جستجوی استاد راهنمای دانشگاهی

Professor Alexandra M. Schmidt is a leading academic in Biostatistics at McGill University, holding an endowed University Chair. She specializes in spatial and spatio-temporal modeling, particularly in epidemiology and environmental health. Previously, she served as a Full Professor at the Federal University of Rio de Janeiro (2012–2016). Her research focuses on Bayesian methodologies for analyzing complex processes, including disease spread, environmental hazards, and socio-economic disparities. She has authored influential books such as Spatio-Temporal Methods in Environmental Epidemiology with R (2023) and contributed to over 150 peer-reviewed articles. Key awards include the ISBA Fellowship (2024), ASA Fellowship (2020), and the Abdel El-Shaarawi Award (2008). Education: PhD in Statistics (2001, University of Sheffield, UK), MSc and BSc in Statistics (Federal University of Rio de Janeiro, Brazil). Research interests span Bayesian inference, spatial statistics, and environmental epidemiology. She has advised numerous PhD/MSc students and collaborated on projects linking statistical methods to public health challenges, such as modeling dengue outbreaks and air pollution impacts. Active in academic service, she has chaired major conferences (e.g., 2022 ISBA World Meeting) and serves on editorial boards of top journals like Bayesian Analysis and Canadian Journal of Statistics . Teaching includes advanced courses on generalized linear models, spatial epidemiology, and Bayesian analysis. Her work bridges theoretical statistics with practical applications, addressing global health issues through innovative spatio-temporal modeling techniques.

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

Dr. Sudhir R. Paul is a Professor in the Department of Mathematics and Statistics at the University of Windsor, Faculty of Science. He holds a Ph.D. from Wales and has received prestigious awards including Fellowships from the American Statistical Association (2006) and the Royal Statistical Society (1982). His research focuses on Biostatistics and Statistical Inference, with expertise in areas such as Generalized Linear Models, Clustered/Longitudinal Data Analysis, and Categorical Data Analysis. He has supervised numerous graduate students and maintains an active research program addressing topics like risk difference estimation, bias correction in statistical models, and applications in environmental and medical contexts. Education: Ph.D. (Wales). Research interests span advanced statistical methodologies, including zero-inflated models, measurement error correction, and dose-response modeling. His work bridges theoretical development and practical applications in epidemiology, clinical trials, and environmental studies. His publications reflect contributions to clustered data analysis, interval estimation, and generalized estimating equations. Awards highlight his impact in advancing statistical science through teaching, research, and service. Advising: Over 30 M.Sc. and Ph.D. students have been supervised, with current students engaged in doctoral and master’s research. Postdoctoral fellows include experts in statistical theory and applications.

• Biostatistics
• Statistical Inference
• Generalized Linear Models
• +۳ مورد دیگر

Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.

• Optimal experimental design
• Biostatistics
• Data science
• +۷ مورد دیگر

Grace Yi is a Professor at the University of Western Ontario and holds a Tier I Canada Research Chair in Data Science. She is affiliated with the Departments of Statistical and Actuarial Sciences and Computer Science. Her research focuses on statistical methodology addressing challenges in measurement error, causal inference, missing data, and machine learning. Yi has authored influential works, including the monograph Statistical Analysis with Measurement Error or Misclassification and co-edited Handbook of Measurement Error Models . She has served as Co-Editor-in-Chief of The Electronic Journal of Statistics and President of the Statistical Society of Canada. Her accolades include the CRM-SSC Prize (2010), Fellowships from the IMS and ASA, and leadership roles in professional societies. Education: Ph.D. in Statistics (University of Toronto, 2000), M.A. in Statistics (York University, 1996), M.Sc. and B.Sc. in Mathematics (Sichuan University, China). Research Interests: Measurement error models, causal inference, high-dimensional data analysis, statistical machine learning. Yi’s work bridges theoretical advancements and practical applications, particularly in handling noisy data across disciplines like epidemiology and public health. Her recent studies include analysis of COVID-19 data dynamics and quarantine strategies. She has supervised numerous students and contributed to software development, including R packages like augSIMEX and swgee .

• Statistical Methodology
• Measurement Error
• Causal Inference
• +۳ مورد دیگر

Charmaine Dean is the Vice-President, Research and Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. She holds leadership roles in research administration and academic governance, including previous service as Dean of Science at Western University and Associate Dean at Simon Fraser University. Dr. Dean earned her Ph.D. in Statistics from the University of Waterloo in 1988. Her research focuses on biostatistics, environmental science, and spatio-temporal analysis, with applications to public health, wildfire management, and ecological modeling. Notable contributions include disease mapping, clinical trial design, and statistical methods for analyzing wildfire risk and forest ecology. She has received prestigious awards such as the CRM-SSC Prize, Fellowships from leading statistical societies, and the L’Ordre des Palmes Académiques from France. Her service includes roles on advisory boards for institutions like the Pacific Institute for the Mathematical Sciences and the Banff International Research Station. Dr. Dean’s work bridges academic research and policy, with impactful contributions to environmental and public health policy through interdisciplinary collaborations. Her research group’s activities include developing statistical tools for environmental risk assessment and pandemic response.

• Biostatistics
• Environmental Science
• Epidemiology
• +۷ مورد دیگر

Cindy Feng, PhD, is an Associate Professor in the Department of Community Health and Epidemiology at Dalhousie University's Faculty of Medicine. Her research focuses on developing biostatistical models for analyzing correlated public health data, including spatial statistics, longitudinal studies, and environmental health applications. She holds a PhD from Simon Fraser University and has received funding from NSERC, Canadian Statistical Sciences Institute, and MITACS. Dr. Feng collaborates with interdisciplinary teams across medicine, psychology, and environmental sciences. Education: PhD (Simon Fraser University), MSc (Simon Fraser University), BSc (Beijing University of Technology). Her work emphasizes bridging statistical theory and practice in public health, with notable contributions to disease mapping, survival analysis, and infectious disease surveillance. Key grants include NSERC Discovery Grants ($80,000, 2019-2023) and a MITACS Accelerate Grant ($45,000, 2016-2019). Research interests include zero-inflated models, spatial epidemiology, and methodological advancements for correlated data. Recent work addresses pandemic-related mental health trends, occupational injury risk factors, and global health challenges in malaria-schistosomiasis co-endemic regions. She has published over 30 peer-reviewed articles, with a focus on statistical diagnostics, public health policy, and environmental health impacts.

• Biostatistics
• Spatial statistics and disease mapping
• Statistical methods for survival and longitudinal data
• +۳ مورد دیگر

Kevin McGregor is an Assistant Professor in the Department of Statistics at the University of Manitoba's Faculty of Science. Previously, he was employed as a faculty member at York University. His office is located in 256 Parker. Dr. McGregor's research focuses on biostatistics, statistical genetics, Bayesian statistics, and compositional data analysis with emphasis on microbiome data. His work involves developing statistical methods for analyzing high-dimensional biological data, particularly in the areas of microbiome composition, DNA methylation, and RNA-Seq analysis. Microbiome data analysis and network modeling Statistical genetics applications Bayesian inference methods Compositional data analysis techniques Variational inference approaches He has developed several software packages including countprop for proportionality metrics, MDiNE for microbiome differential network estimation, and MiCoRe for microbiome covariance regression. His current research includes implementing the zero-inflated alpha-folded normal distribution for microbiome modeling. Dr. McGregor provides statistical consulting services in microbiome analysis, DNA methylation and RNA-Seq analysis, psychology, educational psychology, emergency medicine, and pediatric surgery. He has advised several students including Ismaila Ba (CANSSI Distinguished Postdoctoral Fellow), MA student Zhijie Li, and undergraduate research assistants Nneka Okaeme (NSERC USRA 2022) and Yutong Chen (YorkU Dean's Undergraduate Research Award 2022).

• Biostatistics
• Statistical genetics
• Bayesian statistics
• +۶ مورد دیگر

Sergio Afonso Lago Alves is an Adjunct Research Professor at Carleton University's Department of Economics and an Economist at the Central Bank of Brazil. His primary affiliation is with the Faculty of Public and Global Affairs. He collaborates with Professor Hashmat Khan on research topics including macro-labour dynamics and monetary policy, actively participating in departmental research events, workshops, and seminars during his visits. His research focuses on Trend Inflation, Monetary Policy frameworks, Frictions in Expectations, Survey-Based Forecasts, Monte Carlo and Bayesian Inference methods, Unemployment Analysis, CBDC implications, and Macroeconomic DSGE modeling. He also examines the effects of the Zero Lower Bound on interest rates. Dr. Alves holds positions at both Carleton University and the Central Bank of Brazil, maintaining an interdisciplinary approach to economic analysis. His work bridges academic research and central banking practice, with a strong emphasis on quantitative methodologies.

• Monetary Policy
• Macroeconomic DSGE Models
• Trend Inflation
• +۴ مورد دیگر

Dr. Connie Stewart is a Professor and Chair of the Department of Mathematics and Statistics at the University of New Brunswick Saint John. She holds a PhD in Statistics from Dalhousie University (2005). Her research focuses on compositional data analysis, statistical ecology, and computational statistics, with applications to dietary analysis in marine biology and healthcare collaboration. She has led an NSERC Discovery Grant since 2008 and supervises undergraduate and graduate students in Statistics. Dr. Stewart collaborates with biologists studying grey seals in Atlantic Canada, developing statistical tools for dietary estimation via fatty acid signature analysis. She also contributes to healthcare projects, including studies on nursing outcomes and geriatric rehabilitation. Her work bridges theoretical statistics with applied ecological and medical challenges. As a researcher, she has presented at national and international conferences and published extensively on statistical methodologies for compositional data, healthcare systems analysis, and ecological modeling. She actively engages in mentoring, including co-supervising MSc/PhD students and guiding undergraduate research projects.

• Compositional Data Analysis
• Statistical Ecology
• Computational Statistics
• +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.

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

Dr. Dianliang Deng is a Professor in the Department of Mathematics & Statistics at the University of Regina, Canada. His academic affiliations include the Faculty of Science, where he specializes in advanced statistical methodologies and probability theory. He has held this position since at least 2000, as evidenced by his long-standing research output. Research Interests: Biostatistics and Probability: Survival analysis, statistical analysis of discrete data, longitudinal data analysis, and limit theorems in probability Focus areas include medical cost analysis, zero-inflated models, and applications in healthcare economics and genomics Recent Research Trends: Recent work emphasizes statistical models for medical costs, longitudinal quantile regression, and handling complex data structures like censored or sparse datasets. His publications span applications in genomics, healthcare economics, and time-series analysis. Awards & Grants: No specific awards or grants are explicitly mentioned in the provided texts. Labs & Teams: No lab affiliations or collaborative teams are listed, though his work suggests affiliation with interdisciplinary research groups in biostatistics and genomics.

• Biostatistics
• Survival Analysis
• Discrete Data Analysis
• +۳ مورد دیگر

Melody Ghahramani is a Professor in the Department of Mathematics and Statistics at the University of Winnipeg. Her research focuses on time series analysis, environmetrics, and biostatistics with applications in finance and infectious disease modeling. She holds a Ph.D. in Statistics from the University of Manitoba and has held academic positions in Winnipeg since completing her postgraduate studies. Education: Ph.D. in Statistics, University of Manitoba M.Sc. in Statistics, Simon Fraser University Bachelor's Degree in Mathematics and Statistics, University of Manitoba Research interests emphasize statistical methodologies for zero-inflated and overdispersed data, environmental modeling of temporary streams in Canadian prairies, and biostatistical applications in healthcare and epidemiology. Her work bridges theoretical statistics with practical applications in finance and ecological systems. Publications highlight advancements in time series regression, volatility modeling, and cryptocurrency trading strategies. She has contributed to statistical frameworks for analyzing count data and financial market dynamics. Her applied research includes modeling long-term survival outcomes in healthcare and environmental water resource systems. No formal awards are listed, though her extensive publication record reflects sustained academic contribution. She advises courses in statistics and supervises student research in mathematical and statistical methodologies. Contact details: Office 6L25, Lockhart Hall, reachable at m.ghahramani@uwinnipeg.ca or 204.789.1414.

• Time Series Analysis
• Biostatistics
• Environmetrics
• +۳ مورد دیگر

Julien Bureau is a Full Professor at Laval University, affiliated with the Faculty of Education and the Department of Foundations and Practices in Education . His research focuses on plagiarism and cheating in schools , equity in evaluation , and self-determination theory (motivation, autonomy support) across education, work, and family contexts. Research Themes: Academic Integrity and AI Impact Self-Determination and Autonomy Equity in Educational Assessment Recent publications span topics like transformational leadership , cheating dynamics , and AI in education . His work has been funded by SSHRC and Laval University's Strategic Acceleration Fund . Julien supervises doctoral students like Jee Seul Ahn and collaborates on initiatives exploring AI's role in pedagogy. Scientific Awards: Strategic Acceleration Fund Grant for AI in Teaching SSHRC Insight Development Grant

• Plagiarism and Cheating in Education
• Equity in Evaluation
• Self-Determination Theory
• +۴ مورد دیگر

Guohua Yan is Professor in Mathematics and Statistics at the University of New Brunswick, specializing in statistical methodologies for pattern recognition and complex data structures. His research develops innovative approaches to cluster analysis with applications across biological and medical sciences. Primary contributions include advancing linear clustering techniques that identify linear relationships within datasets, such as allometric studies in biology and genotyping analysis. Complementary research creates statistical models for clustered count data with excessive zeros, incorporating random effects to account for subject-level heterogeneity in medical studies. Current methodologies address challenges including missing data, measurement errors, and spatial dependencies in large-scale epidemiological research.

• Cluster Analysis
• Linear Clustering
• Statistical Modeling
• +۲ مورد دیگر

Hafedh Bouakez is a Full Professor and Director of the Department of Applied Economics at HEC Montréal, Canada. Holding a PhD and MSc in Economic Sciences from Université de Montréal, his expertise spans macroeconomics, international finance, and open economy dynamics. Full Professor, Department of Applied Economics, HEC Montréal Director, Department of Applied Economics PhD & MSc in Economic Sciences, Université de Montréal Research focuses on fiscal policy effectiveness in multisector economies, with particular emphasis on government spending multipliers , public debt dynamics , and monetary-fiscal interactions during economic crises. Key areas include business cycle analysis , exchange rate responses , and technology shock transmissions . Google Scholar reveals 15 recent articles (2015–2025) analyzing topics like Neo-Fisherian effects , price rigidity , climate policy , and international adjustment mechanisms . Collaborations with scholars like Omar Rachedi and Emiliano Santoro demonstrate sustained academic engagement. Supervisory activities include mentoring 11 graduate students (2019–2024) on topics ranging from real estate market fundamentals to Migrant remittances in WAEMU countries , exchange rate determinants , and import substitution strategies . Courses taught: Macroeconomic Environment of the Firm , Monetary and Financial Macroeconomics , and International Macroeconomics Seminar .

• Macroeconomics
• Open macroeconomics
• International finance
• +۵ مورد دیگر