoretically grounded algorithms with practical applications in imaging and unsupervised learning, emphasizing provable convergence guarantees derived from classical mathematics. Analysis of his 13 recent publications reveals a cohesive research program connecting optimal transport theory, manifold learning, and regularization techniques. His work demonstrates how geometric insights enable efficient solutions for high-dimensional problems, particularly in image analysis where dimensionality effects transform from curse to blessing. Key themes include Wasserstein proximal methods, dataset distillation via quantization, and accelerating mirror descent through equivariance. His scientific recognition includes: Masason Foundation Fellowship GSK.ai PhD Fellowship Tan has secured research funding through the GSK.ai PhD studentship and operates within Professor Stanley Osher's group at UCLA. He maintains active collaborations from his Cambridge tenure, particularly with the Cambridge Image Analysis group. Notably, he handles 100% of coding and 98% of writing for first-author publications, actively encouraging code reuse by the community. His work continues to explore foundational questions in learning theory while developing practical tools for inverse problems and imaging science.

• Statistical Methodology
• Machine Learning
• Parametric Inference
• +۷ مورد دیگر

Claudia Di Caterina is an Associate Professor of Statistics at the University of Verona's Department of Economics. Her research focuses on statistical methodology, including likelihood asymptotics, pseudo-likelihoods, anytime-valid inference, and bias reduction techniques. She holds a PhD in Statistical Sciences from the University of Padova and has conducted research at University College London and the University of Toronto. She teaches courses such as Machine Learning for Economics and Statistics for Business Analysis across various programs. Her academic roles include membership in the Economics Department Council and involvement in committees like the Comitato per le Ricerche con Dati sull'Individuo (CORDI) and Commissione Paritetica Docenti-Studenti. She contributes to research laboratories like VELE (Verona Experimental Lab in Economics) and collaborates with centers such as CRIEP and CIDE. Her publications span topics in high-dimensional data analysis, confidence sequences, and causal inference. She actively engages in public events and third mission activities, including workshops on climate risk and cyber security survey methodologies.