جستجوی استاد راهنمای دانشگاهی

François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.

• Machine Learning
• Statistical Inference
• Computational Statistics
• +۴ مورد دیگر

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +۹ مورد دیگر

Iain Murray is Professor of Machine Learning and Inference at the School of Informatics, University of Edinburgh. His research focuses on developing flexible probabilistic models applicable across diverse domains including cosmology, neuroscience, perception, speech, sports, and text. Program Chair for ICLR (2018) Publications Chair for ICML (2017, 2018) Area Chair for AISTATS, ICLR, ICML, NeurIPS, and UAI Amazon Scholar (2018-2024), first appointed in Europe Murray's research interests center on probabilistic reasoning using machine learning, with specific expertise in density estimation and Markov chain Monte Carlo methods. His work spans theoretical foundations and practical applications, with significant contributions to neural autoregressive distribution estimation (NADE), real-valued NADE (RNADE), and pseudo-marginal slice sampling techniques. His research has enabled advances in flexible probabilistic modeling across multiple domains. His publications show consistent focus on advancing probabilistic modeling techniques, with recent work emphasizing neural autoregressive models, density estimation methods, and efficient sampling algorithms. The research trajectory demonstrates progression from foundational work on NADE to increasingly sophisticated deep learning approaches for density estimation and inference. Notable Paper Award for NADE work Amazon Scholar (2018-2024) Murray has supervised numerous PhD students who have gone on to prominent positions at Google DeepMind, NYU, stability.ai, and other leading institutions. His teaching responsibilities include the Machine Learning and Pattern Recognition course and project supervision. His research group focuses on developing tractable probabilistic models with applications across multiple scientific domains.

• Machine Learning
• Probabilistic Modeling
• Density Estimation
• +۳ مورد دیگر

Christophe Andrieu is a Professor in Statistics within the School of Mathematics at the University of Bristol. His research bridges theoretical probability, computational statistics, and applied mathematics, with significant contributions to Markov Chain Monte Carlo methodologies and Bayesian inference frameworks. He maintains active collaborations across engineering and data science domains. His educational background includes: M.A. from List.Natnl.Scis.App.Lyon Additional M.A. (institution unspecified) Ph.D. from Paris Andrieu's research focuses on Markov Chain Monte Carlo theory , where he develops convergence guarantees and efficiency bounds for complex samplers. His work extends to non-reversible MCMC algorithms , piecewise deterministic processes , and gradient-free optimization techniques. Recent publications demonstrate innovative approaches to state-space models and numerical integration, often addressing high-dimensional statistical challenges through stochastic approximation methods. His fingerprint reveals deep specialization in Markov chain convergence analysis and computational Bayesian statistics. His 15 most recent publications (2021-2025) exhibit consistent focus on theoretical foundations of Monte Carlo methods, particularly convergence analysis of Markov chains and novel sampler designs. Key trends include the application of weak Poincaré inequalities to pseudo-marginal MCMC, development of self-organizing state-space models, and exploration of hypocoercivity in piecewise deterministic processes. The work spans both theoretical advancements and practical implementations for engineering and statistical applications. Andrieu has secured significant research funding including: COmputational Statistical INference for Engineering and Security (COSINES) (2018-2023) New Approaches to Data Science (2018-2023) He has supervised 5 research students and maintains active collaborations in computational statistics and machine learning. His network shows strong connections with probability theory and engineering research groups.

• Markov Chain Monte Carlo
• Bayesian Statistics
• Computational Statistics
• +۳ مورد دیگر

Dr. Yunxiao Chen is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), where he co-leads a psychometric lab with Professor Irini Moustaki. Previously, he was an Assistant Professor at Emory University (2016–2018) and earned his PhD in Statistics from Columbia University (2016). His research focuses on developing statistical and computational methods for social data science, addressing challenges in high-dimensional data analysis, latent variable models, and educational assessment. Education: PhD in Statistics, Columbia University, 2016 Research Interests: High-dimensional factor models (matrices, tensors, counting processes) Dynamic behavioral data analysis Sequential decision theory in personalized learning Statistical inference for large-scale item response data Applications in education, psychology, and marketing Publications: Recent work includes advancements in factor analysis, change-point detection, and DIF statistical inference Key journals: Journal of the American Statistical Association , Psychometrika , Journal of Machine Learning Research Awards: 2024 Psychometrics Society Best Reviewer Award 2022 Early Career Award 2018 NCME Loyd Dissertation Award Advising & Grants: Accepts PhD students in statistical methodology Funded by National Academy of Education/Spencer Fellowship (2018–2020) and IEA R&D grants (2022–2023) Labs & Teams: Runs LSE’s psychometric lab focused on educational measurement Collaborates with interdisciplinary teams on machine learning applications

• Multivariate Statistics
• Social Statistics
• Latent Variable Models
• +۵ مورد دیگر

Joel Dyer is a Senior Research Fellow at the Oxford Institute for New Economic Thinking and a Senior Research Associate at the University of Oxford's Department of Computer Science. He holds a DPhil in computational statistics and machine learning from the University of Oxford’s Mathematical Institute. His research focuses on agent-based simulation models, likelihood-free parameter inference, and simulation-based planning under uncertainty. Affiliations: University of Oxford (Department of Computer Science), Oxford Institute for New Economic Thinking Education: DPhil in Computational Statistics and Machine Learning, Mathematical Institute, University of Oxford Research Interests: Agent-based modelling Bayesian inference and likelihood-free methods Monte Carlo simulation Simulation-based optimization and planning Research Contributions: His work bridges computational statistics and economic modeling, emphasizing scalable inference techniques for complex systems. Recent efforts include surrogate modeling for simulation optimization and path signatures for time-series analysis. Labs/Teams: Part of the Complexity Economics Programme at Oxford INET and collaborates with institutions like the Alan Turing Institute and Improbable.

• Artificial Intelligence
• Machine Learning
• Agent-based Modelling
• +۳ مورد دیگر

Professor George Streftaris is a faculty member at Heriot-Watt University within the Actuarial Mathematics and Statistics department under the School of Mathematical and Computer Sciences . His academic career spans over two decades, including roles as associate professor and lecturer at Heriot-Watt University (2004-2019) and post-doctoral positions at BioSS and Heriot-Watt (2001-2004). He serves on the Board of Examiners for the Institute and Faculty of Actuaries and acts as an external examiner for multiple institutions. Professional memberships include Fellow of the Royal Statistical Society , member of the International Society for Bayesian Analysis , and the Greek Statistical Institute . Education: PhD in Statistics (University of Edinburgh) MSc in Statistics and OR (University of Essex, Distinction) BSc in Statistics and Actuarial Science (University of Piraeus, Greece) Research Interests: Streftaris specializes in Bayesian stochastic modeling , inference, and assessment at the intersection of statistics, epidemiology, and actuarial science. His work addresses critical illness insurance, longevity risk, and health-related insurance through predictive modeling and statistical machine learning. Key themes include disease transmission dynamics, model diagnostics, and uncertainty quantification in epidemic systems. Collaborations extend to life and biomedical sciences. Recent Publications: Recent articles focus on COVID-19 pandemic impacts on breast cancer mortality using semi-Markov models, neural network approaches for admission rate prediction, and Bayesian modeling of epidemic systems. Notable projects involve machine learning for multi-asset strategies, model uncertainty in insurance pricing, and stochastic frameworks for disease spread. Research Projects: Centers of Actuarial Excellence (SOA, 2019-2023): Predictive modeling for medical morbidity risk SCOR Foundation of Science (2022-2024): Breast cancer life insurance impact ARC Project (IFoA, 2016-2022): Longevity and morbidity risk management The Data Lab (2017-2018): Machine learning for multi-asset strategies Advising: Supervises ongoing PhD students in Bayesian and neural network modeling in epidemiology, with completed students working on topics like critical illness insurance, disease transmission, and stochastic mortality. Collaborations include researchers in the UK, USA, and international institutions.

• Bayesian Statistics
• Epidemiology
• Actuarial Science
• +۳ مورد دیگر

Song Liu serves as Associate Professor in Data Sciences and AI within the School of Mathematics at the University of Bristol. His academic journey spans multiple continents with a BEng from Suzhou University, MSc from Bristol, and Doctor of Engineering from Tokyo Tech. Current research focuses integrate mathematical foundations with practical AI applications across engineering domains. His educational background demonstrates international expertise: BEng: Suzhou University MSc: University of Bristol Doctor of Engineering: Tokyo Tech Research centers on exponential family manifolds and graphical models , with significant contributions to score matching techniques for missing data and generative modeling. His work bridges theoretical statistics with real-world applications in structural health monitoring and power electronics, particularly through transfer learning frameworks for magnetic core loss prediction. Recent publications reveal increasing focus on Wasserstein gradient flows and differential parameter inference in high-dimensional spaces. Liu's publication trajectory shows consistent innovation in density estimation and generative modeling, with recent work (2023-2025) emphasizing practical implementations in engineering contexts. Key themes include score-based diffusion models, manifold learning applications, and novel approaches to divergence minimization using velocity fields and optimal transport theory. Award recognition includes: Outstanding Paper at ICML2025 3rd Place in MagNet Challenge 2023 (Outstanding Performance Award) Grant leadership includes the 2023-2024 project Using Machine Learning to Correct Probe Skew in High-frequency Electrical Loss Measurements as Co-Investigator, and the 2019 Joint Workshop Between JGI and ISM as Principal Investigator. His academic service extends to hosting international researchers like Ayaka Sakata (2023) and receiving competitive fellowships for boundary example simulation (2018-2020). While no formal lab structure is specified, his collaborative network spans electrical engineering (magnetic core loss projects) and structural analysis (offshore wind foundation monitoring).

• Machine Learning
• Artificial Intelligence
• Data Science
• +۵ مورد دیگر

Professor Christopher Nemeth of Lancaster University's School Of Mathematical Sciences is a leading researcher in computational statistics and probabilistic machine learning. His work focuses on Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC), Gaussian processes, and approximate Bayesian computation, with applications in environmental science, target tracking, and econometrics. He currently holds a UKRI Turing AI Acceleration Fellowship and leads the ProbAI research hub. Research Interests: Development of probabilistic AI algorithms for large-scale learning, state-space modeling, and intersections between sampling and optimization algorithms. Grants: £9M UKRI-EPSRC ProbAI hub (2024-2029), £1.1M Turing AI Acceleration Fellowship (2021-2026), and multiple NERC grants. Academic Roles: Turing University Academic Liaison (2023-present), Associate Editor for ACM Transactions on Probabilistic Machine Learning (2023-present), and leadership roles in the Royal Statistical Society. Supervision: Completed supervision of 7 PhD students with projects on scalable Gaussian processes, Monte Carlo methods, and network modeling.

• Computational Statistics
• Probabilistic Machine Learning
• Markov Chain Monte Carlo
• +۷ مورد دیگر

Dennis Prangle is an Associate Professor in Statistics at the University of Bristol, conducting research at the intersection of Bayesian statistics and machine learning. His academic profile demonstrates expertise in developing novel computational inference methods with applications across multiple scientific domains. Dr. Prangle's primary research interests include: Approximate inference methods such as simulation-based inference and variational techniques Likelihood-free inference through Approximate Bayesian Computation (ABC) Experimental design for high-dimensional problems Applications in population genetics, physics, ecology, and epidemiology Stochastic differential equations and composite likelihood approaches His publication record shows consistent methodological contributions with increasing integration of machine learning techniques. Recent work focuses on normalizing flows with flexible tails for improved density estimation, Bayesian emulation of complex systems, and optimal combination of composite likelihoods. His research bridges theoretical statistics with practical applications in financial modeling, infrastructure engineering, and fair classification algorithms. Dr. Prangle maintains an active academic blog where he discusses technical aspects of Bayesian statistics, experimental design, and computational methods, demonstrating his commitment to scholarly communication. His detailed posts on topics like Fisher information gain versus Shannon information gain in experimental design highlight his theoretical contributions to the field.

• Bayesian Statistics
• Machine Learning
• Approximate Inference Methods
• +۸ مورد دیگر

Professor Simon Burgess is a Professor of Economics at the School of Economics, University of Bristol . He is a Fellow of the British Academy and a Research Fellow at the Institute for the Study of Labor (IZA) . His academic career includes directing major research centers such as the Centre for Market and Public Organisation (CMPO) and the Centre for Understanding Behaviour Change (CUBeC) , with a focus on education policy and social equity. Education: MA (Cambridge), DPhil (Oxford) Research Interests : Burgess specializes in the economics of education, examining teacher effectiveness, pupil motivation, school accountability, and ethnic segregation in schools. He also explores broader topics like unemployment, poverty, and public sector incentives. Recent Publications emphasize school performance metrics, classroom time allocation, and gender gaps in STEM education. Key themes include Education Policy , Labour Market Dynamics , and Social Equity , with methodological strengths in experimental economics and statistical modeling. Scientific Awards : Fellow of the British Academy Research Fellow, IZA Advisory Roles : Burgess chairs the Somerset Challenge (2016-2017) and serves on multiple advisory boards, while also contributing to public policy discussions on education reform.

• Economics of Education
• Teacher Effectiveness
• Pupil Motivation
• +۷ مورد دیگر

Professor Dankmar Böhning holds a Chair in Medical Statistics at the University of Southampton, affiliated with the Southampton Statistical Sciences Research Institute (S3RI) as Deputy Director. His expertise spans mathematical sciences and medicine, with a focus on meta-analysis, capture-recapture methods in health/social sciences, count data analysis, and mixture models. Education: Bielefeld and Berlin universities (Mathematics/Social Sciences), PhD (1981) and Habilitation (1992) from Free University Berlin. Career highlights include roles as Chair in Statistics at University of Reading (2005–2011) and visiting positions at institutions in Vienna, Graz, PennState, Bangkok, and Manila. Research interests include applying statistical methods to healthcare challenges, including epidemiological studies and clinical trials. He leads projects like CLASP Cancer and capture-recapture for contact-tracing efficacy. Active in teaching statistical inference and epidemiology courses at undergraduate and postgraduate levels. Key achievements: Bualuang ASEAN Chair Professor Prize (2022), editorial roles (e.g., Biometrical Journal), and leadership in national/international research collaborations. His recent work focuses on pandemic modeling (Covid-19) and improving diagnostic testing strategies.

• Medical Statistics
• Meta-analysis
• Capture-Recapture Methods
• +۳ مورد دیگر

Professor Martin Haehnelt is affiliated with the University of Cambridge, where he serves as Professor of Cosmology and Astrophysics at the Institute of Astronomy. His research spans galaxy formation, supermassive black holes, reionization, and the intergalactic medium. Current roles: Professor of Cosmology and Astrophysics Affiliation: Institute of Astronomy, Faculty of Physics & Chemistry, University of Cambridge College: St John's College Research interests include: Galaxy formation and evolution Supermassive black hole dynamics Reionization processes Intergalactic medium (IGM) studies 21-cm cosmology and Lyman-alpha forest High-resolution spectrograph instrumentation (e.g., CODEX, ELT/ANDES) Recent publications focus on reionization timing, Lyman-alpha forest constraints, cosmic ray feedback, and simulations like Sherwood–Relics and SPHINX-MHD. Key keywords include cosmology, astrophysics, quantum mechanics, and data-driven models. Scientific awards include leading the ERC Advanced Grant 'The Emergence of Structure during the Epoch of Reionization.'

• Formation of galaxies
• Supermassive black holes
• Intergalactic medium
• +۴ مورد دیگر

Teo Deveney is a Researcher in the Department of Computer Science at the University of Bath, actively contributing to mathematical and computational research since completing his PhD in 2022. His work bridges theoretical mathematics with practical applications across medical imaging and climate science domains. His doctoral research focused on accelerating Bayesian inference using deep learning for physics-governed likelihoods, supervised by T. Shardlow and E. Mueller. Educational background centers on computational mathematics with strong foundations in differential equations and probabilistic modeling. Research interests prioritize mathematical frameworks for complex systems: Partial Differential Equations (100% fingerprint match), Bayesian Statistics (85%), and Stochastic Differential Equations (75%). Secondary emphases include Neural Networks (60%), Climate Modeling, and Medical Imaging applications. His methodology consistently integrates uncertainty quantification with deep learning architectures. Recent publications (2024-2025) reveal converging trends: applying score-based diffusion models to ODE-SDE gaps via Fokker-Planck equations, developing graph-adaptive finite element methods, and creating machine-learned climate parameterizations. Medical imaging work focuses on Bayesian MRI reconstruction with structured uncertainty distributions using autoencoders. No scientific awards documented in current records Collaborative networks include C. Budd, C. B. Schönlieb, and L. Kreusser across Bath's computational mathematics groups. Current projects involve physics-informed neural networks for atmospheric modeling and uncertainty-aware medical image reconstruction, leveraging the university's high-performance computing infrastructure.

• Partial Differential Equations
• Bayesian Statistics
• Ordinary Differential Equations
• +۷ مورد دیگر

Anestis Touloumis is a Principal Lecturer in the School of Architecture, Technology and Engineering at the University of Brighton, affiliated with the Computing and Mathematical Sciences Research Excellence Group. He holds a PhD in Statistics from the University of Florida and has held postdoctoral positions at the European Bioinformatics Institute and the University of Cambridge before joining Brighton in 2015. Education: PhD in Statistics, University of Florida (2006–2011) MSc in Applied Statistics, University of Piraeus (2003–2006) BSc in Mathematics, Aristotle University of Thessaloniki (1998–2003) His research centers on biostatistics, high-dimensional statistics, and statistical genetics, with a focus on developing methods for correlated responses, covariance matrix estimation, and hypothesis testing in high-dimensional settings. His work is applied in omics, health studies, and social sciences, and he actively disseminates methods through R packages on CRAN and Bioconductor. He teaches a range of statistical modules using R and SAS, emphasizing conceptual understanding and critical thinking. The recent research articles reflect strong trends in biostatistical methodology, particularly in high-dimensional inference, longitudinal and correlated data analysis, and statistical computing. There is a consistent emphasis on developing robust, open-source tools for real-world applications in healthcare and data science. Scientific Awards and Professional Service: Treasurer, British and Irish Region of the International Biometric Society (2020–present) Committee Member, British and Irish Region of the International Biometric Society (2019–present) External Examiner, MSc in Applied Statistics, Birkbeck University of London (2020–present) Member, International Biometric Society He has been active in research supervision and mentoring, with a stated interest in supervising postgraduate students in machine learning, categorical data, and high-dimensional statistics, often involving software development. He has contributed to research grants, notably as a Co-Investigator on the ERDF-funded Digital Research & Innovation Value Accelerator (DRIVA) project (2018–2021). He is involved in multiple research collaborations and has delivered invited talks and peer reviews for leading journals. His work bridges theoretical statistics and practical applications across disciplines.

• Biostatistics
• Categorical Data Analysis
• High-Dimensional Statistics
• +۱۳ مورد دیگر