جستجوی استاد راهنمای دانشگاهی

Michael U. Gutmannمشاهده پروفایل

مدرس ارشد

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

David Frazierمشاهده پروفایل

استاد

• Simulation-based inference
• Financial Econometrics
• Nonparametric and semiparametric econometric modeling
• +۳ مورد دیگر

David Frazier is a Professor in the Department of Econometrics & Business Statistics at Monash University, specializing in simulation-based inference, financial econometrics, and nonparametric/semiparametric modeling. He teaches ETC 1010: Data Modeling and Computing. His research focuses on robust statistical methods, Bayesian computation, and model misspecification. Key projects include 'Consequences of Model Misspecification in Approximate Bayesian Computation' (2020-2025) and 'Loss-based Bayesian Prediction' (2020-2025). Recent work addresses forecasting in misspecified models, weak identification in econometric frameworks, and robust variational Bayes techniques. His contributions align with UN Sustainable Development Goals related to economic and environmental sustainability. Projects: 4 active/funded projects with ARC, Brown University, and international collaborators. Publications: Over 37 peer-reviewed articles in journals like the Journal of the American Statistical Association and Journal of Econometrics. Research interests include advancing Bayesian methodologies for complex models, with applications in asset pricing and economic forecasting. His work emphasizes reliability in statistical inference under model uncertainty and computational efficiency.

• Bayesian Statistics
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Yanan Fan is a Senior Principal Research Scientist at CSIRO's Data61 and an Adjunct Professor of Statistics at the University of New South Wales (UNSW). His research focuses on Bayesian models, computational methods for real-world problems, and interdisciplinary applications in fields like medical imaging, cosmology, and climate science. He holds a PhD in Statistics from the University of Bristol and has over 20 years of academic experience at UNSW's School of Mathematics and Statistics. Education: PhD in Statistics, University of Bristol, UK Undergraduate Degree in Mathematics, University of Melbourne Research Interests: Fan develops Bayesian semiparametric models, approximate Bayesian computation (ABC), and scalable computational methods for medical imaging (e.g., PET), cosmology, and climate modeling. He also investigates gender bias in educational evaluations and leads initiatives like the Data4Good stream of UDASH. His work emphasizes practical problem-solving through advanced statistical techniques. Leadership & Contributions: As Team Leader of Bayesian Computational Methods and Applications at Data61, he drives innovation in statistical methodologies. He has served on the Scientific Committee of MATRIX research institute and as an Associate Editor for major statistical journals. His projects include probabilistic climate projections and bias analysis in student evaluations. Labs & Groups: Active member of the StatML Group and leader of the Bayesian Computational Methods team, focusing on integrating machine learning and statistical computing.

• Evolutionary Biology
• Statistical Genetics
• Computational Biology
• +۳ مورد دیگر

Paul Marjoram is a Research Professor at the University of Southern California in the Department of Population and Public Health Sciences. His research focuses on probabilistic and statistical methods for modeling evolutionary processes, including population dynamics and cancer progression. He is a pioneer in Approximate Bayesian Computation (ABC) and computational genetics. Research Interests Development of Bayesian statistical methods for evolutionary and cancer genomics Computational modeling of gene regulatory networks Epigenetic conservation and mutational signatures analysis Publications His work spans hierarchical Bayesian models, MCMC algorithms, and ABC frameworks applied to diverse biological systems, from Drosophila development to human cancer genetics. Software Contributions Methcon5 for DNA methylation analysis fmcmc (Friendly MCMC Framework) slurmR for HPC workflows

• Bayesian Statistics
• Machine Learning
• Approximate Inference Methods
• +۸ 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.

Massimiliano Tamborrinoمشاهده پروفایل

دانشیار

• Stochastic Processes
• Approximate Bayesian Computation (ABC)
• Parallel-in-Time Numerical Methods
• +۴ مورد دیگر

Massimiliano Tamborrino is an Associate Professor in the Department of Statistics at the University of Warwick since August 2024. He holds a WIHEA Fellowship (2023-2026) and has organized the One World ABC Seminar (2020–present), focusing on approximate Bayesian computation (ABC) and simulation-based inference. He co-organizes the BioInference conference series, which explores mathematical modeling in biological systems. His research integrates stochastic processes, numerical methods, and statistical inference, with applications in neuroscience, biology, and parallel-in-time algorithms. Education: PhD in Probability Theory and Statistics from the University of Copenhagen (2013), supervised by Prof. Susanne Ditlevsen. Previously held roles include University Research Assistant at JKU Linz (2014–2019) and Postdoc at the University of Copenhagen (2012–2014). Research interests include stochastic processes (diffusions, point processes), parallel-in-time numerical schemes (PinT), and ABC methods. His work bridges stochastic numerics with computational statistics, particularly in neuroscience and biological systems. He has developed R packages for exact simulation of non-Gaussian processes (e.g., shot noise, OU processes). Awards: WIHEA Fellow (2023–2026). Active in grants, including leadership of the EPSRC-funded project on AI-informed decision-making using Decision Field Theory. Teaching responsibilities include ST232/ST233: Introduction to Mathematical Statistics for undergraduate students.

Nitin Williamsمشاهده پروفایل

استاد مهمان

• Neuroscience
• Aging
• Functional Connectivity
• +۶ مورد دیگر

Nitin Williams is a Visiting Professor in the Department of Neuroscience and Biomedical Engineering. His research focuses on brain connectivity, aging, and neuroimaging techniques like MEG and fMRI. He has contributed to understanding functional connectivity dynamics, age-related neural changes, and computational models of brain networks. Recent work includes studies on TMS-evoked potentials, phase synchronization in resting-state networks, and discrete Ricci curvatures to analyze brain networks. He has collaborated internationally, including a visiting research stint at the Institute of Mathematical Sciences in India. Williams is an active member of academic communities, serving on editorial boards for journals like Frontiers in Computational Neuroscience and Network: Computation in Neural Systems . His datasets, including connectome analyses and biophysical models, highlight computational approaches to neuroscience challenges. Key research themes include: (1) Aging’s impact on neural plasticity and connectivity, (2) Biophysical modeling of phase synchronization, (3) Multimodal neuroimaging integration, and (4) Applications of graph theory in brain network analysis.

Jamie R. Oaksمشاهده پروفایل

دانشیار

• Evolutionary Biology
• Phylogenetics
• Phylogeography
• +۷ مورد دیگر

Jamie R. Oaks is an Associate Professor in the Department of Biological Sciences at Auburn University, where he leads the Phyletica Lab. His research focuses on understanding the processes that generate biodiversity through evolutionary and computational approaches. Ph.D. in Ecology and Evolutionary Biology – University of Kansas, 2013 M.S. in Biological Sciences – Louisiana State University, 2007 B.Sc. in Biology – University of Wisconsin Oshkosh, 2004 Dr. Oaks' research integrates theoretical, computational, and applied methods in phylogenetics, phylogeography, and population genetics . His lab develops Bayesian models and computational tools to infer evolutionary histories and test diversification models. The empirical focus is primarily on reptiles and amphibians , though the lab also works with systems ranging from viruses to plants. The recent publications highlight a strong trend in methodological development in Bayesian phylogenetics and biogeographic inference , with applications in conservation, speciation, and cryptic diversity. His work often involves fossil calibration, multilocus data, and model-based testing of historical diversification scenarios. Scientific Awards and Recognition: SSB GSRA award ($2900) – awarded to student Tanner from the Society of Systematic Biologists STRI Fellowship – awarded to student Claire for research on temperature tolerance evolution Dr. Oaks actively mentors graduate students, with several receiving competitive awards. His lab has secured research support through fellowships and grants, enabling field and genomic studies. The Phyletica Lab also contributes to open science through software development shared via GitHub and active science communication via a lab blog. The Phyletica Lab is a dynamic research group focused on exploring biodiversification . It combines model development, computational implementation, and empirical data analysis to address fundamental questions in evolutionary biology. The team collaborates widely and presents at major conferences such as MIC-Phy.

• Mathematical Biology
• Oncology
• Virotherapy
• +۶ مورد دیگر

Dr Adrianne Jenner is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT). She holds a PhD in Applied Mathematics from the University of Sydney (2015-2019), and conducted postdoctoral research at the Université de Montréal (2019-2021) and the University of Sydney. Her research focuses on mathematical biology, particularly oncology, virotherapy, immunology, and agent-based modelling. She is an active member of professional societies including the Society for Mathematical Biology and the Australian Mathematical Society. Her teaching spans abstract mathematical reasoning, calculus, and mathematical biology applications. Jenner's research emphasizes computational approaches to understand disease mechanisms, including cancer drug resistance, tumor microenvironment dynamics, and in silico clinical trials. She has contributed to high-impact studies on topics like glioblastoma immunotherapy, coral bleaching, and multiple sclerosis pathogenesis. Jenner actively engages in public outreach, including media appearances on ABC and science festivals. Her work bridges theoretical models with real-world applications, leveraging agent-based simulations and Bayesian methods to address challenges in healthcare and environmental science. Jenner collaborates extensively with interdisciplinary teams to translate mathematical insights into actionable strategies for disease treatment and ecological conservation.

Andrea Tancrediمشاهده پروفایل

استاد

• Bayesian Inference
• Bayesian Computational Methods
• Biostatistics
• +۹ مورد دیگر

Andrea Tancredi is a Professor at the Department of Methods and Models for Economy, Territory, and Finance, Sapienza University of Rome. His research focuses on Bayesian inference, computational statistics, and their applications in biostatistics, population size estimation, and health economics. He has coordinated multiple research projects funded by the University, including studies on record linkage, approximate Bayesian computation (ABC), and stochastic frontier models. Research Interests: Bayesian Inference Computational Methods (MCMC, ABC) Population Size Estimation Record Linkage and Data Integration Extreme Value Theory Biostatistics and Health Economics Teaching: He teaches courses such as Basic Statistics, Advanced Statistical Methods, and Extreme Value Theory at the Faculty of Economics and doctoral programs in Economics. Publications and Citations: With 47 publications (23 journal articles), his work spans Bayesian modeling for microbial diversity, climate-conflict risk forecasting, and multi-state health models. His research is cited 682 times on Google Scholar (h-index 12) and 401 times in Scopus (h-index 9).

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

Dr. Matt Moores is a Lecturer in Statistical Science at the University of Wollongong (UOW), affiliated with the School of Mathematics and Applied Statistics within the Faculty of Engineering and Information Sciences. He joined UOW in 2018 and holds a Docent title in Computational Statistics from LUT University (2021) and is an Elected Member of the International Statistical Institute (2022). He leads research in Bayesian computation, uncertainty quantification, and spatial statistics, with applications in energy infrastructure, environmental modeling, and spectroscopy. His academic roles include Academic Program Director for the Bachelor of Data Science & Analytics and Subject Coordinator for courses such as DSAA301 and MATH205. He co-leads a 2023 Learning & Teaching Innovation Grant focusing on work-integrated learning (WIL) and serves as an Associate Editor for Statistics & Computing . Moores is a Chief Investigator in the ARC Research Hub for Transforming Energy Infrastructure (TIDE), contributing to digital engineering advancements. Research interests span Bayesian inverse problems in oceanography, combining neural networks with Bayesian methods, sequential Monte Carlo, and approximate Bayesian computation (ABC). His publications reflect expertise in Gaussian processes, spatial statistics, and computational algorithms for intractable likelihoods. Awards include the 2023 L&TIG and recognition for contributions to statistical methodology. Teaching and supervision focus on integrating practical industry experiences with theoretical foundations, supported by grants advancing pedagogical innovation. Collaborations span academia and industry, emphasizing interdisciplinary problem-solving in energy, environment, and data science.

Wentao Liمشاهده پروفایل

مدرس

• Bayesian asymptotic theory
• Bayesian computation methods
• Monte Carlo methods
• +۳ مورد دیگر

Wentao Li is a Lecturer in Statistics at the University of Manchester (2021–present). Previously, he held positions as Assistant Professor at the University of Hong Kong (2019–2021), Lecturer at Newcastle University (2017–2018), and Senior Research Associate at Lancaster University (2013–2017). He earned a PhD in Statistics from Rutgers University (2013), an M.S. from Colorado State University (2008), and a B.S. from the University of Science and Technology of China (2006). His research focuses on Bayesian asymptotic theory, computational methods, Monte Carlo techniques, and simulation-based inference, with applications to financial time series and state-space models. Recent work emphasizes developing algorithms like approximate Bayesian computation (ABC) and sequential Monte Carlo for intractable likelihood models. He supervises a 2025 PhD studentship on Bayesian computation. Key contributions include studies on ABC convergence, asymptotic efficiency, and scalable methods for big data. His work bridges theoretical foundations and practical applications in econometrics, population genetics, and other fields.

Mitchell O’Sullivanمشاهده پروفایل

پژوهشگر

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

Mitchell O’Sullivan is a PhD student at Queensland University of Technology (QUT), affiliated with the School of Mathematical Sciences. His research focuses on novel sequential Monte Carlo methods for approximate Bayesian computation (ABC) and dimensionality reduction techniques. Prior to this, he worked as an analyst modeling payments data to detect financial crime before returning to QUT in 2019 to complete his honours in Mathematics. Education: Bachelor of Mathematics (Honours) – Queensland University of Technology (2019) Research interests include Bayesian statistics, likelihood-free inference, machine learning, and high-performance computing. He is particularly enthusiastic about advancing computational methods for complex implicit models and leveraging modern computer hardware. Advising and Grants: No advising or grants listed. Labs/Teams: Affiliated with the QUT Centre for Data Science.

• Spatial Statistics
• Statistical Consulting
• Biostatistics
• +۳ مورد دیگر

Ronald Barry is a Professor of Statistics at the University of Alaska Fairbanks, where he has maintained an active research and consulting practice since 1991. Holding a PhD from the University of California, Irvine, he operates from office CH 201C (contact: 907-474-7226, rpbarry@alaska.edu) and specializes in methodological innovations with cross-disciplinary applications. Education PhD in Statistics, University of California, Irvine (1991) Research Focus Barry's core expertise lies in spatial statistics, developing advanced techniques for geolocated data analysis in regions with irregular boundaries and holes. His work includes diffusion-based density estimators, nonparametric co-kriging, and lattice-based smoothing algorithms implemented through R packages. This methodological research extends to biostatistical applications in wildlife biology, nutrition, and fisheries, alongside chemometrics projects involving NMR spectroscopy for chemical mixture analysis and medical statistics research on sentinel node biopsy failures in melanoma using SEER cancer data. Publication Trends His recent publications (2015-2021) reveal three dominant trajectories: (1) continuous refinement of spatial modeling techniques for complex geometries, (2) expansion into biomedical and petroleum engineering applications through collaborative consulting, and (3) development of statistical software tools that bridge theoretical methodology with practical implementation. The interdisciplinary nature of his work is evident in publications spanning environmental statistics, reservoir engineering, and adolescent health studies. Scientific Awards No specific awards, fellowships, or honors were documented in the source material. Advising and Collaborations Barry actively mentors graduate students at UAF, with Bernard, Paryani, Canary, and Maier serving as primary authors on collaborative publications. His extensive consulting portfolio—covering experimental design, analysis, and reporting across wildlife biology, nutrition, and fisheries—suggests significant engagement with external research funding, though specific grants aren't detailed. Current projects indicate ongoing collaboration with medical researchers on melanoma diagnostics and petroleum engineers on reservoir modeling.

Stuart Barberمشاهده پروفایل

مدرس ارشد

• Wavelet Methods in Statistics
• Statistical Bioinformatics
• Applied Statistics
• +۵ مورد دیگر

Dr Stuart Barber is a Senior Lecturer in Statistics at the School of Mathematics, University of Leeds, affiliated with the Faculty of Engineering and Physical Sciences. His expertise spans wavelet methods, statistical bioinformatics, and applied statistics, with interdisciplinary applications in fields such as industrial tomography, spatial data analysis, and genomics. His research focuses on wavelet-based statistical techniques, Bayesian inference, and computational methods. Recent applications include clustering, phylogenetics, anaesthesiology, and copy-number genomic data segmentation. Research trends from his publications highlight advanced methodologies like Gaussian process modeling, Voronoi tessellations, and Approximate Bayesian Computation (ABC), alongside bioinformatics-driven studies on protein alignment and structure analysis. Responsibilities include serving as Head of the Department of Statistics. He actively supervises postgraduate researchers, including Fatih Gezer, and acknowledges support from the Simons Foundation. His work is associated with the Statistics research group, fostering collaborations and PhD opportunities in diverse statistical domains.