جستجوی استاد راهنمای دانشگاهی

Joshua Braultمشاهده پروفایل

پژوهشگر

• Business Cycles
• Monetary Policy
• Quantitative Macroeconomics
• +۳ مورد دیگر

Joshua Brault is a Senior Data Scientist at the Bank of Canada, specializing in the intersection of data science and macroeconomic modeling. He holds a Ph.D. in Economics from Carleton University (2021), an M.A. from Carleton University (2016), and a B.Sc. from Trent University (2014). His research focuses on business cycles, monetary policy, and quantitative methods, with notable contributions to dynamic stochastic general equilibrium (DSGE) model estimation and econometric techniques. Brault's work emphasizes applying advanced computational methods, such as parallel tempering algorithms, to enhance macroeconomic model accuracy. His recent studies explore inflation dynamics, central bank policy frameworks, and granular data challenges. He has published in journals like the Journal of Money, Credit and Banking and authored Bank of Canada staff working papers on topics such as Taylor rule reliability and DSGE estimation. His academic background includes a postdoctoral fellowship at Université du Québec à Montréal, reinforcing his expertise in structural macroeconomic analysis. Brault’s research bridges theoretical models with practical policy applications, contributing to the Bank of Canada’s understanding of economic stability and data-driven decision-making.

Longhai Liمشاهده پروفایل

استاد

• Statistical Machine Learning
• Model Diagnostics
• Bioinformatics
• +۳ مورد دیگر

Prof. Longhai Li is a Professor at the University of Saskatchewan, specializing in statistical machine learning and model diagnostics. His research focuses on developing methods for analyzing high-throughput and complex data, particularly in bioinformatics and medical research. He holds a Ph.D. from the University of Toronto and a B.Sc. from the University of Science and Technology of China. Prof. Li teaches diverse statistics courses, including introductory and applied statistics, computational methods, and probability theory. His teaching emphasizes practical tools like R and R Markdown, and he involves undergraduates in research projects, such as developing real-time disease tracking websites and statistical packages. His research has been funded by agencies like NSERC, CFI, and MITACS. Notable projects include statistical methodologies for microbiome data, geospatial AI for wheat production analysis, and predictive modeling for high-dimensional datasets. He has developed R packages like HTLR for Bayesian logistic regression. Prof. Li has supervised 3 postdoctoral fellows, 23 graduate students, and 14 undergraduates. His work appears in journals such as the American Statistician and BMC Medical Research Methodology.

Tariq Hasanمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Zero-Inflated Models
• Spatiotemporal Statistics
• +۲ مورد دیگر

Tariq Hasan is Professor and Director of Graduate Studies in Mathematics and Statistics at the University of New Brunswick. His research develops statistical methods for complex data structures including longitudinal, zero-inflated, and spatiotemporal datasets. Applications span environmental science, public health, and ecology. Methodological innovations include: joint modeling of clustered multinomial outcomes, Tweedie GLMs for skewed longitudinal data, and zero-inflated models accounting for autocorrelation. Work addresses real-world challenges like amphibian roadkill mortality analysis, air pollution health impacts, and osteoporotic fracture risk prediction. Publications demonstrate consistent focus on handling data complexities: simultaneous modeling of correlated processes, preservation of experimental designs in paired analyses, and integration of random effects for cluster heterogeneity. Recent advances address state-space frameworks for binomial series with random cluster sizes.

Woldegebriel Assefa Woldegerimaمشاهده پروفایل

استادیار

• Biomathematics
• Differential Equations
• Infectious Disease Modeling
• +۲ مورد دیگر

Dr. Woldegebriel Assefa Woldegerima is an Assistant Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. He previously held a Postdoctoral Research Fellowship at the University of Pretoria, South Africa. His academic credentials include a Ph.D. in Applied Mathematics from the University of Buea (co-supervised with Lehigh University), and Master's degrees from the African Institute for Mathematical Sciences and Addis Ababa University. His research program focuses on mathematical modeling of biological systems, with particular emphasis on infectious disease dynamics, in-host pathogen interactions, and the impact of climate change on disease transmission. Specific research areas include biomathematics, differential equations, artificial intelligence applications in epidemiology, and computational analysis of disease dynamics using Python-based frameworks. Dr. Woldegerima's publications demonstrate a consistent focus on developing novel mathematical frameworks for emerging infectious diseases. His recent work employs machine learning techniques to model transmission dynamics of pathogens including MPXV, Zika virus, and avian influenza. A significant portion of his research utilizes geospatial analysis and Bayesian hierarchical modeling to investigate socio-environmental determinants of disease spread across diverse global contexts.

• Stochastic Modeling
• Actuarial Science
• Catastrophe Risk Modeling
• +۵ مورد دیگر

Petar Jevtic is an Adjunct Assistant Professor in the Department of Mathematics and Statistics at McMaster University. His research focuses on stochastic modeling, actuarial science, catastrophe risk modeling, and cybersecurity. He has contributed significantly to mortality modeling, cyber risk quantification, and disaster impact analysis. His work often integrates interdisciplinary methods from statistics, economics, and computer science. Research interests include spatial mortality analysis, percolation theory applications in network vulnerability, and blockchain security. Notable projects involve quantifying social hardship from disasters like Hurricane Maria in Puerto Rico and developing frameworks for cyber risk assessment in critical infrastructure. Publications span topics such as mortality hedging strategies, loss distribution models for cyber attacks, and software tools for Petri net analysis. Collaborations include work with Traian Pirvu, Paul McNicholas, and Hector Antonio Paez. No grants or awards are explicitly mentioned in the provided texts. Advising and lab affiliations remain unspecified.

• Macroeconomics
• Monetary Economics
• General Equilibrium Models
• +۴ مورد دیگر

Malik Shukayev is an Associate Professor in the Department of Economics at the University of Alberta, Faculty of Arts. He holds a PhD from the University of Minnesota (2005), an MA from the University of Colorado at Boulder (1997), and an undergraduate degree in Engineering from the Kazakh National Technical University. Prior to joining the University of Alberta in 2016, he served as a researcher at the Bank of Canada. Education PhD in Economics, University of Minnesota at Twin Cities (2005) MA in Economics, University of Colorado at Boulder (1997) Bachelor of Engineering, Kazakh National Technical University (Kazakhstan) Research Interests Shukayev specializes in macroeconomics and monetary economics, focusing on general equilibrium models to analyze monetary, fiscal, and macroprudential policies. His work addresses challenges such as price-level targeting, inflation dynamics, and the interplay between financial stability and monetary policy. Recent research includes studies on risk-taking behavior at low interest rates and cross-border financial linkages. Teaching He teaches courses such as Intermediate Macroeconomics, Monetary Economics, and Central Banking: Models and Computation. His ECON 485 team won the national Governor’s Challenge competition in 2021 and 2023. Advising & Grants Shukayev has advised on projects related to central bank modeling and financial stability. His work frequently involves collaborations with institutions like the Bank of Canada, focusing on empirical macroeconomic models and policy simulations. Labs & Teams His research group emphasizes practical applications of dynamic stochastic general equilibrium (DSGE) models, with a focus on Canadian economic data and policy analysis.

• Macroeconomics
• Monetary Policy
• Fiscal Policy
• +۳ مورد دیگر

Jinill Kim is a Professor in the Department of Economics at Korea University, Seoul, South Korea. His research focuses on macroeconomic policy, particularly monetary and fiscal interactions, with affiliations to institutions including the Board of Governors of the Federal Reserve System, Bank of Korea, and International Monetary Fund. Primary Affiliation: Department of Economics, Korea University Secondary Affiliations: IMF, Federal Reserve Board, Bank of Finland, Barcelona School of Economics His work spans computational economics, dynamic general equilibrium modeling, and optimal policy design under conditions like zero interest rate bounds and demographic shifts. Recent publications address topics such as: Designing loss functions for central banks Welfare effects of fiscal transfers Extreme event responses in monetary policy Two-sector productivity shocks Kim's methodological expertise includes perturbation techniques, projection methods, and calibration of macroeconomic models.

Denis Larocqueمشاهده پروفایل

استاد

• Non-parametric statistical methods
• Data mining
• Multidimensional Data Analysis
• +۶ مورد دیگر

Denis Larocque is a Full Professor in the Department of Decision Sciences at HEC Montréal, specializing in statistical methods, data mining, and multidimensional data analysis. He serves as Director of the Doctorate in Administration (Ph.D.) program and is affiliated with the GERAD (Group for Research in Decision Analysis) and CIRRELT (Interuniversity Research Center on Enterprise Networks, Logistics and Transport). His academic background includes a M.Sc. and Ph.D. in Statistics from the University of Montreal. His research focuses on non-parametric statistical methods, survival analysis, reinforcement learning applications in transportation systems, and machine learning techniques such as random forests. Recent work includes studies on traffic signal control optimization, causal inference in medical data, and predictive modeling in healthcare. Larocque has supervised over 100 students and projects, including 2 doctoral theses and 4 master’s theses in the last five years. He teaches advanced data science courses and has contributed to interdisciplinary research across transportation, healthcare, and marketing domains. His publications span journals in statistics, transportation engineering, and bioinformatics. He actively collaborates with industry partners, applying statistical and machine learning methodologies to solve real-world problems in sectors such as finance, logistics, and healthcare. His work emphasizes practical applications of data-driven decision-making and methodological advancements in statistical computing.

Marc Fredetteمشاهده پروفایل

استاد

• Analysis of longitudinal data
• Prediction
• Biostatistics
• +۷ مورد دیگر

Marc Fredette is a Professor in the Department of Decision Sciences at HEC Montréal. His roles include academic supervision of the Master of Science (MSc) - User Experience in a Business Context and the Short Graduate Program in User Experience. He is also a board member of Tech3Lab, a member of CIRRELT (Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation), and GERAD (Group for Research in Decision Analysis). Education: M.Sc. in Statistics from Université de Montréal and Ph.D. in Statistics from the University of Waterloo. Research interests focus on longitudinal data analysis, biostatistics, Bayesian statistics, and random effects models. His work also explores human factors, UX design, and neuroIS applications, including studies on pedestrian safety, multitasking with IT systems, gamification, and neurophysiological correlates of collaborative tasks. Recent publications emphasize IT-related multitasking effects, gamification in workplaces, and neurophysiological studies of collaborative tasks. He has been recognized for contributions to UX evaluation methodologies and neuroIS research. Supervision includes 3 PhD dissertations and 7 MSc theses, along with 18 supervised projects on data analysis and business applications. His teaching includes courses on database analysis and project-based data analysis. Labs/Teams: Active in Tech3Lab, CIRRELT, and GERAD, focusing on interdisciplinary research in decision sciences and user experience innovation.

Paul Marriottمشاهده پروفایل

استاد

• Geometric Statistics
• Differential Geometry
• Mixture Models
• +۶ مورد دیگر

Paul Marriott is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on integrating geometric principles, particularly differential and convex geometry, into statistical methodologies, with a recent emphasis on mixture models and information geometry. He has published extensively across diverse journals such as Biometrika, Annals of Statistics, and Psychological Medicine, bridging theoretical and applied statistics. Education: PhD, University of Warwick (1989) MA, University of Oxford (1984) Research Trends: His work explores geometric frameworks for statistical inference, mixture model parameterization, and robustness analysis. Recent publications highlight causal modeling, neural spike train analysis, and high-dimensional data applications. Contact: Office: Mathematics & Computer Building (M3) 4204, Phone: 519-888-4911 x35545, Email: pmarriot@math.uwaterloo.ca