First-Order Methods Professors and Supervisors in Canada

Antti Arppeمشاهده پروفایل

دانشیار

University of AlbertaCanada

Antti Arppe is an Associate Professor in the Department of Linguistics at the University of Alberta, Faculty of Arts. His research focuses on computational methods in linguistic analysis, particularly applied to endangered and Indigenous languages. He teaches courses like LING 325 - Writing Systems, exploring the historical and typological dimensions of writing systems and their cognitive impacts. His work integrates formal linguistic theory with advanced computational tools, addressing challenges in morphosyntax, phonology, and language documentation. Key research areas include: computational modeling of polysynthetic languages (e.g., Plains Cree), finite-state methods for morphological analysis, and developing open-source tools like DescTools for descriptive statistics. Arppe collaborates with Indigenous communities to create language revitalization technologies, including speech databases and interactive learning platforms. His contributions bridge academic linguistics with practical applications in language preservation and pedagogy. Recent projects highlight innovations in AI for endangered languages, such as evaluating neural ASR robustness for morphologically complex systems and leveraging word embeddings to enhance low-resource language tools. He actively participates in workshops and conferences on computational methods in endangered language studies, emphasizing interdisciplinary collaboration between linguists, technologists, and community stakeholders. Teaching emphasizes connecting theoretical linguistic concepts to real-world applications, with courses addressing writing systems’ sociocultural roles and technical dimensions. His publications span over two decades, demonstrating sustained engagement with both foundational linguistic theory and cutting-edge computational methodologies.

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Huang Fangمشاهده پروفایل

پژوهشگر

University of British ColumbiaCanada

Huang Fang is a Research Scientist at Baidu Research's Cognitive Computing Lab, specializing in optimization theory, machine learning, and data mining. With a strong academic background from top institutions, Huang has established themselves as a prolific researcher in the field of machine learning, particularly in federated learning, privacy-preserving algorithms, and optimization methods. Ph.D. in Computer Science, 2021, University of British Columbia M.S. in Statistics and Computer Science, 2017, University of California, Davis B.Sc. in Financial Mathematics, 2015, Central University of Finance and Economics (China) Huang Fang's research spans multiple critical areas in machine learning, with a particular focus on optimization theory and its applications. Their work bridges theoretical foundations with practical implementations, especially in federated learning systems where privacy and efficiency are paramount. The research portfolio demonstrates expertise in developing novel algorithms for large-scale learning problems, with particular emphasis on convergence analysis, sparse optimization, and scalable methods for knowledge representation. Huang has made significant contributions to understanding the theoretical properties of optimization algorithms under various constraints and settings. The publication record reveals a strong trajectory in advancing machine learning methodologies, with recent work focusing on differentiable neuro-symbolic reasoning, efficient Markov logic networks, and improved convergence guarantees for privacy-preserving stochastic optimization. The research shows a consistent pattern of addressing fundamental challenges in distributed and large-scale machine learning, particularly in the areas of federated learning fairness, efficient coordinate descent methods, and matrix completion techniques. Huang Fang has demonstrated exceptional productivity in high-impact venues including NeurIPS, ICML, ICLR, WWW, and other top-tier machine learning conferences. The work consistently combines theoretical rigor with practical applicability, making significant contributions to both the foundational understanding of optimization methods and their implementation in real-world systems.

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Yann-Meing Lawمشاهده پروفایل

استادیار

Polytechnic School of MontrealCanada

Yann-Meing Law is an Assistant Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, where he conducts research in numerical methods for partial differential equations with a focus on computational electromagnetics. He teaches courses including Differential Equations for Engineers and Scientific Computing for Engineers. Education: Ph.D. in Engineering Mathematics, Polytechnique Montréal (2018) M.Sc.A. in Applied Mathematics, Polytechnique Montréal (2014) B.Eng. in Biomedical Engineering, Polytechnique Montréal (2013) Dr. Law's research focuses on developing advanced numerical techniques for solving electromagnetic wave propagation problems. His work particularly emphasizes high-order methods for interface and embedded boundary problems, Hermite methods, the p-curl problem, and modeling nonlinear dispersive media. He applies these methods to Maxwell's equations, developing innovative correction function approaches that maintain accuracy at interfaces with discontinuous coefficients or complex geometries. Analysis of Dr. Law's recent publications (2019-2025) reveals a consistent research trajectory centered around developing and refining the Hermite-Taylor Correction Function Method. His work demonstrates increasing sophistication in handling complex interfaces, embedded boundaries, and nonlinear dispersive media, with strong interdisciplinary connections between computational mathematics, physics, and engineering applications. Dr. Law actively mentors graduate students and has expressed interest in recruiting motivated students for research-based Master's and Ph.D. programs focused on numerical methods for wave propagation in complex heterogeneous media. He has also been involved in outreach activities including mentoring first-generation students and advising coding clubs for K-12 students. His professional activities include presenting at major conferences such as SIAM meetings, WAVES conferences, and the North American High Order Methods Conference, demonstrating active engagement with the computational mathematics community.

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Pat Clancyمشاهده پروفایل

استادیار

McMaster UniversityCanada

Pat Clancy is an Assistant Professor in the Department of Physics and Astronomy at McMaster University. His research focuses on condensed matter physics, quantum physics, and materials synthesis, with a strong emphasis on neutron scattering studies of magnetic materials. Educational Background: Ph.D. in Physics, McMaster University (2007-2011) M.Sc. in Physics, McMaster University (2004-2006) B.Sc. in Physics with First Class Honours, St. Francis Xavier University (2000-2004) Banting Postdoctoral Fellow in Quantum Materials at the University of Toronto (2011-2016) Research Interests: Clancy's work explores quantum materials, including magnetic excitations in iridates, spin liquids, and pyrochlore systems. He investigates phenomena such as Dirac magnons, Kitaev materials, and the interplay of spin-orbit coupling with electronic structure. His techniques include neutron scattering, X-ray spectroscopy, and crystallographic analysis. Recent Work Trends: Recent articles highlight studies on quantum spin ice in Ce₂Zr₂O₇, skyrmion materials, and the role of disorder in Mott transitions. His research bridges theoretical models with experimental validation, often using McMaster's neutron facilities. Teaching & Mentorship: Clancy teaches core physics courses including Quantum Mechanics I, Condensed Matter Physics II, and introductory mechanics. He mentors students in experimental condensed matter physics and neutron scattering techniques. Labs & Facilities: He utilizes the McMaster Nuclear Reactor for neutron scattering experiments and collaborates on facility development. His group contributes to advancing quantum materials characterization methods.

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Kenneth Gilbertمشاهده پروفایل

استاد

McGill UniversityCanada

Kenneth Gilbert (1931–2020) was a distinguished harpsichordist, organist, musicologist, and professor. He held academic positions at institutions including McGill University (1964–72), Laval University (1969–76), and the Conservatoire de Paris (from 1988), where he became the first Canadian to hold the title of Professor of Harpsichord. His teaching extended globally, including masterclasses at universities in the U.S. and Europe, and summer courses in Antwerp and Haarlem. His research focused on Baroque music interpretation, editorial scholarship, and historical performance practices. Notable contributions include critical editions of works by François Couperin, Domenico Scarlatti, and Jean-Philippe Rameau. He was a pioneer in reviving interest in French and Canadian Baroque music, influencing organ building in Canada through the installation of a modern tracker organ at Queen Mary Rd United Church (1959). Gilbert's awards include Officer of the Order of Canada (1986), Fellow of the Royal Society of Canada (1988), and the Prix de musique Calixa-Lavallée (1981). His legacy includes training prominent musicians such as Scott Ross and John Grew.

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Amy Wuمشاهده پروفایل

استادیار

Queen's University at KingstonCanada

Amy Wu is an Assistant Professor in the Department of Mechanical and Materials Engineering at Queen’s University, Canada. She leads the Biomechanics x Robotics Laboratory (BxRL) and holds the Mitchell Professorship in Bio-Inspired Robotics. Her research integrates human biomechanics and robotics to develop assistive technologies, focusing on mobility augmentation for individuals with disabilities. Key areas include exoskeleton design, gait analysis, and neuromuscular control systems. Affiliations: Queen’s University (Faculty of Engineering & Applied Science), Ingenuity Labs Research Institute Appointments: Mitchell Professor in Bio-Inspired Robotics Dr. Wu’s educational background includes a PhD in Mechanical Engineering from the University of Michigan (2015), an MSE in Aerospace Engineering from the University of Michigan (2011), and an SB in Aeronautics and Astronautics from MIT (2006). She previously conducted postdoctoral research at EPFL’s Biorobotics Lab in Switzerland. Research Interests: Human biomechanics, gait and balance control, robotic exoskeletons, neuromuscular modeling, assistive technology innovation, and open-source fabrication methods for robotic devices. Her lab’s work emphasizes first-principles biomechanical insights applied to robotics, with applications in spinal cord injury rehabilitation and human-robot symbiotic systems. Recent projects include the Symbitron modular exoskeleton and haptic-guided motor learning devices. Awards: Mitchell Professorship appointment recognizing her contributions to bio-inspired robotics and assistive technology development. Teaching: MECH 210 - Electronic Circuits and Motors for Mechatronics Labs/Teams: Biomechanics x Robotics Laboratory (BxRL), Ingenuity Labs (cross-appointed). Collaborates with industry and academic partners on wearable robotics and biomedical engineering.

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Sheila M Embletonمشاهده پروفایل

استاد

York UniversityCanada

Dr. Sheila Embleton is a Distinguished Research Professor in Linguistics at York University with joint appointments in mathematics and linguistics. She served as Vice-President Academic & Provost at York University (2000-2009) and interim President of Laurentian University (2023). Her research spans historical linguistics, mathematical methods in linguistics, sociolinguistics, dialectology, and onomastics, with notable publications including Statistics in Historical Linguistics . Her work has been supported by SSHRC grants, focusing on dialectometry applications. Dr. Embleton holds degrees from the University of Toronto: PhD in Linguistics, MSc in Mathematics & Statistics, and BSc in Mathematics & Linguistics. She received additional certifications from European institutions in French, German, and Finnish linguistics. Research Interests: Mathematical linguistics, dialectometry (British, Finnish, Romanian), onomastics (personal/place names), Finnish literature, women and language, international education policy, and Canada-India academic relations. Her work integrates quantitative methods with sociolinguistic analysis. Awards & Honors: Fellow of the Royal Society of Canada Knight First Class of the Order of the White Rose of Finland Internationalization Leadership Award (CBIE) York University's Outstanding Contribution to Internationalization Fellow of the Bahamas Historical Society Continuing Senior Fellow, Massey College Professional Leadership: Former Secretary of the Royal Society of Canada (2015-2021), President of its Academy of Arts and Humanities, editorial board member for Diachronica and Word , and Chair of the Ontario Geographical Names Board. Significant contributions to international education include leading York's India Strategy and developing the Ontario-Maharashtra-Goa exchange program.

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Florent Madelaineمشاهده پروفایل

استاد

University of WaterlooCanada

Florent Madelaine is a Professor at the Laboratoire d'Algorithmique, Complexité et Logique (LACL) at the Université Paris-Est Créteil (UPEC). His academic journey includes a PhD from the University of Leicester (2003) under Iain Stewart and Etienne Grandjean. He has held roles such as Lecturer at Clermont-Ferrand (2007–2018) and Research Associate at Durham University (2003–2007), with visiting positions at Charles University and École Polytechnique. His research focuses on theoretical computer science, particularly finite model theory, computational complexity, and constraint satisfaction problems (CSP/QCSP), with contributions to MMSNP logic and complexity dichotomy conjectures. Currently affiliated with CNRS GdR IM, his work bridges algebraic methods and logical frameworks in computational problems. Education: B.Sc. and M.Sc. in Mathematics (Caen University), M.Sc. in Computer Science (Dresden), PhD in Computer Science (Leicester). Research interests emphasize foundational aspects of computational complexity, with a focus on logical characterizations, algebraic approaches, and constraint satisfaction problem classifications.

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Christopher Eagleمشاهده پروفایل

عضو هیئت علمی

University of VictoriaCanada

Christopher Eagle is an Associate Teaching Professor in the Department of Mathematics and Statistics at the University of Victoria, Canada. He holds a Ph.D. in Mathematics from the University of Toronto (2015), with prior degrees including an M.Math. in Pure Mathematics (University of Waterloo, 2010), an M.Litt. in Philosophy (University of St. Andrews, 2008), and a B.Math. in Pure Mathematics (University of Waterloo, 2007). His research focuses on mathematical logic and its applications, particularly model theory and its interactions with functional analysis, topology, and infinitary logics. Key areas include real-valued logics, applications to C*-algebras, and the development of pure model theory. He also explores mathematics education, emphasizing the role of language and writing in teaching and learning. Eagle has taught a wide range of courses at UVic, including matrix algebra, real analysis, mathematical logic, topology, and directed studies in model theory and set theory. His teaching roles have included coordinating multi-section courses such as Matrix Algebra for Engineers and Calculus. His publications span model theory, functional analysis, and topology, with contributions to journals like the Annals of Pure and Applied Logic, Journal of Functional Analysis, and Topology and its Applications. Recent work includes studies on computable K-theory for C*-algebras, topological principality in groupoid C*-algebras, and applications of topology to model theory. Eagle’s academic trajectory includes postdoctoral research at the University of Toronto at Mississauga (2015) and prior teaching roles as an Assistant Teaching Professor at UVic (2016–2023). He actively engages in interdisciplinary research, bridging logic, algebra, and topology.

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José CÔTÉمشاهده پروفایل

استاد

University of MontrealCanada

José CÔTÉ is a Full Professor in the Faculty of Nursing Sciences at the University of Montreal and a regular researcher at the Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM). She holds the Research Chair on New Nursing Care Practices (2005-2025) and has been instrumental in pioneering innovative nursing practices in digital health. Her work spans multiple programs including undergraduate and graduate nursing education at the University of Montreal. Dr. CÔTÉ's educational background includes: Baccalaureate in Nursing Sciences (1985, Université Laval) Master's in Nursing Sciences (1989, Université de Montréal) Doctorate in Nursing Sciences (1999, McGill University) Postdoctoral Fellowship (2002-2005, Université Laval) As a pioneer in digital health nursing, Dr. CÔTÉ created the TAVIE™ concept (Traitement Assistance Virtuelle Infirmière et Enseignement) for supporting people in managing chronic health conditions. Her research focuses on innovative nursing interventions using information and communication technologies to improve chronic disease management, particularly for people living with HIV/AIDS. She explores how virtual nursing interventions can enhance therapeutic education, patient empowerment, and accessibility to healthcare services, with particular attention to salutogenesis and care continuum. Analysis of Dr. CÔTÉ's recent publications reveals a strong emphasis on digital health interventions for chronic disease management, particularly in HIV/AIDS care, mental health, and substance use disorders. Her work consistently explores how technology-mediated nursing interventions can improve patient outcomes, with a focus on virtual care models, patient empowerment, and tailored therapeutic approaches. The interdisciplinary nature of her research spans nursing, psychology, digital health, and public health, reflecting her commitment to innovative, evidence-based nursing practices. Dr. CÔTÉ has received numerous prestigious awards including: Fellow of the Canadian Academy of Nursing Sciences (2024) SIDIIEF International Recognition Award (2025) Canadian Association of Schools of Nursing Award Y Foundation for Women Award Order of Nurses of Quebec Award Quebec Government Women's Status Secretariat Award Millenia 2015 International Foundation Award Sanofi International Award Dr. CÔTÉ has supervised numerous graduate students across multiple cohorts, guiding PhD and Master's research in areas related to digital health nursing, chronic disease management, and HIV/AIDS care. Her research program is extensively funded by major Canadian research agencies including the Canadian Institutes of Health Research (CIHR), the Fonds de recherche du Québec - Santé (FRQS), and the Fonds de recherche du Québec - Société et culture (FRQSC). Current projects include 'Accueillir la parole de ceux qui cherchent refuge' (2025-2030), 'Science of Nursing and Health Practices' (2025-2030), and 'Réseau SOIN: SOigner et INnover avec et pour tous les acteurs du soin' (2024-2026). Dr. CÔTÉ co-directs the RRISIQ (Réseau de recherche en interventions en sciences infirmières du Québec), a research network focused on nursing interventions that brings together researchers from multiple Quebec universities. She is also affiliated with the CRCHUM research center where she conducts her clinical research. Her work with the TAVIE™ platform represents a significant technological infrastructure for developing and testing virtual nursing interventions across various chronic conditions.

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Dominique Orbanمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.

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Ange Adrienne Nyamen Tatoمشاهده پروفایل

استادیار

Laval UniversityCanada

Ange Adrienne Nyamen Tato serves as an Assistant Professor in the Department of Teaching and Learning Studies at Laval University's Faculty of Education. Her academic journey spans multiple institutions across Canada and Morocco, with a strong interdisciplinary background combining computer science, artificial intelligence, and educational theory. PhD in Computer Science (Artificial Intelligence) from University of Quebec at Montreal (UQAM), 2020 Master's degree in Computer Science from University of Quebec at Montreal (UQAM), 2015 Engineering degree in Information Systems from Mohammedia School of Engineers in Morocco, 2014 DEUST in Mathematics, Computer Science and Physics from Hassan II University, Morocco, 2011 Professor Tato's research focuses on the intersection of artificial intelligence and education, with particular expertise in generative AI applications for educational contexts, machine learning algorithms, intelligent tutoring systems, educational data mining, and serious game design. Her work addresses critical challenges in educational technology including transparency, assessment practices, and engagement issues that have limited the adoption of AI-powered learning tools. She is particularly interested in the ethical implications, environmental impact, and potential biases of AI in educational settings. Her recent publications demonstrate a consistent focus on developing sophisticated models for user behavior prediction, adaptive learning systems, and integrating pedagogical knowledge into AI frameworks. The research spans multiple domains including logical reasoning development, socio-moral reasoning, piloting training, and general educational applications of deep learning and knowledge tracing techniques. Professor Tato has secured significant research funding, including a $192,500 NSERC Discovery Grant for her project 'Optimizing Generative Artificial Intelligence for Education: Towards a Holistic Approach Integrating Teachers and Learners.' This five-year project aims to develop pedagogically aware large language models (PA-LLMs) that better serve educational purposes by integrating educational theories, human learning factors, and bias correction mechanisms. Principal Investigator for NSERC Discovery Grant ($192,500 over 5 years) PC Member for ICCE 2023 and 2024 conferences PC Member for EDM 2024 conference PC Member for AIED 2024 conference Professor Tato teaches graduate courses including TEN-7028 Games and Learning and TEN-7030 Digital Intelligence in Education: Opportunities and Challenges. She is currently accepting Master's students interested in AI applied to education. Her previous professional experience includes work as an Artificial Intelligence Specialist at Beam Me Up Augmented Intelligence (2018-2022) and a postdoctoral fellowship in Deep Learning applied to aeronautics in partnership with Bombardier and CAE (2020-2022).

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Jean-François Côtéمشاهده پروفایل

استاد

Laval UniversityCanada

Jean-François Côté is a Full Professor at the Department of Operations and Decision Systems within the Faculty of Business Administration at Laval University. His research focuses on combinatorial optimization, stochastic programming, and mathematical programming with applications in vehicle routing, cutting and loading problems, and logistics optimization. Dr. Côté holds a Doctorate in Computer Science, Operations Research from the University of Montreal, along with Master's and Bachelor's degrees in the same field from the same institution. His academic credentials provide a strong foundation for his interdisciplinary research at the intersection of computer science and operations research. His research spans several key areas in operations research including: Combinatorial optimization techniques for complex routing problems Stochastic programming approaches for handling uncertainty in logistics Mathematical programming models for integrated decision making Vehicle routing and delivery optimization in various contexts Cutting and loading problems in transportation and manufacturing Professor Côté's recent publication record demonstrates a strong focus on solving complex logistics and transportation problems with mathematical rigor. His work addresses challenging real-world problems including same-day delivery optimization, bike sharing systems, home healthcare routing, and automotive manufacturing sequencing. His methodological contributions include novel algorithmic approaches such as logic-based Benders decomposition, disaggregated integer L-shaped methods, and branch-and-regret algorithms. Dr. Côté has published extensively in top-tier operations research journals including: INFORMS Journal on Computing European Journal of Operational Research Transportation Science Computers & Operations Research Operations Research He actively collaborates with researchers across North America and Europe through the CIRRELT research center. His work addresses practical problems faced by industries including transportation, healthcare, manufacturing, and e-commerce. Professor Côté supervises graduate students working on cutting-edge optimization problems and contributes to advancing both theoretical and applied aspects of operations research. His laboratory, part of the broader research ecosystem at Laval University, focuses on developing innovative mathematical models and algorithms to solve complex decision-making problems in logistics and supply chain management.

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Wylie Strobergمشاهده پروفایل

استادیار

University of AlbertaCanada

Wylie Stroberg is an Assistant Professor in the Department of Mechanical Engineering within the Faculty of Engineering at the University of Alberta. He leads the Computational BioSystems Lab, where he conducts research at the interface between cell biology, physiology and engineering using a range of computational and theoretical techniques. His research interests span multiple areas including: Biomechanics and Biomedical Engineering Biomaterials Mechanics and Materials Composites and Polymers Nano and Micro Materials Computational Mechanics Systems Biology of Proteostasis and the Unfolded Protein Response Reactions in Crowded Cellular Compartments Wetting Phenomena of Nanoscale Structures Dr. Stroberg's research focuses on developing new quantitative techniques for studying complex biological systems across disparate length and time scales. His work has significant implications for understanding cellular physiology, protein homeostasis, and developing new therapeutic approaches for age-associated and protein folding diseases. He employs advanced computational methods including multiscale modeling, machine learning, and theoretical analysis to investigate biological phenomena from the molecular to cellular level. His research group has published extensively in mathematical biology and computational mechanics, with a consistent focus on enzyme kinetics, cellular stress responses, and computational modeling of biological processes. The publications demonstrate a strong interdisciplinary approach combining techniques from mechanical engineering, computational science, and molecular biology. Dr. Stroberg has received recognition for his work in mathematical biology and systems biology, with publications in journals such as the Journal of Theoretical Biology, Biophysical Journal, and Mathematical Biosciences. He currently advises several graduate students working on diverse projects including multiscale modeling of enzymatic nanosensors, protein-based modulation of endoplasmic reticulum shape, development of machine learning-based coarse grained models for high-entropy alloys, and simulation of high-entropy alloys for thermal-spray deposition. His lab has trained undergraduate and master's students who have gone on to further academic pursuits. The Computational BioSystems Lab maintains active collaborations with other researchers at the University of Alberta and beyond, working at the intersection of engineering, biology, and computational science.

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