Matrix Analytic Methods Professors and Supervisors in Canada

Qi-Ming Heمشاهده پروفایل

استاد

University of WaterlooCanada

Dr. Qi-Ming He is a Professor in the Department of Management Sciences at the University of Waterloo, within the Faculty of Engineering. His research focuses on Operations Research, Applied Probability, and Matrix Analytic Methods, with applications to queueing systems, inventory management, and telecommunications. He holds BSc and PhD degrees from the University of Science and Technology of China (1984, 1989) and a second PhD from the University of Waterloo (1996). His research interests span algorithmic probability, stochastic processes, and matrix analytic techniques applied to real-world systems such as manufacturing and healthcare logistics. Current projects involve phase-type distributions, queueing system design, and inventory optimization under multiple-item demand constraints. He has organized international conferences like CanQueue 2013 and contributed to proceedings of Matrix-Analytic Methods (MAM) conferences. Dr. He has published extensively in top journals including *Naval Research Logistics*, *European Journal of Operational Research*, and *Stochastic Models*. His work bridges theory and practice, addressing challenges in service systems, supply chains, and healthcare operations. He is active in academic service, serving on editorial boards and as a member of professional societies like INFORMS and CORS. Teaching responsibilities include graduate courses in stochastic processes, operations research, and matrix-analytic methods. He has taught at institutions worldwide, including Tsinghua University and City University of Hong Kong.

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Elif Acarمشاهده پروفایل

دانشیار

University of GuelphCanada

Dr. Elif Acar is an Associate Professor in the Department of Statistics at the University of Guelph. Her research focuses on high-dimensional data analysis, multivariate modeling strategies, and meta-analysis in biomedical applications. She holds a PhD in Statistics from the University of Toronto (2010), an MSc in Mathematics from the University of New Hampshire (2006), and a BSc in Statistics from Middle East Technical University (2003). Her research themes include high-dimensional dependence modeling and research synthesis in high-throughput studies, with applications in genome-wide association studies, microbiome research, and phenotyping experiments. She emphasizes methodological developments in handling complex data structures such as longitudinal and incomplete datasets. Dr. Acar has received multiple awards including the MATRIX-Simons Travel Award (2024) and NSERC Discovery Grants (2020–2027). Her recent publications span copula-based models, longitudinal data analysis, and interdisciplinary applications in genetics and neuroscience. She mentors postdoctoral fellows and accepts PhD/MSc students interested in her research areas. Current grants include NSERC funding for statistical method development and collaborative research initiatives.

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Jeff Andrewsمشاهده پروفایل

دانشیار

University of British ColumbiaCanada

Jeff Andrews is an Associate Professor at the University of British Columbia (Okanagan Campus), affiliated with the Department of Computer Science, Mathematics, Physics and Statistics within the Irving K. Barber Faculty of Science. He co-directs the Master of Data Science Program and leads the Andrews Research Group. Educated at the University of Guelph (PhD, MSc) and Acadia University (BSc), his work focuses on statistical machine learning, particularly mixture models for clustering and classification. His research explores parameter estimation, variable selection, and applications in bioinformatics, healthcare, and engineering. Research interests include computational statistics, radiation biology, and algorithm development for data analysis. Notable contributions include software packages like teigen , mmtfa , and vscc , which advance model-based clustering techniques. He has secured grants from NSERC, Mitacs, and CFI, and holds awards such as the Top 40 Under 40 (2024) and Chikio Hayashi Award (2017). His publications span machine learning applications in healthcare diagnostics, radiation response modeling, and statistical methodology. Prof. Andrews supervises graduate students in data science and statistics, emphasizing rigorous coursework in multivariate analysis and programming proficiency. He advocates for early-career researchers through mentorship and grant preparation guidance.

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Ghasan Doudakمشاهده پروفایل

استاد

University of OttawaCanada

Ghasan Doudak is a Professor of Structural Engineering at the University of Ottawa’s Faculty of Engineering, Department of Civil Engineering. He currently serves as Vice-Dean of EDI and Governance. His expertise spans structural systems’ multi-scale analysis, including windstorm, earthquake, and blast resistance in timber and hybrid buildings. He holds a Ph.D. from McGill University, an M.Sc. from the Technical University of Denmark (DTU), and a BASc from DTU. His research focuses on mid-rise wood buildings, blast-resistant design, and code development, supported by collaborations with institutions like the NSERC Strategic Network on Innovative Wood Products and Building Systems (NEWBuildS). Key research areas include timber structure behavior under dynamic loads, CLT shearwall performance with openings, and energy-absorbing connections. He has advised on national and international standards, including CSA 086 and CSA S850. Notable achievements include the 2008 Forest Products Laboratory Young Engineer Award for contributions to wood engineering. Dr. Doudak’s work bridges academia and industry, with past roles at the Canadian Wood Council shaping construction codes. His lab collaborations include the University of New Brunswick, ETH Zurich, and Penn State. Current projects emphasize sustainable retrofitting using mass timber and enhancing seismic resilience in light-frame buildings.

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Peter J. Dukesمشاهده پروفایل

استاد

University of VictoriaCanada

Combinatorics
Algebra
Graph Theory
+۳ مورد دیگر

Professor Peter J. Dukes holds a faculty position in the Department of Mathematics and Statistics at the University of Victoria. His research focuses on combinatorics, particularly applying algebraic and analytic methods to combinatorial problems. He has held positions including postdoctoral fellowships at Arizona State University and the University of Toronto. Dukes teaches advanced courses such as MATH 427 (Combinatorial Designs) and MATH 529 (Topics in Discrete Mathematics). His work spans graph theory, coding theory, and quasirandomness, with recent publications addressing graph decomposition, matrix balancing, and permutation patterns. He actively participates in academic conferences and organizes sessions on combinatorial design theory. His teaching philosophy emphasizes problem-solving and the appreciation of mathematical beauty, supported by resources like lecture notes for MATH 413 (applied algebra). Dukes also engages in outreach through activities like the UVic Math Competition and triangle decomposition games. Education: B.Sc. Mathematics, University of Victoria, 1997 M.Sc. Mathematics, University of Toronto, 1998 Ph.D. Mathematics, California Institute of Technology (Caltech), 2003 Research Interests: Combinatorics, algebraic methods in combinatorics, extremal graph theory, quasirandom structures, and applications to coding theory. His work often explores the intersection of discrete mathematics with algebraic techniques, such as permutation balancing and matrix decomposition problems.

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Thai Phanمشاهده پروفایل

استادیار مدعو

University of WaterlooCanada

Thai Phan is an Adjunct Assistant Professor specializing in Metal Isotope Geochemistry at the University of Waterloo. His research focuses on water-rock interactions and environmental processes, particularly in geological carbon sequestration and natural resource development. He employs geochemical modeling, field surveys, and laboratory experiments utilizing radiogenic and stable metal isotopes. Education: Doctorate in Environmental and Life Sciences (2012, Trent University), Master of Engineering in Environmental and Engineering Management (2007, Asian Institute of Technology), Bachelor's in Chemical Engineering (2004, University of Technology, Vietnam). Research interests include: Environmental isotope tracing for fluid mixing and contamination sources Geochemical impacts of hydraulic fracturing and CO2 storage Lithium isotope thermometry in sedimentary basins Microbial and abiotic controls on metal isotope fractionation Paleoenvironmental studies through lake sediment analysis Trace metal mobility in unconventional resource extraction Recent publications demonstrate expertise in using Li, Sr, B, Nd, and U isotopes to investigate Marcellus Shale systems, Permian Basin aquifers, and CO2 storage monitoring. His work spans groundwater contamination, diagenesis, and environmental remediation. No scientific awards were mentioned in the provided text. Teaching includes EARTH 221 (Introductory Geochemistry) and EARTH 358 (Earth System Science) at the University of Waterloo during 2020 and 2021.

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Michel C. Desmaraisمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Michel C. Desmarais is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has been faculty since 2002. With a PhD in Psychology from Université de Montréal, his research bridges artificial intelligence, educational technology, and human-computer interaction. He holds affiliations with IVADO and LAMA-WeST research groups, and has held visiting positions at Sorbonne University, Eindhoven Technical University, and other European institutions. His research focuses on three interconnected pillars: 1) Cognitive modeling and educational data mining , developing algorithms for student knowledge assessment and adaptive learning systems; 2) AI-driven educational tools , including automated grading systems and peer instruction platforms; and 3) Recommendation systems and user modeling , particularly for personalized learning interfaces. His work consistently applies machine learning to solve practical challenges in technology-enhanced education. Analysis of his 150+ publications reveals strong trends in educational NLP (sentence similarity for short-answer grading), generative AI (LLM-generated code validation), and Bayesian modeling (Q-matrix refinement). Recent work increasingly focuses on transformer architectures and real-world educational datasets. He maintains an active supervision record, having graduated 35+ graduate students. Current PhD candidates work on NLP for educational applications (Bakhtiari, Kamdem) and AI for engineering (Wang). His teaching covers user interface design, recommender systems, and intelligent interfaces. Professional service includes editorial leadership (JEDM journal), conference co-chairing (UMAP 2017, EDM founding), and grant review panels for NSERC, MITACS, and EU programs. Industry experience includes prior roles as R&D Director at MVM Inc. and researcher at Montreal Computer Research Center.

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