جستجوی استاد راهنمای دانشگاهی

Samuel Jean Bassettoمشاهده پروفایل

دانشیار

• Industrial Engineering
• Artificial Intelligence
• Cognitive Science
• +۴ مورد دیگر

Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.

Christopher Ramseyمشاهده پروفایل

دانشیار

• Operator Algebras
• Functional Analysis
• Multivariable Operator Theory
• +۵ مورد دیگر

Christopher Ramsey is an Associate Professor and Interim Chair of the Department of Mathematics and Statistics within the Faculty of Arts and Science at MacEwan University in Edmonton, Alberta. He holds a PhD in Pure Mathematics from the University of Waterloo (2013), an MMath from Waterloo, and a BSc Honours from the University of Regina. Dr. Ramsey's research centers on operator algebras and functional analysis, with particular emphasis on non-selfadjoint operator algebras and multivariable operator theory. His work explores connections between analysis and algebra, studying algebras of infinite matrices and their applications to group theory, dynamical systems, free probability, and quantum information theory. He also investigates aperiodic order and its mathematical structures. His recent publications (2020-2025) demonstrate a consistent focus on operator algebras, with significant contributions to C*-algebras, tensor algebras, and their applications. The research spans theoretical foundations in functional analysis while connecting to diverse fields including symbolic dynamics, aperiodic structures, and quantum information. His work often bridges abstract algebraic structures with concrete analytical problems. Dr. Ramsey has received notable recognition including an NSERC Discovery Grant (2019), a MacEwan University Project Grant (2019), and an NSERC Postdoctoral Fellowship (2013). He serves as Editor-in-Chief of the MacEwan University Student eJournal (MUSe) and Associate Editor of the Canadian Transactions of Operator Theory. As an educator, Dr. Ramsey teaches various mathematics courses and supervises senior students' independent studies. His academic service includes editorial work and active participation in the Canadian Mathematical Society. His research program continues to develop connections between operator algebras and their diverse applications across mathematical disciplines.

Natalia Noldeمشاهده پروفایل

استاد

• Statistics
• Probability
• Multivariate Extreme Value Theory
• +۵ مورد دیگر

Natalia Nolde is a Professor in the Department of Statistics at the University of British Columbia, Faculty of Science. Her research focuses on multivariate extreme value theory , probabilistic modeling , and applications in quantitative risk management across finance, insurance, hydrology, and geosciences. Her work explores non-classical approaches to multivariate extremes, particularly through limit set geometry and asymptotic dependence structures , offering novel insights into tail dependence and risk assessment. Recent publications highlight her expertise in copula-based risk modeling , financial stress testing , and geohazard prediction . Current students include: Daniel Hadley Jonathan O.K. Agyeman

Svetlana Yanushkevichمشاهده پروفایل

استاد

• Biometric Technologies
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.

Bhushan Gopaluniمشاهده پروفایل

استاد

• Process Control
• Machine Learning
• Chemical Engineering
• +۷ مورد دیگر

Bhushan Gopaluni is a Professor in the Department of Chemical and Biological Engineering at the University of British Columbia, where he also serves as Associate Dean for Education and Professional Development in the Faculty of Applied Science. He holds associate faculty positions in multiple interdisciplinary institutes including the Institute of Applied Mathematics, Institute for Computing, Information and Cognitive Systems, Pulp and Paper Center, and Clean Energy Research Center. He previously held the Elizabeth and Leslie Gould Teaching Professorship from 2014 to 2017. Education: Ph.D. in Chemical Engineering, University of Alberta (2003) Bachelor of Technology in Chemical Engineering, Indian Institute of Technology, Madras (1997) Research Interests: Professor Gopaluni's research spans several critical areas at the intersection of chemical engineering, machine learning, and process control. His primary focus includes the development of advanced process control strategies using reinforcement learning and machine learning techniques. He has made significant contributions to battery technology research, particularly in capacity estimation and remaining useful life prediction for lithium-ion batteries. His work also encompasses sustainable energy systems, industrial process monitoring, fault diagnosis, and the application of digital twin technology in chemical processes. His research methodology emphasizes the integration of data-driven approaches with fundamental process understanding, leading to practical solutions for complex industrial challenges. This includes the development of interpretable machine learning models for industrial applications, real-time optimization strategies, and advanced monitoring systems for process industries. Publications and Research Impact: Professor Gopaluni's recent publications demonstrate a strong focus on cutting-edge applications of machine learning in chemical engineering. His work prominently features battery technology and energy systems, with multiple papers addressing lithium-ion battery capacity estimation and management. He has also contributed significantly to process control applications, including drilling process monitoring, greenhouse gas reduction in marine transport, and renewable carbon tracking in biofuel processing. His research extends to advanced computational methods including deep learning, reinforcement learning, and causal discovery in industrial processes. Awards and Recognition: Killam Teaching Prize (University of British Columbia) Dean's Service Medal (University of British Columbia) D.G. Fisher Award in Process Control (Canadian Society for Chemical Engineers) Elizabeth and Leslie Gould Teaching Professor (2014-2017) Professional Service and Editorial Roles: Professor Gopaluni currently serves as Associate Editor for three prestigious journals: Journal of Process Control, The Journal of Franklin Institute, and Results in Control and Optimization. His service to the academic community extends through his role as Associate Dean for Education and Professional Development, where he oversees educational initiatives across the Faculty of Applied Science. Industry Experience: From 2003 to 2005, Professor Gopaluni worked as an engineering consultant at Matrikon Inc. (now Honeywell Process Solutions), where he designed and commissioned multivariable controllers for British Columbia's pulp and paper industry and implemented controller performance monitoring projects across oil & gas and chemical industries.

• Construction Engineering
• Simulation Science
• Information Technology in Construction
• +۳ مورد دیگر

Simaan Abourizk, PhD, PEng, is the Dean of the Faculty of Engineering at the University of Alberta and holds the rank of Professor in Construction Engineering and Management. He also served as the NSERC/Alberta Construction Industry Research Chair (1997–2011) and Canada Research Chair in Operations Simulation (2001–2008). His roles include Executive Board membership at the Construction Research Institute of Canada. Education: Doctor of Philosophy in Construction Engineering and Management from Purdue University (1990), Master of Science and Bachelor of Civil Engineering (Honors) from Georgia Institute of Technology (1985 and 1984). Research focuses on advancing simulation technologies for construction and natural resource industries, including: Development of the Simphony simulation environment and COSYE framework Integration of AI, visualization, and scheduling tools (e.g., SmartEst, MTRACK) Optimization of tunneling, modular assembly, and industrial fabrication processes Key awards include the E. Whitman Wright Award (2002), E.W.R. Steacie Memorial Fellowship (2001), and Walter Shanly Award (2001). Current projects emphasize synthetic environments, safety analytics, and data-driven decision support systems in construction. He leads initiatives on risk simulation, resource allocation, and industrial automation.

Douglas G Downمشاهده پروفایل

استاد

• Stochastic Modelling
• Queueing Networks
• Scheduling Algorithms
• +۴ مورد دیگر

Douglas G Down is a Professor in the Department of Computing and Software at McMaster University . He leads the Resource Allocation and Stochastic Systems Lab (RASSL) and serves as co-PI of the Computing Infrastructure Research Centre (CIRC) . Education: Ph.D., University of Illinois at Urbana-Champaign His research focuses on stochastic modelling , scheduling , and performance evaluation of computer systems, with recent work on intelligent control of data centers , thermal-aware workload management , and data-driven resource allocation for healthcare systems. Publications span queueing theory , energy-aware scheduling , and machine learning applications in logistics. Recent scholarly trends include deep learning for pandemics (2023-2025), stochastic optimization of cooling systems (2024), and hybrid models combining queueing theory with AI (2023). He has pioneered frameworks like MGST for scheduling in heterogeneous grids and contributed to cache locality improvements in Hadoop systems. Scientific Awards: Nominated for Best Paper at MASCOTS 2013

Bin Hanمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Partial Differential Equations (PDEs)
• Applied Harmonic Analysis
• +۴ مورد دیگر

Bin Han is a Professor of Mathematics at the Department of Mathematical and Statistical Sciences , University of Alberta, Canada. He holds a PhD (1998), MSc (1994), and BSc (1991) in Mathematics from the University of Alberta, Chinese Academy of Sciences, and Fudan University, respectively. Research Interests: Computational Mathematics: High-order finite difference methods, numerical solutions of PDEs (Helmholtz, elliptic interface, Burgers' equations), and Fourier/wavelet-based algorithms. Applied Harmonic Analysis: Framelets/wavelets with applications in image processing, data sciences, and deep learning, focusing on directional and quasi-tight properties. Wavelet Theory: Construction of wavelets on bounded intervals for boundary value problems, Gibbs phenomenon analysis, and stability of refinable functions. Computer Aided Geometric Design (CAGD): Subdivision schemes, spline approximation, and isogemetric analysis. Article Trends: His recent work (2021-2022) emphasizes high-order finite difference methods for Helmholtz and interface problems, directional tensor product complex tight framelets for image processing, and quasi-tight framelets with balancing orders for robustness and sparsity. Scientific Awards: NSERC Postdoctoral Fellowship (1999-2000) Advising & Grants: He has supervised PhD students Qiwei Feng, Michelle Michelle, Ran Lu, and Chenzhe Diao. His research is supported by NSERC, Westgrid, Compute Canada, and MITACS.

Michael Friedlanderمشاهده پروفایل

استاد

• Convex Optimization
• Machine Learning
• First-Order Methods
• +۳ مورد دیگر

Michael Friedlander is a Professor of Computational Mathematics at the University of British Columbia (UBC), holding joint appointments in the Departments of Computer Science and Mathematics. He serves as Director of the UBC Institute of Applied Mathematics and is a Sauder School Distinguished Scholar. His research focuses on optimization theory, algorithms, and applications, particularly in convex optimization, first-order methods, and machine learning. Friedlander is a SIAM Fellow (2024) and has received multiple teaching awards, including the 2013 UBC CS Teaching Award. Education: PhD (2002) in Operations Research from Stanford University; BA (1993) in Physics from Cornell University. Research Interests: Friedlander’s work spans computational optimization, including first-order methods, gauge duality, federated learning, and applications in signal processing and quantum computing. His contributions include SPGL1, a widely used convex optimization solver, and foundational work on atomic decomposition and polar alignment in structured optimization. Key Contributions: His research bridges optimization with practical applications, such as compressed sensing and data science. Notable areas include dual methods for federated learning, low-rank spectral optimization, and algorithms for sparse recovery. He has advised numerous PhD students, including work on topics like gauge duality and federated learning contribution valuation. Awards & Honors: SIAM Fellow (2024) 2013 UBC CS Department Teaching Award CACS Outstanding Young Researcher Prize AI & Statistics 2009 Best Paper Award Advising & Grants: Supervised over 20 graduate students and postdocs. Active in grant-funded research, including projects on optimization algorithms and their industrial applications. Collaborates with institutions like Pacific Institute for Mathematical Sciences (PIMS) and the Data Science Institute at UBC. Labs & Affiliations: Affiliated with the Institute for Computing, Information and Cognitive Systems (ICICS), Data Science Institute, and Quantum Computing Research Cluster at UBC. Leads interdisciplinary efforts in applied mathematics and computational science.

Yiqiang Q. Zhaoمشاهده پروفایل

استاد

• Applied Probability
• Stochastic Processes
• Queueing Theory
• +۶ مورد دیگر

Professor Yiqiang Q. Zhao is a faculty member at the School of Mathematics and Statistics, Carleton University, where he has served as Associate Dean (Research and Graduate Studies) of the Faculty of Science since 2021. His research focuses on applied probability, stochastic processes, and their applications in telecommunication networks, queueing systems, and Gaussian processes. With over 130 peer-reviewed publications and 150+ students supervised since 2000, he has received the Carleton Faculty of Science Teaching Award (2002-2003) and twice been recommended as the 'Most Welcomed Teacher' by graduate students. He has also held editorial roles for journals like Stochastic Models and Queueing Systems . Dr. Zhao's research explores exact tail asymptotics in queueing systems, mean-field interaction models, copula constructions, and statistical inference for stochastic processes. His work spans wireless network modeling, resource allocation, and geometric methods in probability, with funding from NSERC and industry partners like Alcatel and MITACS. His recent publications highlight advancements in mean-field stability, retrial queue approximations, and kernel methods for multidimensional queueing analysis. Editorial board memberships and committee leadership roles underscore his contributions to academic governance. Scientific Awards: Carleton Faculty of Science Teaching Award (2002-2003) Most Welcomed Teacher recognition (twice) Dr. Zhao collaborates with international institutions and supervises a dynamic research team. His grants from NSERC and industry partnerships reflect his impact on applied probability research.

Hong Liمشاهده پروفایل

استاد

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

Alvaro Nosedal Sanchezمشاهده پروفایل

دانشیار

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

Yunran Weiمشاهده پروفایل

استادیار

• Quantitative Risk Management
• Actuarial Science
• Mathematical Finance
• +۴ مورد دیگر

Yunran Wei is a Tenure-track Assistant Professor at the School of Mathematics and Statistics, Carleton University. His research focuses on Quantitative Risk Management, Actuarial Science, Mathematical Finance, and FinTech/InsurTech. He holds a Ph.D. in Actuarial Science from the University of Waterloo, with supervisors Ruodu Wang and Gord Willmot, and has earned credentials including the Associate of the Society of Actuaries (ASA). Education: Ph.D. in Actuarial Science, University of Waterloo (Supervisors: Ruodu Wang, Gord Willmot) MMath in Statistics, University of Waterloo (Supervisor: Carole Bernard) BMath, Double Major in Pure Mathematics and Mathematical Finance, University of Waterloo Research Interests: Dr. Wei’s work bridges theoretical advancements in risk management and practical applications in financial markets. His research explores Cryptocurrency Market Risk , Risk Sharing Mechanisms , and Statistical Methods for Actuarial Applications . Recent projects analyze vulnerability in financial systems using conditional risk measures and investigate optimal allocations under heterogeneous beliefs. Awards & Funding: NSERC Discovery Grant (2023–2028) James C. Hickman Scholar Fellowship (2018–2019) Grants & Advising: As sole PI of NSERC grants totaling $39,500 CAD annually, Dr. Wei leads research teams focusing on risk analytics. His advising includes collaborations on cryptocurrency risk modeling and parametric risk measures. Professional Activities: Active contributor to journals like Mathematical Finance and Insurance: Mathematics and Economics , with a focus on advancing quantitative methods in finance and insurance.

• Multivariate Statistics
• Statistical Decision Theory
• Asymptotic Methods
• +۳ مورد دیگر

Martin Bilodeau is a full professor in the Department of Mathematics and Statistics at Université de Montréal. He holds a Ph.D. from the University of Toronto (1986) and is an Associate of the Society of Actuaries (ASA) since 1993. His research focuses on multivariate statistics, statistical decision theory, asymptotic methods, and robust statistics. Bilodeau has authored a notable textbook, *Theory of Multivariate Statistics* (Springer, 1999), co-authored with David Brenner, which provides a rigorous treatment of modern multivariate statistical theory. His work emphasizes foundational topics such as multivariate regression, principal components analysis, and robust statistical methods. Bilodeau has also contributed to computational statistics through R packages like *groc* and *IndependenceTests*, which implement advanced statistical techniques for regression analysis and independence testing. Recent publications include methodological advancements in variance component models (2021), independence testing across scales (2017), and applications of meta-elliptical distributions (2014). Bilodeau's research integrates theoretical developments with practical applications, spanning fields from ecology to actuarial science. His work on nonparametric independence tests (2007) and robust SUR models (2000) demonstrates his commitment to both statistical methodology and real-world problem-solving. He maintains an active academic profile with contributions to statistical education and ongoing collaborations in multivariate analysis.

Robert T.W. Martinمشاهده پروفایل

دانشیار

• Functional Analysis
• Operator Theory
• Multivariate Hardy Spaces
• +۳ مورد دیگر

Robert T.W. Martin is Associate Professor in the Department of Mathematics at the University of Manitoba, specializing in functional analysis and operator theory. His current research extends classical Hardy space theory to non-commutative multi-variable settings. His PhD from the University of Waterloo focused on bandlimited functions and self-adjoint operators. Research explores connections between multivariate Hardy spaces, non-commutative function theory, and operator algebras, developing new frameworks for multi-dimensional operator theory. Research develops non-commutative analogues of classical complex analysis concepts including Lebesgue decomposition, inner-outer factorization, and Clark measures. Recent publications establish fundamental results in non-commutative rational function theory and operator realizations. Publications demonstrate progression from classical operator theory to innovative non-commutative frameworks. Recent work on operator factorizations and Fock space applications has expanded mathematical foundations for multi-variable systems. He collaborates with colleagues internationally on operator algebras and free analysis. Research presentations at major conferences advance understanding of Hardy spaces in non-commutative settings.