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Mohamedou Ould HayeView profile

Associate Professor

• Stochastic Processes
• Time Series Analysis
• Limit Theorems
• +5 more

Mohamedou Ould Haye is an Associate Professor in the School of Mathematics and Statistics at Carleton University. He holds a PhD in Mathematics and Statistics from Université Lille 1, France (2001), followed by postdoctoral research at Lille and HEC Montreal. He joined Carleton in 2003 as an Assistant Professor and was promoted to Associate Professor in 2007. His research focuses on Stochastic Processes , Time Series Analysis , Limit Theorems , and Long Memory Processes , with applications to forecasting and dependence modeling. He has contributed extensively to methodologies in seasonal long-memory data , nonstationarity detection , and empirical process analysis . His publications span over two decades, addressing topics such as frequency-domain testing for long-range dependence , confidence interval estimation for linear processes , and robust regression techniques . He teaches advanced courses in time series analysis, stochastic processes, and statistical theory. While no formal advisees are listed, his teaching portfolio includes courses like Statistical Methods for Business and Probability Theory . His work bridges theoretical advancements with practical statistical challenges in time series and dependent data analysis.

Yogendra P. ChaubeyView profile

Professor

• Sampling
• Distribution Theory
• Survival Analysis
• +3 more

Dr. Yogendra P. Chaubey is a Professor in the Department of Mathematics and Statistics at Concordia University, Montréal, Canada. Holding a Ph.D. from the University of Rochester (1977), his research focuses on statistical methodology with emphasis on sampling theory, distribution modeling, and survival analysis. Education: Ph.D., University of Rochester (1977) His work spans nonparametric density estimation for circular and non-negative data, wavelet-based noise reduction in biological imaging, entropy estimation, left-censored spatial modeling (e.g., arsenic contamination), and inverse Gaussian distributions. Recent collaborations include applications in protein classification and public health. Dr. Chaubey has published extensively on statistical theory and methodology, including corrections and extensions to classical estimators. His teaching includes advanced courses in sample survey theory and applications.

Manar AmayriView profile

Researcher

• Machine Learning
• Data Science
• Explainable AI
• +5 more

Dr. Manar Amayri is a researcher affiliated with the CIISE (College/Institution name not explicitly provided), focusing on machine learning, data science, and their applications in energy management systems. Her work emphasizes smart buildings, smart cities, and human-in-the-loop AI systems. She explores advanced clustering techniques, domain adaptation, and anomaly detection in smart environments. Research Interests: Machine Learning, Explainable AI, Energy Management Systems, Smart Buildings, Data Clustering, Domain Adaptation, and Anomaly Detection. Her studies address challenges in load forecasting, occupancy estimation, and energy disaggregation using deep learning, Bayesian methods, and probabilistic models. Publications highlight innovations in hybrid models (e.g., Transformer-BiLSTM networks), data imputation for electric vehicle charging, and unsupervised learning techniques for smart grid applications. Recent work emphasizes interpretability and robustness in AI systems. No scientific awards or grants were explicitly mentioned in the provided texts. No student advisees or lab affiliations were listed.

Pratheepa JeganathanView profile

Assistant Professor

• Statistical Learning
• Spatial Statistics
• Bayesian Methods
• +3 more

Dr. Pratheepa Jeganathan is an Assistant Professor in the Department of Mathematics and Statistics at McMaster University. Her research focuses on developing statistical methods for multi-view learning, particularly in modeling dependencies across heterogeneous data sources. Applications span molecular microbiology, spatial omics, sensor-based traffic data, and loss reserving. Her methodological work includes generative models, Bayesian sampling, constrained clustering, and spatio-temporal statistics. Education: PhD in Mathematics (Statistics) from Texas Tech University (2016), Postdoctoral Fellowship at Stanford University (2016–2020). Research Interests: Spatial statistics, statistical learning, high-throughput data methods, and statistical theory. Recent work includes analyzing microbiome interventions using transfer functions, studying vaginal microbiota communities, and applying recurrent neural networks to multivariate loss reserving. She has published in journals like PLoS Computational Biology and Genome Biology . Teaching: Instructs courses in data science (STATS 3DA3, CSE 780), statistical research projects, and graduate-level topics in statistics.

Christian GenestView profile

Professor

• Statistics
• Dependence modeling
• Extreme-value theory
• +6 more

Christian Genest is a Distinguished James McGill Professor in the Department of Mathematics and Statistics at McGill University. He holds a PhD from the University of British Columbia and has been a leading figure in statistics and probability for decades. His research focuses on dependence modeling, extreme-value theory, multivariate analysis, and their applications in environmental science, hydrology, insurance, and risk management. Education: BSc (1977) Université du Québec à Chicoutimi, MSc (1978) Université de Montréal, PhD (1983) University of British Columbia. Genest has received numerous accolades, including Fellowships from the Royal Society of Canada, American Statistical Association, Institute of Mathematical Statistics, and Fields Institute. He served as SSC President (2007-2008), Director of the Institut des Sciences Mathématiques (2012-2015), and held the Canada Research Chair in Stochastic Dependence Modeling (2011-2025). He has supervised 59 MSc, 9 PhD students, and 15 postdocs, with ongoing mentorship of 2 MSc, 3 PhD candidates, and 2 postdocs. Genest is a prolific speaker, having delivered over 364 talks globally, including plenaries at major conferences like the Statistical Society of Canada and the Fields Institute. His work bridges theoretical advancements and practical applications, with contributions to copula theory, extreme-value analysis, and statistical methodology. He advocates for public engagement, delivering outreach lectures at schools and colleges across Quebec. Award Highlights: 2011 SSC Gold Medalist, 2015 Royal Society of Canada Fellow, 2023 CRM-Fields-PIMS Prize, and 2024 Emanuel and Carol Parzen Prize for Statistical Innovation. Grants and Leadership: Coordinated the CRM Risk in Complex Systems thematic program (2017), edited major statistical journals, and chaired departmental interim leadership (2022-2023).

Lang WuView profile

Professor

• Longitudinal Data Analysis
• Mixed Effects Models
• Biostatistics
• +5 more

Lang Wu is a Professor in the Department of Statistics at the University of British Columbia (UBC), part of the Faculty of Science. His research focuses on biostatistical methods for analyzing complex health science datasets, particularly longitudinal data with missing values, censored observations, and measurement errors. He has expertise in constrained statistical inference, joint modeling of longitudinal and survival data, and applications in HIV/AIDS and cancer studies. Education: B.Sc. in Mathematics from East China Normal University (China), M.Sc. in Mathematics from Tulane University (USA), Ph.D. in Statistics from the University of Washington (USA), followed by a postdoctoral fellowship at Harvard University's Biostatistics department. Research Interests include: Longitudinal data analysis Mixed effects models Joint models linking survival and longitudinal outcomes Missing data imputation techniques Constrained hypothesis testing Biostatistical applications in infectious diseases Current advisees include Sihaoyu Gao and Qian Ye. Contact information includes lang@stat.ubc.ca (primary) and langwuubc@outlook.com (alternative). No scientific awards are listed, though his extensive publication record indicates impactful contributions to statistical methodology in health sciences.

Camila de SouzaView profile

Associate Professor

• Statistics
• Data Science
• Bayesian Statistics
• +7 more

Camila de Souza is an Associate Professor in the Department of Statistical and Actuarial Sciences within the Faculty of Science at Western University. She serves as Vice-director of Western Data Science Solutions (WDSS) and interim director of the Master of Data Analytics professional program, bridging advanced statistical methodology with real-world applications across healthcare, environmental science, and engineering domains. Her educational foundation includes a PhD in Statistics from the University of British Columbia, complemented by Master's and Bachelor's degrees in Statistics from Brazil's University of Campinas. This international training informs her interdisciplinary approach to complex data challenges. De Souza's research program develops cutting-edge statistical methods for analyzing large-scale complex data structures, with particular expertise in Bayesian variational inference, clustering algorithms, hierarchical mixture models, and survival analysis. Her work on hidden Markov models and nonparametric regression enables breakthroughs in fields ranging from ICU patient monitoring to astronomical data interpretation, consistently addressing methodological gaps in handling high-dimensional and heterogeneous datasets. Recent publications reveal a pronounced trend toward healthcare analytics applications, particularly in intensive care settings where her survival analysis models predict mechanical ventilation duration and patient flow optimization. Simultaneously, she extends statistical frameworks for environmental risk assessment (tornado-flood hazards) and energy systems through functional data analysis, demonstrating remarkable methodological versatility across disciplines. Her scientific recognition includes: 2014 Journal of Nonparametric Statistics Best Student Paper Award De Souza actively mentors doctoral candidates Ana Carolin Da Cruz and Chengqian Xian alongside MSc student Renan S. Barbosa, while securing research funding from natural sciences and health councils. Her supervisory approach emphasizes methodological rigor coupled with domain-specific application, preparing students for careers at the statistics-data science interface. Through WDSS, she leads a team providing statistical consulting services across Western University's research ecosystem, while shaping the Master of Data Analytics curriculum to meet industry demands for advanced modeling capabilities in an era of exponential data growth.

• Financial Econometrics
• Asset Pricing
• Market Microstructure
• +3 more

Jia Liu is an Associate Professor of Finance at the Sobey School of Business, Saint Mary's University. His research focuses on financial econometrics, asset pricing, and quantitative finance, with an emphasis on Bayesian methods and market dynamics. He has contributed to the analysis of market regimes, volatility modeling, and high-frequency financial data. His work often addresses topics such as bull/bear market identification, realized covariance estimation, and pairs trading strategies. Key methodologies include Bayesian nonparametric techniques and stochastic volatility models. He has published in top-tier journals, with a focus on empirical financial analysis and econometric modeling. No scientific awards or grants are explicitly mentioned in the provided text. No advisees or students are listed, and there is no information about affiliated labs or teams.

• Bayesian methods
• Biostatistics
• Data science
• +5 more

Saman Muthukumarana is a Professor and Department Head in the Department of Statistics at the University of Manitoba's Faculty of Science. His research focuses on Bayesian methods, biostatistics, data science, environmental statistics, multivariate analysis, and statistical machine learning. He actively publishes in top-tier journals and conferences, addressing challenges in imbalanced data classification, anomaly detection, and computational statistics. Recent work includes advancing SMOTE techniques for high-dimensional data, Bayesian network analysis for environmental monitoring, and applications of machine learning to healthcare and sports analytics. He is a sought-after collaborator, contributing to interdisciplinary projects in ecology, engineering, and public health. His academic profile highlights over 60 peer-reviewed publications since 2008, reflecting a strong commitment to methodological innovation and real-world impact. Muthukumarana is also involved in supervising graduate students and mentoring early-career researchers. His departmental leadership positions him at the forefront of statistical education and research strategy within the Faculty of Science.

Jiaying GuView profile

Associate Professor

• Econometric Theory
• Applied Econometrics
• Panel Data Analysis
• +7 more

Jiaying Gu is an Associate Professor in the Department of Economics at the University of Toronto. Her research focuses on econometric theory and applied econometrics, particularly in panel data analysis, structural dynamic models, and empirical Bayes methods. She holds a Ph.D. from the University of Illinois at Urbana-Champaign (2015), an M.A. from the National University of Singapore (2008), and a B.S. from Fudan University (2006). Gu’s work emphasizes unobserved heterogeneity in economic models, with contributions to teacher value-added estimation, health economics, and pandemic modeling. Her methodological advancements include nonparametric techniques for handling panel data and dynamic logit models. She has developed the R package REBayes , which implements empirical Bayes mixture methods for econometric applications. Her research themes include: Structural estimation of dynamic economic decisions Empirical Bayes approaches for longitudinal data Health and education policy evaluation Network-based diffusion modeling Gu’s recent work addresses challenges in ranking and selection methods, virus diffusion in economic networks, and partial identification in nonseparable models. Her empirical applications span teacher performance metrics, affordable housing trends, and pandemic economic impacts.

Luai Al LabadiView profile

Associate Professor

• Bayesian statistics
• Relative belief ratios
• Information theory
• +3 more

Luai Al Labadi is an Associate Professor in the Department of Applied Statistics at the University of Toronto , affiliated with the Mathematical and Computational Sciences school. His research focuses on Bayesian statistics, inferences related to relative belief ratios, information theory, model checking, and D-optimal designs in nonparametric regression.

John MaheuView profile

Professor

• Financial Econometrics
• Time Series Forecasting
• Stochastic Volatility Modeling
• +5 more

Dr. John Maheu is a Professor and Business Research Chair at the DeGroote School of Business, McMaster University. His research focuses on financial econometrics, time series forecasting, and stochastic volatility modeling, with a particular emphasis on structural breaks and Bayesian methodologies. University: McMaster University School: DeGroote School of Business Department: Finance and Business Economics Maheu’s work explores how macroeconomic and financial factors influence market volatility, bull/bear cycles, and portfolio allocation. His models incorporate flexibility to adapt to unforeseen changes, such as those observed during the COVID-19 pandemic. He advocates for the use of disaggregated data to sharpen risk estimates and improve investment decisions. His recent publications analyze topics like infinite hidden Markov models, Bayesian forecasting frameworks, and jump processes in financial markets. These works highlight advancements in modeling market regimes and volatility dynamics under uncertainty. Scientific awards include the Social Sciences and Humanities Research Council (SSHRC) grant, which supports his long-term research initiatives. This funding enables him to address complex financial and economic challenges through innovative econometric modeling. Maheu’s research bridges theory and practice, offering insights into market risk, structural instability, and the efficacy of volatility models. His contributions extend to advising on financial decision-making tools that account for evolving economic conditions.

Kevin SongView profile

Professor

• Econometrics
• Nonparametric Statistics
• Semiparametric Models
• +3 more

Kevin Song is a Professor at the Vancouver School of Economics (VSE), part of the Faculty of Arts at the University of British Columbia (UBC). He holds a B.A. from Seoul National University and a Ph.D. from Yale University. Prior to joining UBC in 2011, he served as an Assistant Professor at the University of Pennsylvania. His research focuses on econometric theory, particularly nonparametric and semiparametric models, optimal inference for nonregular and set-identified parameters, and structural models analyzing economic agent interactions. Education Background: B.A., Seoul National University, South Korea Ph.D., Yale University, USA Research Interests: Inference on nonparametric/semiparametric models Optimal estimation of nonregular parameters Set-identified parameters Structural models in economic interactions Recent Work Trends: His publications emphasize methodological advancements in econometric inference, including diffusion over networks, stable matching theory, and partial identification. His work bridges theoretical rigor with practical applications in service markets, education policy, and large-scale network analysis. Grants and Collaborations: Collaborations with institutions like the Lima Summer School and contributions to research centers such as the Stone Centre on Wealth and Income Inequality highlight his interdisciplinary reach. He oversees research streams in the VSE and contributes to policy discussions through structured empirical methodologies.

• Robust Statistics
• Machine Learning
• Biostatistics
• +3 more

Matias Salibian-Barrera is a full Professor in the Department of Statistics at the University of British Columbia's Faculty of Science, where he leads research in robust statistical methodologies with applications across machine learning, biostatistics, and environmental sciences. His research focuses on developing computationally efficient robust techniques resistant to outliers and model misspecification. Key areas include: Robust regression and inference for high-dimensional data Robust causal inference with complex observational data Robust functional and spatial data analysis Integration of robust methods with deep learning architectures Applications in genomics, climate science, and finance His recent publications demonstrate a strong trend toward unifying robust statistics with modern machine learning, particularly in adversarial settings and high-dimensional inference, while maintaining rigorous theoretical foundations. Notable honors include: CRM–SSC Prize in Statistics Fellow of the American Statistical Association Killam Research Prize Coxeter-James Prize He actively supervises doctoral students and secures major grants from NSERC and CIHR, focusing on robust methods for biomedical and environmental data. His work involves collaborations with UBC's Machine Learning Group and the Pacific Institute for the Mathematical Sciences, where he develops open-source software for robust statistical computing.

Mayer AlvoView profile

Professor

• Nonparametric Statistics
• Sequential Analysis
• Spatial Statistics
• +5 more

**Mayer Alvo** is a **Full Professor** in the **Department of Mathematics and Statistics** at the **University of Ottawa**. He holds an MSc from McGill University and a PhD from Columbia University. His research focuses on **Nonparametric Statistics**, **Sequential Analysis**, and **Spatial Statistics**, with applications in environmental modeling and ranking data analysis. Alvo has authored three books, including *A Parametric Approach to Nonparametric Statistics* (2018) and *Statistical Methods for Ranking Data* (2014). He has developed an **R package** *hypersampleplan* for hypergeometric distribution calculations. His research interests include analyzing trends in acid deposition in the Great Lakes, Bayesian statistics, and the application of statistical methods to big data. Alvo has supervised multiple graduate students, including **Rachid Bentoumi**, **Jingrui Mu**, and **Xiuwen Duan**. He teaches courses such as MAT 1371, MAT 2375, and MAT 4376, covering topics in probability, statistics, and applied mathematics. Alvo's recent work explores parametric embedding in nonparametric problems and change-point detection in stress-strength reliability. His contributions span theoretical advancements in ranking data analysis and practical applications in environmental science and finance. He is affiliated with the **Statistics and Probability Research Group** at the University of Ottawa.