جستجوی استاد راهنمای دانشگاهی

Xin Gao is a Professor in the Department of Mathematics and Statistics at York University, Toronto. His research focuses on Artificial Intelligence , Machine Learning , and Statistical Genetics , with applications in biomedical data analysis and planetary science. He leads the Artificial Intelligence and Machine Learning Lab , which has developed impactful tools like an online Type 2 Diabetes risk predictor using logistic regression and Mars rock composition analyzers for NASA. His methodological work includes penalized composite likelihood and multi-task feature learning , implemented in R packages FusionLearn and lassoGEE . Scientific Awards NSERC Discovery Acceleration Award ($120,000, 2018-2020) Key Software Contributions FusionLearn : Correlated multi-task feature learning lassoGEE : High-dimensional clustered/longitudinal data analysis Notable Collaborations Vector Institute (AI scholarship mentoring) Fields Institute (committee roles) International genomic data integration projects

• Artificial Intelligence
• Machine Learning
• Statistical Genetics
• +۵ 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

Liqun Diao is an Associate Professor (Tenured) in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds affiliations with the Health Data Science Lab and the Waterloo Artificial Intelligence Institute. His research focuses on developing statistical methods and machine learning algorithms for applications in medicine, public health, and insurance. Education: B.Econ. in Statistics, Renmin University of China (2007) M.Math. in Statistics (Biostatistics), University of Waterloo (2009) Ph.D. in Statistics (Biostatistics), University of Waterloo (2013) Research Interests: Recursive partitioning and tree-based methods for survival and health data Causal inference and missing data methodologies Bayesian nonparametric models and copula dependence structures Mortality forecasting and actuarial science applications Awards: 2013 Pierre Robillard Award (best doctoral thesis in Canadian statistics) 2024 Outstanding Performance and Teaching Awards at UW Professional Activities: Led research groups in health data science and AI Recipient of multiple grants from NSERC and industry partners Advises graduate students in statistics and actuarial science Labs/Teams: Active contributor to the Health Data Science Lab and Waterloo AI Institute, focusing on applying statistical innovations to real-world health and insurance challenges.

• Statistical Methodology
• Machine Learning
• Causal Inference
• +۷ مورد دیگر

J.N.K. Rao is a Distinguished Research Professor in the Department of Mathematics & Statistics at Carleton University. A leading expert in survey sampling and statistical inference, he has made groundbreaking contributions to small area estimation (SAE) and data integration methodologies. Research Focus: Specializes in survey methodology, poverty mapping, Bayesian inference, and empirical likelihood techniques. Honors: Gold Medal of the Statistical Society of Canada (1993), Fellow of the Royal Society of Canada (1991), Waksberg Award (2005), SAE Outstanding Achievement Medal (2017). His recent work explores model-based SAE, combining probability and non-probability samples, and improving inference validity through robust calibration. Awards highlight his decades-long impact on statistical theory and practice. Email: jrao@math.carleton.ca

• Statistics
• Survey Sampling
• Statistical Inference
• +۳ مورد دیگر

Hugh Chipman is a Professor in the Department of Mathematics and Statistics at Acadia University , Canada. He holds a BScH (Acadia), MMath and PhD (University of Waterloo), and is a P.Stat. member of the Statistical Society of Canada. Education: BScH, Acadia University MMath, University of Waterloo PhD, University of Waterloo P.Stat. (Professional Statistician) Research Interests: Dr. Chipman specializes in statistical learning and data mining with applications to complex data structures. Key areas include Bayesian ensemble methods , tree-based models , design of experiments , network analysis , and high-dimensional data problems. He has contributed to drug discovery and functional data monitoring through computational statistics. Recent Talks: His 2024 presentation at UNC Chapel Hill focused on simulation studies for experimental design. Earlier work (2015) addressed big data education at Dalhousie University, while 2011-2014 talks covered active learning and network data mining at institutions like Harvard and MITACS. Scientific Awards: Co-organizer of NSERC-funded workshops Contributed to major statistical learning summer schools (e.g., AARMS 2014) Teaching: Courses include Statistics I and Design and Analysis of Experiments . He maintains software tools like BART (Bayesian Additive Regression Trees) and hybrid hierarchical clustering algorithms.

• Statistical Learning
• Data Mining
• Bayesian Methods
• +۵ مورد دیگر

Harry Joe is a Professor in the Department of Statistics at the University of British Columbia (Vancouver Campus). His primary research focuses on dependence modeling, copula theory, multivariate analysis, and applications in biostatistics, finance, and psychometrics. He has advised students including Xiaoting Li, Xinyao Fan, and Pavel Krupskiy. Research Interests: - Advanced copula constructions (e.g., vine copulas) - Extreme value theory and tail dependence - Applications in financial risk, biomedical research, and educational measurement - Multivariate time series analysis and non-Gaussian models Publications highlight contributions to copula-based classification methods (2024), factor copula models (2015), and dynamic dependence modeling (2020). His work bridges theoretical developments with practical applications across disciplines. Teaching and advising emphasize methodological innovation. Current research explores high-dimensional dependence structures and computational methods for complex data. No lab/team affiliations explicitly noted in provided materials.

• Dependence Modeling
• Copula Models
• Multivariate Analysis
• +۷ مورد دیگر

Junxi Zhang is an Assistant Professor in the Department of Mathematics & Statistics at Concordia University (Montreal, Canada). His research focuses on Bayesian nonparametric models, fairness in machine learning, and reinforcement learning. He holds a PhD in Statistics from the University of Alberta and has previously served as a postdoctoral fellow at the same institution. Education: PhD (Statistics, University of Alberta), MS (Mathematics, University of Kansas), BS (Statistics, Huazhong University of Science and Technology) Research interests include: Bayesian nonparametric models: asymptotic analysis, random measures, hierarchical models Fairness in machine learning: fair prediction models, bias measurement, health science applications Reinforcement learning: optimal control problems, temporal resolution trade-offs Additional interests: foundation models, time series analysis, survival analysis His recent publications address fairness in precision medicine, social bias in labor market text generation, and mathematical foundations of Bayesian nonparametric models. He is actively seeking graduate students in Mathematics and Statistics at the MA, MSc, and PhD levels for collaborative research.

• Bayesian nonparametric models
• Fairness in machine learning
• Reinforcement learning
• +۴ مورد دیگر

Asokan Variyath is a Professor of Statistics at Memorial University of Newfoundland, specializing in experimental design, statistical process control, and educational statistics. Education: Ph.D. in Statistics, University of Waterloo (2006) S.D.P Fellow (SQC & OR), Indian Statistical Institute (1991) Post Graduate Diploma in SQC & OR, Indian Statistical Institute (1988) M.Sc. Agricultural Statistics, Kerala Agricultural University (1987) Research Interests: Develops novel statistical methods in experimental design, quality control, and multivariate analysis. Applied research spans fisheries management, reliability engineering, and computational statistics. Created educational statistical applets to enhance student learning. Publication Trends: Recent publications focus on Bayesian methods, statistical education tools, and applications in fluid dynamics. Methodological work emphasizes variable selection, longitudinal data analysis, and nonparametric techniques. Leadership: Director of Statistical Consultancy Centre since 2010. Former Chair of Master of Data Science program and Deputy Head for Graduate Studies. Received Dean's Service Awards in 2023 and 2024.

• Statistical Process Control
• Experimental Design
• Machine Learning
• +۲ مورد دیگر

Dr. Angelo J. Canty is an Associate Professor in the Department of Mathematics and Statistics at McMaster University, Canada. His research focuses on computational statistics, genetic data analysis, and resampling methods. B.Sc. (1989) from University College Cork, Ireland M.Sc. (1991) and Ph.D. (1995) from the University of Toronto Postdoctoral work at University of Oxford and EPFL, Lausanne Assistant Professor at Concordia University (1998–2001) before joining McMaster His research spans computational statistics, with key contributions to: Markov Chain Monte Carlo convergence diagnostics Bootstrap and resampling techniques Saddlepoint approximations in statistical inference Efficient algorithm implementation for survey data analysis Genetic data analysis (microarrays, GWAS) His publications highlight a focus on automating convergence assessment (1994–1999), resampling for labor statistics (1999), and saddlepoint approximations in resampling (1996–1999). He developed an influential S-Plus library for resampling methods. Teaching includes: Statistics 4C03/6C03: Generalized Linear Models (Undergraduate/Graduate) Statistics 752: Linear Models and Experimental Design (Graduate)

• Computational Statistics
• Markov Chain Monte Carlo Methods
• Bootstrap and Resampling Techniques
• +۴ مورد دیگر

Andrei Volodin is a Professor in the Department of Mathematics and Statistics at the University of Regina, Canada. He serves as the Co-op Work/Study Coordinator and has an extensive publication record spanning probability theory, statistical inference, and applied statistics. His research focuses on limit theorems, bootstrap methods, and distributional analysis with applications to quality control and healthcare economics.

• Statistical Inference
• Limit Theorems
• Distributions on Abstract Spaces
• +۱۲ مورد دیگر

Jun Zhao is a tenure-track Assistant Professor of Economics at York University's Department of Economics, affiliated with the Faculty of Liberal Arts & Professional Studies. Her research focuses on micro-econometrics, empirical industrial organization, causal inference, and political economy. She holds a Ph.D. in Economics from Vanderbilt University, alongside MA and BA in Economics and a BS in Mathematics from Renmin University of China. Education: Ph.D. Economics, Vanderbilt University MA and BA Economics, Renmin University of China BS Mathematics, Renmin University of China Her research explores topics such as campaign financing effects on U.S. elections, nonparametric identification in Bayesian games, and causal inference methodologies. Recent work includes contributions to Journal of Econometrics . No awards listed. No advising or grant details provided beyond her research focus areas.

• Micro-econometrics
• Empirical industrial organization
• Causal inference
• +۳ مورد دیگر

Hong Li is a Professor in the Department of Economics and Finance at the Gordon S. Lang School of Business and Economics , University of Guelph (ranked #1 in Canada for Actuarial Science). He also holds an Adjunct Associate Professor position at the University of Waterloo's Department of Statistics and Actuarial Science. His research focuses on actuarial science, insurance economics, and data analytics applied to risk management challenges in longevity, climate, and agricultural sectors. Education : PhD in Econometrics & Operations Research, Tilburg University (2015) MPhil in Economics, Tilburg University (2012) B.A. in Finance, Xiamen University (2010) Research Interests include: Machine learning for insurance underwriting and pricing Mortality forecasting and longevity risk transfer Catastrophe bond design and climate risk modeling Optimization of index-based agricultural insurance Dynamic hedging strategies for financial institutions Awards : 2018 Bob Alting von Geusau Prize (best ASTIN Bulletin paper) University Research Award (2022-2023) Grants & Funding include: SSHRC Insight Development Grant ($69,623 CAD) NSERC Discovery Grant ($127,500 CAD) CAS Individual Grant ($18,000 USD) Teaching includes courses on risk management, actuarial economics, and financial econometrics at both undergraduate and graduate levels. Professional Service : Editorial Board Member: Annals of Actuarial Science and Journal of Risk and Insurance Member, Canadian Institute of Actuaries Academic Research Committee

• Actuarial Science
• Insurance Economics
• Data Analytics
• +۶ مورد دیگر

Alvaro Nosedal Sanchez is an Associate Professor in the Teaching Stream and Associate Chair (STATS) at the Department of Mathematical and Computational Sciences , University of Toronto Mississauga. His research integrates statistical theory with real-world applications in climate science, transportation, and disaster management. Research Interests Nonparametric Regression Gaussian Markov Random Fields Linear Models Bayesian Statistics Application of Statistical Methods Publication Trends His work spans 2012–2019 , focusing on statistical methodologies for transportation systems, climate modeling, and disaster response. Key themes include spatial statistics, algorithm development, and probabilistic modeling. Recent publications emphasize logistic regression in utility coordination and Gaussian Markov Random Fields in climate analysis. Teaching STA 215: Introduction to Applied Statistics STA 218: Statistics for Management and Economics STA 256/258/260: Core statistics and probability courses STA 302: Regression Analysis STA 313: Topics in Statistics STA 437: Applied Multivariate Statistics STA 457: Applied Time Series Analysis

• Nonparametric Regression
• Gaussian Markov Random Fields
• Linear Models
• +۲ مورد دیگر

Syed Ejaz Ahmed is a Professor of Mathematics and Statistics at Brock University, holding academic leadership roles including Dean of the Faculty of Mathematics and Science. His research focuses on high-dimensional data analysis, predictive modeling, and statistical machine learning, with applications across disciplines. He has held professorships at multiple institutions, including the University of Windsor and University of Regina, and has extensive editorial roles in journals like Technometrics. Education: PhD, Carleton University MSc, University of Guelph MSc, University of Karachi BSc (Honors), University of Karachi Research Interests: Dr. Ahmed’s work spans big data analytics, statistical inference, and applied statistics. He emphasizes developing methodologies for high-dimensional datasets and has contributed to fields like health data analysis, econometrics, and environmental statistics. His research has been funded by NSERC, CIHR, and industry collaborations. Awards: Fellow, American Statistical Association Fellow, Royal Statistical Society Bualuang ASEAN Chair Professorship Grand Prize Advancement Award (2019) Advising/Grants: He has supervised numerous PhD/Master’s students and postdoctoral fellows. Notable grants include continuous NSERC funding since 1987, including an OOO-ranked Discovery Grant (2017–2022). He also leads initiatives like the International Workshop on Perspectives on High-dimensional Data Analysis. Labs/Teams: Founded the Statistical Consulting and Research Center at the University of Windsor and contributed to programs like the Master of Science in Statistics at the University of Regina. He is a key figure in establishing actuarial science and data analytics programs in Canada.

• Big Data
• Predictive Modeling
• Data Science
• +۴ مورد دیگر

Saman Muthukumarana is a Professor and Head of the Department of Statistics at the University of Manitoba. He joined the department in 2010 as an Assistant Professor, was promoted to Associate Professor in 2016, and became a full Professor in 2022. He holds a BSc (Honours Special) in Statistics from the University of Sri Jayewardenepura, an MSc from Simon Fraser University, and a PhD from Simon Fraser University under Dr. Tim Swartz, focusing on Bayesian methods and applications. His research emphasizes Bayesian methodologies for complex models, with applications in social networks, health studies, sports analytics, environmental science, and machine learning. He has secured over $8.4M in research funding from NSERC, Mitacs, CIHR, and other organizations. His work has been published in journals such as the Canadian Journal of Statistics, Machine Learning with Applications, and IEEE Open Journal of Instrumentation & Measurement. Dr. Muthukumarana’s research spans Bayesian computation, biostatistics, data science, and environmental statistics. He has contributed to anomaly detection in buildings, predictive modeling for public health (e.g., Long COVID), and ecological studies like salmon stock recruitment. His collaborative projects include developing statistical tools for microbiome analysis and improving machine learning approaches for imbalanced datasets. He also leads the Data Science Nexus, fostering interdisciplinary research. His grants and collaborations highlight his role in advancing statistical methodologies for real-world challenges, including health, energy efficiency, and ecological conservation. While no specific awards are listed, his extensive funding and publication record reflect his scholarly impact. He currently supervises graduate students and actively participates in academic leadership roles.

• Bayesian Methods
• Biostatistics
• Data Science
• +۴ مورد دیگر