جست‌وجوی استاد و پژوهشگر

Michelle Mirandaمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Neuroimaging
• +۴ مورد دیگر

Michelle Miranda is an Assistant Professor in the Department of Mathematics and Statistics at the University of Victoria (UVic), Faculty of Science. She joined UVic in 2019. Previously, she conducted postdoctoral research at MD Anderson Cancer Center under Jeffrey S. Morris (2016–2019) and at the University of São Paulo’s Center for Neuromathematics under Antonio Galves (2014–2016), supported by a FAPESP scholarship. She earned her PhD in Statistics from the University of North Carolina at Chapel Hill (2014), advised by Joseph G. Ibrahim and Hongtu Zhu, with a focus on Bayesian analysis of neuroimaging data. She also holds a Fulbright Scholarship (2008). Her research emphasizes Bayesian functional regression models for high-dimensional neuroimaging data, including fMRI activation signatures and functional connectivity. She teaches courses such as STAT 260 (Probability/Statistics), STAT 453 (Design of Experiments), and STAT 558 (Advanced Experimental Design). Key research interests include Bayesian methods, neuroimaging analysis, and applications in medical diagnostics. Her work bridges statistical theory and clinical neuroscience, with recent focus on multi-subject brain activation patterns and imaging biomarkers. Her scientific awards include the FAPESP Postdoctoral Scholarship and Fulbright Scholarship. She actively contributes to academic service through teaching and advising, though specific student names are unlisted here. She maintains a lab within UVic’s Mathematics and Statistics department, focusing on interdisciplinary projects combining statistics and neuroscience. Her office is located in the David Turpin Building, A543.

K. Krishnamoorthyمشاهده پروفایل

استاد

• Multivariate Statistics
• Statistical Inference
• Censored Data Analysis
• +۵ مورد دیگر

K. Krishnamoorthy is a Professor of Statistics and holds the Philip and Jean Piccione Endowed Chair in Statistics at the University of Louisiana at Lafayette's Department of Mathematics. His expertise spans multivariate statistics, statistical tolerance regions, censored data analysis, and occupational exposure assessment. He has contributed extensively to methodologies for lognormal distributions, small sample inference, and calibration techniques. His research addresses challenges in environmental and occupational health data analysis, including exposure monitoring and statistical validation of sampling devices. Education: Ph.D. in Statistics from Indian Institute of Technology-Kanpur (1985). He has supervised over 25 Ph.D. students, many now in academic and industry roles. Notable contributions include the development of statistical tolerance regions, methodologies for data with non-detects, and software tools like StatCalc for distributional computations. Grants and Projects: Principal investigator on multiple NIOSH-funded projects (R01-OH03628 series) totaling over $2.6M, focusing on statistical methodologies for occupational exposure assessment. These include studies on lognormal distributions, equivalence testing of sampling devices, and imputation techniques for non-detect data. Publications: Over 200 peer-reviewed articles in journals like *Journal of Statistical Planning and Inference*, *Technometrics*, and *Annals of Occupational Hygiene*. Authored/co-authored three books, including *Handbook of Statistical Distributions with Applications* (CRC Press) and *Statistical Tolerance Regions* (Wiley). Labs/Teams: Leads the statistical methodologies group at UL Lafayette, collaborating with occupational hygienists and environmental scientists. Developed open-source Fortran/R programs for statistical inference in specialized distributions.

Di Gaoمشاهده پروفایل

استادیار

• Statistics
• Bayesian Inference
• Biostatistics
• +۳ مورد دیگر

Dr. Di Gao is an Assistant Professor of Statistics in the Department of Mathematics and Statistics at Sam Houston State University's College of Science & Engineering Technology. Her research focuses on Bayesian inference, biostatistics, dimension reduction, statistical learning with sparsity, and statistical consulting. Education includes a B.S./B.Ec. from Tianjin University of Finance and Economics, M.A. from University of Missouri-Columbia, and Ph.D. from North Dakota State University. Her publications demonstrate interdisciplinary work spanning statistical methodology development and applications in engineering, bioinformatics, education, and materials science. Recent research themes include fluid dynamics simulations, educational statistics, cancer biology, and nanoscale thermodynamics.

Ram C. Kafleمشاهده پروفایل

دانشیار

• Statistics
• Bayesian Analysis
• Epidemiology
• +۳ مورد دیگر

Dr. Ram C. Kafle is an Associate Professor of Statistics and Graduate Advisor for the MS in Data Science and Statistics program at Sam Houston State University. His research spans Bayesian analysis, change-point regression, cancer epidemiology, survival analysis, and risk assessment modeling. Education includes a B.S. and M.S. from Tribhuvan University (Nepal), M.S. from University of Akron, and Ph.D. from University of South Florida. His methodological work in statistical modeling has been applied to diverse domains including public health surveillance, COVID-19 forecasting, sports analytics, and environmental science. Recent publications demonstrate expertise in joinpoint regression, functional data analysis, and Bayesian modeling frameworks.

Ruijin Luمشاهده پروفایل

استادیار

• Biostatistics
• Alzheimer's Disease Research
• Neurodegenerative Disorders
• +۵ مورد دیگر

Ruijin Lu serves as Assistant Professor of Biostatistics at Washington University School of Medicine, affiliated with both the Institute for Informatics, Data Science and Biostatistics (I 2 DB) and the Center for Biostatistics and Data Science (CBDS). With 238 citations across 24 research publications spanning from 2017 to 2025, Dr. Lu maintains an active research program focused on statistical applications in neurodegenerative disease research. Dr. Lu's research interests center on biostatistical methods applied to Alzheimer's disease research, particularly in biomarker analysis for autosomal dominant Alzheimer's disease. Key research areas include: Statistical modeling of cerebrospinal fluid and plasma biomarkers Longitudinal analysis of neurodegenerative disease progression Development of sensitivity analysis methods for complex exposure interactions Comparative studies of tau pathology across different patient populations Integration of genetic and clinical data for dementia prediction Recent publications demonstrate significant contributions to high-impact journals including Nature Medicine , Nature Communications , and The Lancet Neurology , with multiple studies gaining substantial media attention (83 news outlets for one publication) and academic engagement (47 Mendeley readers). Dr. Lu actively collaborates with major research networks including the Dominantly Inherited Alzheimer Network and Alzheimer's Biomarker Consortium–Down Syndrome, working alongside prominent researchers such as Bateman, Morris, and Holtzman. Current research output shows accelerating productivity with 12 publications in 2024 and 2 in 2025, indicating an expanding research program with significant clinical implications for understanding and treating Alzheimer's disease.

Todd Kuffnerمشاهده پروفایل

دانشیار

• Statistical Inference
• Asymptotic Theory
• Bayesian Statistics
• +۷ مورد دیگر

Todd Kuffner is an Associate Professor in the Department of Mathematics and Statistics at Washington University in St. Louis , holding a Ph.D. in Mathematics from Imperial College London . His research bridges statistical theory , Bayesian asymptotics , and machine learning , focusing on rigorous inference after model selection. Education: Ph.D., Mathematics, Imperial College London M.Sc., Econometrics and Mathematical Economics, London School of Economics M.Sc., Economics, London School of Economics B.A., Economics, University of Michigan Kuffner's work investigates the validity, accuracy, and power of statistical procedures, with a focus on reconciling Bayesian, frequentist, and neo-Fisherian paradigms. His publications address higher-order asymptotics , post-selection inference , and resampling techniques for small-sample and high-dimensional settings. Key trends in his recent articles include: Bootstrap methods for high-dimensional and dependent data Bayesian approaches to volatility estimation in financial models Connections between machine learning and frequentist post-selection inference Formal Edgeworth and von Mises expansions for robust regression Empirical likelihood and model selection uncertainty Scientific contributions : Organizer of the Workshop on Higher-Order Asymptotics and Post-Selection Inference (WHOA-PSI) (2016-2020) Editorial roles at Harvard Data Science Review , Sankhya Series A , and Journal of the American Statistical Association Recipient of NSF grants for collaborative research His advising and organizational efforts emphasize collaborative research and innovative conference design , including interactive forums and workshops.

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.

• Statistics
• Experimental Design
• Bayesian Statistics
• +۴ مورد دیگر

Dr. Timothy Waite is a Lecturer in Statistics at the University of Manchester, specializing in experimental design and Bayesian statistics. His academic work focuses on developing innovative statistical methodologies for complex modeling scenarios with applications across various domains. Dr. Waite's research interests span multiple areas of statistics with a particular focus on: Bayesian statistics and inference Optimal experimental design Nonlinear and generalized linear models Design of experiments Monte Carlo methods and simulation Statistical modeling for prediction and inference Analysis of Dr. Waite's publication record reveals a strong focus on advancing experimental design methodologies, particularly in Bayesian frameworks. His work consistently addresses challenges in high-dimensional spaces and nonlinear models, with applications ranging from debt recovery modeling to general statistical inference. A notable trend in his research is the development of efficient computational methods for optimal design problems, with increasing emphasis on robustness and practical applicability in recent years. His publications in top statistical journals demonstrate both theoretical rigor and practical relevance. Dr. Waite serves as a Principal Investigator for the Statistical Advisory Unit (SAU), a collaborative research initiative at the University of Manchester involving multiple faculty members. While specific details about his supervised students are limited in the available information, the mention of 'Supervised Work (1)' suggests he has mentored at least one graduate student or research assistant. His research contributes to the UN Sustainable Development Goals through the University's Digital Futures initiative, indicating applications of his statistical methodologies to broader societal challenges.

Harlan Campbellمشاهده پروفایل

استاد مدعو

• Bayesian Statistics
• Meta-Analysis
• Causal Inference
• +۳ مورد دیگر

Harlan Campbell is an Adjunct Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on Bayesian statistics, causal inference, meta-analysis, and statistical methodology, with applications in medical and public health contexts. Campbell collaborates internationally, such as leading the Zika virus individual participant data consortium to study pregnancy outcomes. He has advised at least one student: Nathaniel Wu Dyrkton. His work integrates evidence synthesis, clinical trial analysis, and methodological innovation to address gaps in data reporting and analysis across disciplines. Education details are not explicitly provided, but his career reflects advanced training in statistical theory and applications. Research interests include developing robust statistical techniques for observational studies, addressing measurement error in meta-analyses, and improving methodologies for indirect treatment comparisons. He has contributed to frameworks for estimating infection fatality rates during the COVID-19 pandemic and advancing Bayesian approaches to evidence synthesis. Publications span topics like structural uncertainty in evidence synthesis, doubly robust estimators for clinical trials, and causal inference in longitudinal studies. His work emphasizes rigorous statistical methods to enhance reliability in medical and epidemiological research. Grants and collaborations are implied through his involvement in global consortia and methodological reviews but are not explicitly detailed in the provided text. Campbell maintains an active Google Scholar profile with extensive citation activity.

• Renewable Energy
• Energy Storage
• GIS Analysis
• +۳ مورد دیگر

Dr. Cheng Cheng is a Senior Research Officer at the Australian National University's School of Engineering. He holds a Bachelor of Engineering (Honours) and PhD from ANU. His research focuses on renewable energy integration, decarbonization pathways, energy system modeling, and GIS analysis. He specializes in optimizing off-river pumped hydro energy storage systems. Cheng leads multiple renewable energy projects funded by government and industry grants, and has developed methodologies for energy demand forecasting and least-cost optimization. His work bridges theoretical energy modeling with practical implementation through collaborative outreach activities. Education: B.Eng(Hons) & PhD (Australian National University) Research interests emphasize sustainable energy transitions, with particular attention to geospatial analysis for energy infrastructure planning. His recent projects include developing frameworks for integrating renewable generation with storage solutions. Over 150 publications span machine learning applications in energy systems, astrophysics, and bioinformatics. Awards include academic fellowships supporting his interdisciplinary work. Cheng collaborates with industry partners to translate research into policy and technology. He directs the ANU's Renewable Energy Integration Lab (RE100), advancing grid stability solutions through innovative storage technologies. His work has been recognized in special issues of journals focusing on robust machine learning applications.

Nigel Clarkeمشاهده پروفایل

استاد

• Theoretical Polymer Physics
• Soft Matter Physics
• Rheology
• +۳ مورد دیگر

Professor Nigel Clarke is a faculty member at the University of Sheffield, affiliated with the School of Mathematical and Physical Sciences. He holds the academic rank of Professor of Condensed Matter Physics Theory. His research focuses on theoretical polymer physics, soft matter physics, rheology, and neutron scattering techniques applied to polymer nanocomposites. Key interests include understanding microscale structures in nanocomposites, charge transport in organic photovoltaics, and the dynamics of polymer blends and interfaces. Professor Clarke has contributed extensively to the field through publications analyzing polymer dynamics, nanoparticle migration, and the application of machine learning in materials science. His work bridges theoretical modeling with experimental techniques like neutron scattering and SANS. He is a Fellow of the American Physical Society and serves as Chair of the IOP Polymer Physics Group, reflecting his leadership in polymer physics research. His research trends emphasize interdisciplinary approaches, combining computational modeling with experimental validation to study material properties and dynamics. Notable contributions include studies on carbon nanocomposite structures, polymer blend morphologies, and the role of nanoparticles in altering polymer behavior. Awards: Fellow of the American Physical Society Professional Roles: Chair of the IOP Polymer Physics Group, Member of EPSRC e-infrastructure Strategic Advisory Group His work often explores the interplay between structure and functionality in materials, with applications in energy materials, optoelectronics, and advanced composites.

Benoit Liquet-Weilandمشاهده پروفایل

استاد

• Biostatistics
• Survival Analysis
• Machine Learning
• +۱۰ مورد دیگر

Benoit Liquet-Weiland is a Professor at Macquarie University's School of Mathematical and Physical Sciences, affiliated with the Astrophysics and Space Technologies Research Centre and Data Horizons Research Centre. His research focuses on statistical methodologies applied to biomedical and environmental data, including multi-state models, survival analysis, Bayesian modeling, and machine learning. He teaches advanced courses in statistics such as Generalized Linear Models and The Mathematical Engineering of Deep Learning. Education: HDR (PhD Supervisor diploma) in Statistical Modelling and Biomedical Applications from University of Bordeaux (2009), and a PhD in Statistics from Université de Bordeaux (2002). Research Interests: Model selection and variable selection Survival analysis and multi-state models High-dimensional omics data analysis Machine learning applications in healthcare and ecology Statistical methods for precision medicine Recent work emphasizes interdisciplinary applications, including predicting ICU outcomes for COVID-19 patients, detecting pleiotropic effects in cancer datasets, and coral classification using satellite imagery. He leads or collaborates on projects like FightMND MCR Fellowship and computational analysis in brain tumor imaging. Recipient of the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Active in supervising PhD students in statistical methodologies and applications.

Yusha Liuمشاهده پروفایل

استادیار

• Biostatistics
• Genomics
• Cancer Research
• +۶ مورد دیگر

Dr. Yusha Liu is an Assistant Professor in the Department of Biostatistics at the UNC Gillings School of Global Public Health. She holds a PhD in Statistics from Rice University (2019), an MS in Biostatistics from Yale University (2014), and a BS in Biology from Peking University (2012). Her research focuses on developing statistical methods for analyzing large-scale genomic data, particularly in cancer research and biomarker-driven clinical trials. PhD: Rice University (2019) MS: Yale University (2014) BS: Peking University (2012) Her research interests include single-cell data modeling, omics data integration, Bayesian inference, functional data analysis, and quantile regression. She applies these methods to understand complex diseases such as cancer and develop targeted therapies. Awards include the Ralph Budd Best Engineering PhD Thesis Award (2020) and the Distinguished Student Paper Award (2020, ENAR). Her work bridges statistical innovation with biomedical applications, emphasizing scalable solutions for modern genomic datasets. Liu has contributed to adaptive clinical trial designs for biomarker analysis and pioneered methods for dissecting tumor heterogeneity using single-cell RNA-seq. Her research often addresses translational challenges in precision medicine.

Xianming Tanمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials
• Bayesian Methods
• +۷ مورد دیگر

Xianming Tan, PhD, is a Professor in the Department of Biostatistics and Research Associate Professor at the Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill. He holds a PhD in Statistics from Nankai University (2005), an MS in Economics from Huazhong University of Science and Technology (1998), and a BS in Mathematics from Beijing Normal University (1995). His research focuses on developing advanced statistical methods for clinical trials and public health, including innovative trial designs, Bayesian methods for drug safety assessment, and applications of AI in healthcare. He collaborates extensively with clinicians at Lineberger on cancer-related studies, particularly in pharmacovigilance, treatment optimization, and high-dimensional data analysis. Key research areas include finite mixture models for population heterogeneity, convolution regression, and interdisciplinary applications in oncology. His work bridges statistical theory and practical healthcare challenges, addressing issues like latent confounder control, treatment effect dynamics, and healthcare equity. He currently serves on the Department of Biostatistics Admissions Committee since 2023. His publications span top journals like Statistics in Medicine , Annals of Applied Statistics , and Systematic Reviews , with a focus on methodological advancements and translational research.

Ivan Svetunkovمشاهده پروفایل

مدرس ارشد

• Forecasting
• Statistical Modelling
• Marketing Analytics
• +۵ مورد دیگر

Ivan Svetunkov is a Senior Lecturer at Lancaster University's Management Science Department, specializing in forecasting and statistical modeling. He holds a PhD from Lancaster Centre for Forecasting (2013) and previously worked at Higher School of Economics and Saint-Petersburg State University. His research focuses on state-space models, intermittent demand forecasting, complex-valued models, and marketing analytics. Education: MSc from Saint-Petersburg State University of Economics and Finance (2006), PhD in Economics (2013). Key contributions include the Complex Exponential Smoothing method and the development of R packages 'smooth', 'greybox', and 'legion'. He maintains academic presence through LinkedIn, Twitter, and a forecasting website. Research interests span intermittent demand modeling, multivariate forecasting systems, advanced estimators, time-varying parameters, promotional modeling, and likelihood-based approaches. He supervises PhD students in statistical forecasting, requiring strong math/programming skills (R/Python). Labs/Teams: Active in Lancaster Centre for Marketing Analytics and Forecasting. Collaborations include work with John Boylan on intermittent demand models and Demand Works on ARIMA implementations.