جست‌وجوی استاد و پژوهشگر

Darfiana Nurمشاهده پروفایل

مدرس ارشد

• Nonlinear time series analysis
• Bayesian inference
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Dr Darfiana Nur is an Adjunct Senior Lecturer at the University of Western Australia (UWA) , affiliated with the School of Physics, Mathematics and Computing and the Department of Mathematics and Statistics . She has a rich academic background with experience at Flinders University and Curtin University, and a strong record in teaching and research. Education: BMath – University of Gadjahmada (UGM), Indonesia (1988) MSc (Research) – University of Western Australia (1993) PhD in Statistics – Curtin University of Technology (1999) Research Interests: Her research focuses on nonlinear time series modelling , particularly Smooth Threshold Autoregressive (STAR) and regime-switching volatility models. She applies Bayesian MCMC algorithms and Hidden Markov Models (HMMs) in genetics and bioinformatics, and explores deep learning for time series forecasting. Her work spans DNA sequence modelling , Bayesian inference , and machine learning applications in complex data environments. She has published extensively in top-tier journals and contributed to both theoretical and applied statistics. Scientific Contributions: She has authored 23 journal publications , 10 refereed conference papers , and 35 non-refereed conference papers . Her recent work includes advanced statistical modelling in GARCH, BEKK, and LASSO frameworks. Supervision: She has supervised 7 PhD students , 1 MMath(Research) , 9 Masters by Coursework , and 7 Honours students across UWA, Flinders University, and the University of Newcastle. Teaching: Her teaching portfolio includes courses in Data Science , Mathematical Statistics , Time Series Analysis , and Bayesian Inference , delivered at both undergraduate and postgraduate levels.

Kahadawala Coorayمشاهده پروفایل

استاد

• Modeling continuous statistical distributions
• Statistical inferences
• Actuarial science
• +۱ مورد دیگر

Kahadawala Cooray is a Professor at the Department of Statistics, Actuarial and Data Sciences within the College of Science and Engineering at the University of Nevada at Las Vegas. He holds a Ph.D. from the same institution (2008) and a B.Sc. from the University of Colombo, Sri Lanka (1994). Education : Ph.D. (2008, University of Nevada at Las Vegas), B.Sc. (1994, University of Colombo, Sri Lanka) Cooray specializes in modeling continuous statistical distributions and applying them to actuarial science and medical research. His work includes parametric analysis of renal failure data, Bayesian estimation of composite distributions, and extensions of the Weibull and Gumbel distributions for complex datasets. His research trends focus on creating flexible statistical models for heavy-tailed data, multivariate dependence structures, and applications in insurance risk analysis. Key contributions include generalized copulas, folded logistic distributions, and hybrid Weibull-Pareto models for survival analysis. Scientific Awards : First Ph.D. graduate in mathematical sciences in Nevada (2008) Wolzinger Family Research Scholarship (2008) Cooray teaches courses in statistics and actuarial science, and he is affiliated with the American Statistical Association and its Mid-Michigan Chapter. His collaborations include researchers like M. M. A. Ananda and S. Gunasekera.

• Applied Statistics
• Biostatistics
• Time Series Analysis
• +۳ مورد دیگر

Dr. Carolina Euan is a Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University, with affiliations to the Data Science Institute and STOR-i Centre for Doctoral Training. Her research focuses on time series analysis, spatio-temporal modeling, and their applications in environmental data science and brain data analysis. School of Mathematical Sciences Data Science Institute STOR-i Centre for Doctoral Training Biostatistics Research Group Centre of Excellence in Environmental Data Science Her work spans multiple disciplines: Environmental Data Science: Marine heatwaves, solar irradiance modeling, particle number size distribution Brain Data Analysis: Neural connectivity, brain signal clustering, coherence-based inference Statistical Methodology: Spatio-temporal extremes, spectral estimation, functional data analysis Recent publications demonstrate expertise in: 2025: Neural connectivity modeling, functional data analysis, particle distribution 2024: Virtual collaboration frameworks, precipitation regime modeling, spectral estimation 2023: Brain signal clustering, source apportionment, directional wave spectra Supervision includes: Kajal Dodhia: STOR-i (extreme sea temperatures) Jordan Hood: Bayesian modeling (COVID prevalence) Carla Pinkney: STOR-i

• Spatial Statistics
• Software Development
• Open Source Geospatial Software
• +۱ مورد دیگر

Barry Rowlingson is a Research Fellow at Lancaster University Medical School , affiliated with the Chicas Research Group and DSI-Health . He specializes in spatial statistics applied to disease epidemiology and geospatial software development . Teaches Geospatial Data module for MRes in Global Health Developed online course on Spatial Statistics in R with DataCamp Focuses on open-source geospatial tools for public health Research Interests: His work bridges spatial statistical methodology and infectious disease modeling , with applications in antimicrobial resistance mapping , wastewater-based pandemic surveillance , and health inequality analysis . Recent projects include modeling ESBL-producing bacteria in Malawi and developing spatio-temporal frameworks for COVID-19 wastewater monitoring . Scientific Contributions: Over 15 years, he has developed critical R packages like stpp for spatio-temporal analysis and rgdal for geospatial data abstraction. His 2023 publications address health workforce disparities and disease transmission dynamics using advanced statistical methods. Collaborations: Works with multidisciplinary teams across Lancaster Medical School , DataCamp , and SAVSNet Agile research projects. Supervises PhD student Charlotte Appleton in biostatistics.

Erik Johnsonمشاهده پروفایل

استاد

• Smart Structures
• Structural Vibration Control
• Controllable Damping Devices
• +۳ مورد دیگر

Erik Johnson is a Professor of Civil Engineering at an unspecified university, affiliated with the Sonny Astani Department of Civil and Environmental Engineering. He has held leadership roles such as Associate Chair, Interim Chair, and currently serves as Vice Dean for Academic Programs. His research focuses on smart structures, structural vibration control, and computationally-efficient simulation algorithms for dynamical systems, with applications in controllable damping devices and seismic mitigation. Education: B.S., M.S., Ph.D. in Aeronautical and Astronautical Engineering (University of Illinois at Urbana-Champaign), Graduate Certificate in Biblical Studies (Trinity Evangelical Divinity School) Professional Affiliations: Senior Member of AIAA; Member of ASCE and ASME; Chair of ASCE technical committees; Associate Editor, ASCE Journal of Engineering Mechanics His work spans disciplines including control theory, structural engineering, and computational methods. Articles highlight Bayesian approaches, inverse problems, and sensor placement optimization under uncertainty. Erik contributes to advancing seismic resilience and mechatronic systems for civil infrastructure. Scientific Awards: 2001 NSF CAREER Award, 2005 International Association for Structural Safety and Reliability Medal, 2016 University of Illinois Distinguished AE Alumnus Award

• Markov chain Monte Carlo
• Monte Carlo methods
• Bayesian computation
• +۱ مورد دیگر

Dootika Vats is an Associate Professor in the Department of Mathematics & Statistics at Indian Institute of Technology Kanpur (IIT Kanpur). She earned her PhD in Statistics from the University of Minnesota, Twin-Cities, and her research focuses on advancing Monte Carlo and Bayesian computational methods, especially Markov chain Monte Carlo diagnostics. Education: PhD, Statistics, University of Minnesota, Twin-Cities, Feb 2017 MS, Statistics, University of Minnesota, Twin-Cities, Nov 2016 MS, Statistics, Rutgers University, New Brunswick, May 2012 BA (honors), Mathematics, University of Delhi, Lady Shri Ram College, May 2010 Research Interests: Her work lies at the intersection of computational statistics and Bayesian inference, with core emphases on: Markov chain Monte Carlo (MCMC) methodology Monte Carlo variance estimation and output analysis Bayesian computation and diagnostics Geometric ergodicity and convergence rates of MCMC algorithms Recent Publications Trend: Across her recent articles and preprints, Dr. Vats has consistently tackled open problems in MCMC output analysis, introducing new diagnostics, optimal batch-size selection, and visualization tools that directly impact practical Bayesian computation. Her contributions bridge theoretical rigor—such as proving strong consistency of spectral variance estimators—with immediately applicable software and graphical methods. Awards & Honors: Director’s Award, University of Minnesota School of Statistics, 2016 Graduate Research Partnership Program Fellowship, Summer 2016 Louise T. Dosdall Fellowship for Women in STEM, 2016–2017 School of Statistics Alumni Fellowship, 2015–2016 Martin–Buehler Fellowship in Statistics, Fall 2015 Bernard W. Lindgren Graduate Student Teaching Award, Spring 2014 Lynn Lin Fellowship in Statistics, Summer 2014 Teaching & Mentoring: At IIT Kanpur she continues to teach and mentor within the statistics curriculum. Earlier, at the University of Minnesota, she served as Instructor for STAT 3011 and as a teaching assistant across multiple undergraduate and graduate courses; at Rutgers University she was a part-time lecturer in calculus and pre-calculus. Labs & Collaboration: While no specific lab is named, her research is computational and collaborative; she has worked with James M. Flegal, Galin L. Jones, and other leading MCMC methodologists, and her Google Summer of Code participation demonstrates engagement with the open-source statistics community.

• Bayesian Statistics
• Bayesian Machine Learning
• Environmental Science
• +۸ مورد دیگر

Julyan Arbel is an Associate Researcher (chargé de recherche) at Inria Grenoble in the Statify team and a member of Laboratoire Jean Kuntzmann at Université Grenoble Alpes. He holds a PhD in Applied Mathematics from Université Paris-Dauphine and graduated from École Polytechnique and ENSAE. He defended his Habilitation à Diriger des Recherches (HDR) in 2019. Research Interests His work spans Bayesian Statistics (approximations, computation, nonparametrics, extreme value theory, objective Bayes) and Bayesian Machine Learning (Bayesian neural networks, variational inference), with applications in environmental science and neuroscience. Key focus areas include: Statistical methods for ecological modeling and species distributions Integration of biotic interactions in predictive frameworks Bayesian approaches to machine learning challenges Publications Overview Recent articles (2022-2025) demonstrate a strong focus on statistical ecology, including species distribution modeling, biotic interactions, community ecology, and biodiversity conservation. Methodological innovations center on joint species distribution models and Bayesian computational techniques applied to environmental datasets. Academic Service Associate Editor: Statistics and Computing (2025–present) Associate Editor: Bayesian Analysis (2019–present) Associate Editor: Australian and New Zealand Journal of Statistics (2019–present) Associate Editor: Statistics & Probability Letters (2019–present) Associate Editor: Statistical Methods & Applications (2024–present) Former Associate Editor: Computational Statistics & Data Analysis (2020–2023) Students Supervised PhD students: Giovanni Poggiato Daria Bystrova Minh Tri Lê Théo Moins Teams Statify Research Team, Inria Grenoble Laboratoire Jean Kuntzmann, Université Grenoble Alpes

Tâm Le Minhمشاهده پروفایل

پژوهشگر

• Statistics
• Ecological Networks
• Nonparametric Statistics
• +۲ مورد دیگر

Tâm Le Minh is a postdoctoral researcher in statistics at Inria Grenoble Rhône-Alpes, focusing on model-based statistical methods and nonparametric models with latent variables. His work bridges statistical theory with applications in life sciences, particularly ecological network analysis. PhD in Applied Mathematics (2023), Université Paris-Saclay Master in Applied Mathematics (2020), Institut Polytechnique de Paris Background in aeronautics and software engineering His research spans U-statistics, row-column exchangeable matrices, and Bayesian nonparametric approaches, with applications to ecological networks and high-throughput sequencing data. Recent publications emphasize bipartite network analysis and variational annealing for optimization. He has presented at major conferences including the International Conference on Bayesian Nonparametrics, Bernoulli-IMS World Congress, and ISBA World Meeting. His work appears in journals like ESAIM: Probability and Statistics and the Electronic Journal of Statistics.

• Bayesian Statistics
• Expert Knowledge Elicitation
• Robust Analyses
• +۳ مورد دیگر

Professor John Paul Gosling is an applied statistician affiliated with the Department of Mathematical Sciences at Durham University . With a background in industry, academia, and the civil service, his work bridges statistical theory and real-world applications, particularly in criminal justice, toxicology, and climate science. Education: MMath in Mathematics (University of York, 1998–2002), PhD in Statistics (University of Sheffield, 2002–2005) Current Role: Professor in Statistics, focusing on human interaction with statistical information His research interests include: Bayesian Statistics : Developing models for complex systems Expert Knowledge Elicitation : Structuring expert input for risk assessment Uncertainty Quantification : Analyzing and communicating uncertainty in data Machine Learning Integration : Combining statistical inference with AI for decision-making Key trends from his recent articles include: Application of Voronoi Tessellations in Bayesian regression and classification Development of emulation techniques for computationally intensive models Focus on legal and public health domains Integration of copula methods for dependent variables He actively supervises PhD students in Bayesian theory and machine learning interfaces and contributes to Statistics research at Durham University.

• Machine Learning
• Artificial Intelligence
• Bayesian Statistics
• +۵ مورد دیگر

Dr Cuong Nguyen is a Lecturer in the Department of Mathematical Sciences at Durham University, specializing in Machine Learning, Artificial Intelligence, and Statistics. His research bridges theoretical foundations with practical applications, with particular expertise in Bayesian methods, transfer learning, and multimodal systems. His educational background includes a PhD in Computer Science or a related field (specific institution not mentioned in provided data), with research focusing on machine learning theory and applications. Nguyen has established himself as a researcher with publications spanning top conferences including NeurIPS, UAI, and ACM Web Conference. Research Interests: Nguyen's work centers on lifelong learning systems that overcome catastrophic forgetting, transferability estimation between tasks, and multimodal learning applications. His research integrates Bayesian principles with deep learning to create more robust and adaptable AI systems. Recent Trends: Analysis of his 15 most recent publications reveals a strong focus on practical applications of theoretical machine learning concepts, particularly in security (CAPTCHA systems), real-world problem solving (fake advertisement detection), and fundamental learning theory (transferability metrics). Dr Nguyen has made significant contributions to understanding the theoretical underpinnings of transfer learning and continual learning, with his work on LEEP providing a practical metric for transferability estimation. His research on CAPTCHA systems demonstrates both theoretical rigor and practical security implications. Advising: While specific students aren't listed in the provided data, his publications show collaborations with researchers across institutions, suggesting active supervision of PhD and Master's students. Research Groups: He is affiliated with the Statistics research center within Durham's Department of Mathematical Sciences, contributing to the university's strength in mathematical and computational research.

Rachel Ryskinمشاهده پروفایل

استادیار

• Language comprehension
• Language production
• Learning and memory
• +۷ مورد دیگر

Rachel Ryskin is an Assistant Professor at the Department of Cognitive & Information Sciences, University of California, Merced. She leads the Language, Interaction, & Cognition (LInC) lab, focusing on real-time language comprehension, predictive processing, and cognitive mechanisms in communication. Her research integrates eye-tracking, EEG, computational modeling, and neuropsychological studies. Current affiliation: Assistant Professor at UC Merced Previous roles: Postdoc at MIT (Brain & Cognitive Sciences) and Boston University (Speech, Language & Hearing Science) Education: Ph.D. in Cognitive Psychology (University of Illinois Urbana-Champaign); B.A. in Cognitive Science (Northwestern University) Her work examines how humans use visuospatial perspective, speaker knowledge, and language statistics to resolve ambiguities during communication. She investigates lifelong learning mechanisms enabling adaptive inference in changing environments, with applications to aphasia and language acquisition research. Recent publications highlight her expertise in noisy-channel modeling, cross-situational learning, and lifespan language adaptation. Articles span topics including ERP studies, Bayesian inference in comprehension, and computational approaches to pragmatic reasoning. No scientific awards or formal advisees are listed in available materials.

• Statistics
• Categorical Data Analysis
• Survey Methodology
• +۳ مورد دیگر

Thomas Suesse is a Senior Lecturer at the School of Mathematics and Applied Statistics, University of Wollongong, with a career spanning institutions in Germany, New Zealand, and Australia. His academic journey includes a M.Sc. in Mathematics from Friedrich-Schiller-University Jena (2003) and a PhD in Statistics from Victoria University of Wellington (2008). Education: M.Sc. in Mathematics (FSU Jena, 2003), PhD in Statistics (VUW, 2008) His research focuses on Statistics, particularly Categorical Data Analysis, Social Network Modeling, and Spatial Statistics. Recent work explores variational Bayes inference for spatial autoregressive models, environmental extreme value detection algorithms, and the impact of missing data in statistical models. His publications span topics in public health, environmental science, and educational methodology. Key trends in his 15 most recent articles (2021-2025) include advancements in spatial statistics, applications of social network analysis to finance, and educational research comparing laboratory teaching objectives. He has also contributed to understanding pandemic effects on child health and activity patterns. Scientific Awards: 2008 VUW PhD thesis submission award; 2005-2008 VUW Postgraduate Scholarship. Thomas co-supervises PhD students on topics like spatial autoregressive modeling with missing data and variance estimation in mixture models. He has secured internal grants for computational infrastructure upgrades and studies on child movement behaviors. His professional affiliations include the Statistical Society of Australia.

• Financial Econometrics
• Time Series Analysis
• Volatility Modeling
• +۴ مورد دیگر

Jeroen ROMBOUTS is a Professor at ESSEC Business School (France) and holds the Full Professor position of the Accenture Strategic Business Analytics Chair since 2017. He joined ESSEC in 2013, previously serving as Associate Professor at HEC Montreal (2004–2012). His research focuses on financial econometrics, volatility modeling, and machine learning applications in financial markets. He holds a Ph.D. in Econometrics from the Catholic University of Louvain (2004) and has held visiting professorships at numerous institutions, including the University of Melbourne, Aarhus University, and Tilburg University. Education: PhD in Econometrics (2004), Catholic University of Louvain; Master's degrees in Statistics (2001), Econometrics (2000), and Economics (1999), all from the same institution. He is also a Researcher at the Finance and Insurance Lab (CREST) since 2014 and serves on editorial boards of journals like Quantitative Finance and International Journal of Forecasting . Research Interests: His work emphasizes volatility modeling, time series analysis, and applications of machine learning to forecast financial markets. Key areas include GARCH models, structural breaks, and cross-temporal forecasting for digital platforms. He has published extensively in top journals such as Journal of Econometrics and International Journal of Forecasting . Articles Overview: Recent contributions include novel methods for cross-temporal forecast reconciliation using machine learning and sparse change-point VAR models. His work bridges econometric theory with practical applications in asset pricing and risk management. Awards: Recipient of the 2024 Risk-Shift award in France. His research has been recognized for advancing methodologies in volatility modeling and financial econometrics. Advising & Grants: While no specific grants are listed, his roles as a researcher and editor highlight significant contributions to the academic community. He advises on policy and industry applications of his models through consulting roles in financial econometrics and macroeconomic forecasting. Labs & Teams: Affiliated with the Finance and Insurance Lab (CREST) and leads the Information Systems, Data Analytics, and Operations department at ESSEC. Collaborates with global institutions on projects involving high-frequency data and platform economics.

Jerry Brunnerمشاهده پروفایل

استاد

• Statistical Models
• Measurement Error
• Structural Equation Models
• +۳ مورد دیگر

Jerry Brunner is a retired Professor in the Department of Statistical Sciences at the University of Toronto, where he served from 1989 until his retirement on July 1, 2024. He holds a Ph.D. in Statistics (State University of New York, 1989) and an earlier Ph.D. in Social Psychology (University of Chicago, 1978). His research focuses on statistical models incorporating measurement error, parameter identifiability, and structural equation models. Notable contributions include work on z-curve methodology for estimating replicability and applications of Groebner basis techniques in statistical modeling. He developed open-source educational resources, including the Structural Equation Models: An Open Textbook project and contributed to SageMath workshops for statistical computation. His teaching and outreach efforts span advanced topics like Bayesian classification and mixed models analysis. Publications highlight methodological innovations in meta-analysis, error handling in regression, and algebraic approaches to statistical modeling. Though retired, he maintains involvement in academic activities and occasionally checks email.

Petros Dellaportasمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Financial Econometrics
• +۷ مورد دیگر

Petros Dellaportas holds dual appointments as a Professor of Statistical Science at University College London (UCL) and a Professor of Statistics at the Athens University of Economics and Business (AUEB). His research focuses on Bayesian statistics, machine learning, financial econometrics, and dynamic pricing. He leads projects on topics such as Poisson processes for cybersecurity, reservoir computing for macroeconomic forecasting, and probabilistic fault detection in wind parks. His recent publications emphasize advancements in Bayesian methods, variational autoencoders, and spatio-temporal point processes. Dellaportas has supervised over 20 PhD students, contributing to areas like stochastic volatility models and inverse reinforcement learning. He co-founded Thales and Friends, an organization bridging mathematics and cultural activities, and organizes the Greek Stochastics workshop series on topics ranging from causal learning to computational statistics. Key projects include anomaly detection in VAT networks and scalable Gaussian process models. His work often integrates statistical theory with applications in finance, sports analytics, and environmental science. Dellaportas maintains active collaborations with institutions globally, advancing interdisciplinary research and methodological innovations in statistical science.