جست‌وجوی استاد و پژوهشگر

Depeng Jiangمشاهده پروفایل

استاد

• Biostatistics
• Longitudinal Data Analysis
• Multilevel Models
• +۳ مورد دیگر

Dr. Depeng Jiang is a Professor in Biostatistics at the University of Manitoba's Max Rady College of Medicine, Department of Community Health Sciences. He also leads the Biostatistics Group in the data science platform of George & Fay Yee Centre for Healthcare Innovation (CHI). His research focuses on developing advanced statistical methods for health services research and program evaluation. Education includes: PhD in Applied Statistics from Southeast University (2001) MSc in Applied Statistics from Southeast University (1991) BSc in Mathematics from Jiangsu Second Normal University (1987) Research interests center on longitudinal data analysis, multilevel models, person-oriented statistical approaches (latent class analysis, growth mixture models), structural equation modeling, and clinical trial design. His work significantly contributes to understanding missing data methodologies and advanced models for complex health data analysis. Recent publications demonstrate a strong focus on applied statistical methods in public health, including mental health interventions during the pandemic, aging research, and financial modeling in healthcare contexts. Articles consistently integrate advanced statistical approaches with real-world health applications. Awards include: Norman Raab Foundation Fellowship (2022) Canadian Jewish Literary Award (2016) John Charles Polanyi Prize for Literature (2007) Horst Frenz Prize (2004) Dr. Jiang actively mentors graduate students and leads several research grants from NSERC, CIHR, and CHRIM. His laboratory conducts interdisciplinary research with clinical partners and focuses on translating statistical methodologies into improved health outcomes.

James Alan Olooمشاهده پروفایل

استادیار

• Educational Equity
• Multicultural Education
• Indigenous Education
• +۵ مورد دیگر

Dr. James Alan Oloo is an Assistant Professor at the University of Windsor's Faculty of Education, specializing in Educational Administration, Policy, and Leadership. His research focuses on enhancing educational equity for marginalized groups, including Indigenous students, immigrants, and refugees. He holds a PhD from the University of Regina and has taught courses such as Foundations of Practice - Classroom Practice and Theories of Educational Administration and Leadership . Dr. Oloo’s research interests include narrative inquiry methods, community cultural wealth, and identity negotiation in multilingual contexts. His work examines systemic barriers faced by underrepresented students and explores strategies to improve their academic success. He has contributed to policy analysis in Ontario, critiquing initiatives addressing racial disparities in education. His publications span topics like racialized mathematics experiences, immigrant student identity negotiation, and Indigenous teacher preparation. He has received grants for research projects and collaborates with institutions such as the University of Nebraska-Lincoln, focusing on multilingual education and culturally responsive teaching practices. Prior to academia, Dr. Oloo was a high school teacher in Kenya. He holds additional degrees from Simon Fraser University, the University of Lethbridge, and Maseno University College.

Mike Picklesمشاهده پروفایل

استادیار

• HIV
• sexually transmitted and blood-borne infections
• mathematical modelling
• +۳ مورد دیگر

Dr. Mike Pickles is an Assistant Professor at the Department of Community Health Sciences, Max Rady College of Medicine, University of Manitoba. He concurrently serves as an Advanced Research Fellow at Imperial College London, UK. His research focuses on mathematical modeling to forecast epidemic trajectories and evaluate interventions targeting HIV and related infections, integrating social and structural factors influencing risk behaviors. Education: PhD in Theoretical Physics (Cambridge University, 2002), Certificate of Advanced Study in Mathematics (Cambridge, 1999), BA (Hons) Mathematics (Cambridge, 1998). Professional history includes roles at Oxford University and the London School of Hygiene and Tropical Medicine. Research interests emphasize global public health, mathematical modeling applications, and key populations affected by HIV. Collaborations involve evaluating HIV self-testing programs and prevention initiatives. Currently not accepting graduate students. Affiliated with Imperial College London and active in multidisciplinary research teams focused on infectious disease dynamics. Contact via michael.pickles@umanitoba.ca or m.pickles@imperial.ac.uk .

• Instructional Technology
• Online Education
• Digital Divide
• +۳ مورد دیگر

Alireza Moghaddam is an Associate Professor of Educational Technology with over 20 years of teaching experience across elementary schools, middle schools, high schools, universities, and teachers' colleges. His research focuses on instructional technology, online education, digital divide, collaborative education, knowledge construction, and lesson study. He holds 12 years of postsecondary education in the field of Education. His work emphasizes integrating technology into pedagogical practices and fostering inclusive teaching environments through self-study and collaborative inquiry. Recent research highlights include exploring cyberloafing in academic settings and leveraging Web 2.0 tools for knowledge co-construction in higher education. No scientific awards or grants are explicitly mentioned in the provided information. His articles consistently address themes of online collaboration, pedagogical innovation, and teacher professional development. Alireza has advised no listed students, though his teaching spans multiple educational levels.

Sanjeev Seahraمشاهده پروفایل

استاد

• Theoretical Physics
• Gravitational Theory
• Cosmology
• +۱ مورد دیگر

Dr. Sanjeev Seahra is a Professor in the Department of Mathematics and Statistics at the University of New Brunswick, where he also serves as Dean of Science. His research in theoretical physics examines gravitational theory, cosmology, and quantum gravity, with focus on modified gravity models, cosmological singularities, and quantum aspects of spacetime. His research investigates alternatives to Einstein's general relativity, including conformal gravity and quadratic gravity theories. He explores cosmological implications of these models such as bouncing universe scenarios, inflation mechanisms, and dark energy explanations. His work also examines quantum gravity effects on blackbody radiation and primordial fluctuations. Dr. Seahra's publications demonstrate expertise in analyzing gravitational wave propagation in modified gravity, resolving cosmological singularities, and developing quantum cosmological models. His recent work includes applications of contact tracing algorithms during the COVID-19 pandemic and foundations of semiclassical gravity. He has received prestigious awards including the W. B. Pearson Medal and Canada-UK Millennium Research Fellowship. As Director of AARMS, he promotes industrial collaborations and STEM outreach initiatives like the AllSySTEMsGo program for youth. He teaches courses in mathematical physics and mentors students in theoretical physics research.

Youmin Tangمشاهده پروفایل

استاد

• Climate Prediction
• Numerical Modeling
• ENSO Dynamics
• +۲ مورد دیگر

Dr. Youmin Tang is a Professor in the Department of Geography, Earth, and Environmental Sciences at the University of Northern British Columbia. His research utilizes sophisticated numerical models and mathematical tools to predict seasonal climate variability, with particular focus on ENSO (El Niño Southern Oscillation) dynamics. His work aims to improve climate predictions critical for sectors like agriculture, forestry, fisheries, and energy production. Research interests center on climate modeling, prediction systems, and understanding ocean-atmosphere interactions. His work combines traditional numerical methods with emerging machine learning techniques to enhance forecast accuracy and understand multidecadal climate patterns. Publication analysis reveals strong focus on tropical climate phenomena (ENSO, IOD, monsoons), prediction methodology development (ensemble systems, machine learning approaches), and ocean-atmosphere interactions. Recent works increasingly incorporate AI techniques to address prediction challenges. Active in graduate education, Dr. Tang supervises students in Natural Resources and Environmental Studies (NRES) programs with focus areas in Environmental Science and Geography.

• Sparse Matrix Computation
• Optimization Algorithms
• High-Performance Computing
• +۲ مورد دیگر

Shahadat Hossain is Professor and Program Chair of Computer Science at the University of Northern British Columbia. His research develops optimization algorithms for sparse matrix problems, complex networks, and high-performance computing. Key contributions include: DSJMToolkit for sparse Jacobian matrices Graph-based methods for matrix estimation Quantum-classical optimization hybrids He has supervised over 20 graduate students and secured NSERC Discovery Grants for optimization research. Hossain holds a Dr.Scient. from University of Bergen and completed visiting positions at Cornell University and Humboldt University. His 15 most-cited publications demonstrate consistent focus on sparse matrix computation, graph algorithms, and computational optimization techniques with applications in scientific computing and network analysis.

• Computational Algebra
• Programming Language Semantics
• Galois Theory

Dr. David Casperson is an Associate Professor in Computer Science at the University of Northern British Columbia. He holds a PhD in Pure Mathematics from the University of Waterloo and a BSc in Mathematical Physics from Simon Fraser University. His research explores computational algebra, Galois theory, universal algebra, and programming language semantics. He has expertise in both formal and natural languages including Java, Haskell, and C++.

Asish Mukhopadhyayمشاهده پروفایل

استاد

• Bioinformatics Algorithms
• Geometric Optimization
• Graph Algorithms
• +۲ مورد دیگر

Asish Mukhopadhyay is a Professor in the School of Computer Science at the University of Windsor's Faculty of Science. His research develops algorithms for computational geometry, graph theory, and bioinformatics, emphasizing optimization and approximation techniques. Research focuses on geometric point placement problems, linear graph layouts, density segment analysis, and algorithms for molecular biology applications. Methodologies integrate combinatorial optimization with spatial computing principles. Publications demonstrate consistent focus on geometric embedding, graph algorithms, and molecular structure analysis. Earlier work includes investigations of Ras protein membrane organization and caveolar complex formation. No awards, student advising, grants, or lab affiliations are mentioned in the source text.

• Cognitive Modelling & Computing
• Computational Intelligence
• Evolutionary Algorithms
• +۷ مورد دیگر

Dr. Aznam Yacoub is an Assistant Professor at the School of Computer Science, University of Windsor. His research focuses on cognitive modelling, computational intelligence, and formal methods in system engineering. He holds a Ph.D. in Computer Science from Aix-Marseille Université (2016). His work integrates simulation and formal verification techniques, particularly through extensions of PROMELA (DEv-PROMELA) for discrete-event systems. Key research areas include evolutionary algorithms, software/system engineering methodologies, and modelling complex systems such as mobile ad-hoc networks and virtual game architectures. He explores human-machine interaction, extended reality (metaverse), and computational philosophy. His educational interests span computer science pedagogy and the theory of computation. Dr. Yacoub's recent work emphasizes interdisciplinary applications like disaster response infrastructure simulation and adaptive storytelling in virtual environments. His technical contributions bridge formal verification with simulation frameworks, enhancing reliability in complex system design.

Glyn Georgeمشاهده پروفایل

دانشیار

• Probability
• Astronomy
• Hydrology
• +۳ مورد دیگر

Dr. Glyn George is an Associate Professor in the Department of Electrical and Computer Engineering at Memorial University of Newfoundland, within the Faculty of Engineering and Applied Science. He holds a B.Sc. from the University of Southampton, a Ph.D. in Astronomy from Cardiff University, and a Certificate in Education. His research focuses on probability applications in astronomy, hydrology, and engineering education. Dr. George has extensive teaching experience in the UK, Bahrain, and Canada, covering courses in mathematics, statistics, and programming. He is a Fellow of the Royal Astronomical Society and the Institute of Mathematics and its Applications. He has received multiple awards, including the President’s Award for Distinguished Teaching (2016) and Honorary Membership from PEGNL (2018). Dr. George has served in numerous academic roles, including Senate membership and committee leadership. His community involvement includes volunteer roles in K-12 education, serving as a school board member, and vice-chair of the College of Registered Nurses of Newfoundland and Labrador. His contributions to educational governance include drafting constitutions for school councils and advocating for educational reforms.

Alex Bihloمشاهده پروفایل

دانشیار

• Geometric Numerical Integration
• Differential Equations
• Machine Learning
• +۶ مورد دیگر

Dr. Alex Bihlo is an Associate Professor at the Department of Mathematics and Statistics, Memorial University of Newfoundland, with a cross-appointment to the Department of Computer Science (2021–2024). He holds a Tier II Canada Research Chair in Numerical Analysis and Scientific Computing (2015–2025) and is affiliated with Memorial’s Centre for Numerical Analysis and Scientific Computing and Data Science Research Group . Research Interests: His work bridges geometric numerical integration of differential equations, symmetry/conservation law preservation in numerical schemes, and machine learning for geophysical fluid dynamics. Key applications include weather prediction, tsunami modeling, and turbulence parameterization. Recent efforts focus on physics-informed neural networks (PINNs) and deep operator networks (DeepONets) that conserve physical properties. Publications & Awards: With over 30 peer-reviewed articles, his 2025 Physics-informed neural networks for tsunami inundation modeling and 2024 PinnDE paper demonstrate cutting-edge integration of ML and classical physics. Awards include the CAIMS-PIMS Early Career Award (2018) and Austria’s Sub Auspiciis Praesidentis distinction (2011). He has supervised 15+ PhD and MSc students across numerical methods, ML applications, and geophysical modeling. Grants & Collaborations: Funded by NSERC, CRC, and IgniteR&D, he collaborates with institutions including UBC, McGill, University of Vienna, and Imperial College London. His teaching portfolio spans advanced scientific programming, deep learning, and geometric numerical integration.

Emmanuel Lorinمشاهده پروفایل

استاد

• Partial Differential Equations
• Numerical Analysis
• Mathematical Modeling
• +۳ مورد دیگر

Emmanuel Lorin is a Professor at Carleton University’s School of Mathematics and Statistics in Ottawa, Canada. He is affiliated with the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS). His research focuses on Partial Differential Equations, Numerical Analysis, Mathematical Modeling, and Scientific Machine Learning applied to Quantum Physics. He actively participates in international conferences such as SIAM, ICOSAHOM, and ICIAM, showcasing his contributions to computational methods and applied mathematics. Prof. Lorin’s academic profile includes roles in editorial boards and teaching advanced courses like MATH 4701/5407 (Advanced Computational Methods) and Numerical Linear Algebra. His work spans theoretical analysis (e.g., quasi-linear hyperbolic systems, spectral methods) and computational techniques (e.g., parallel computing, domain decomposition). Research Highlights: Quantum systems, nonlocal PDEs, and high-performance computing. Recent Activities: Participation in events like the 2023 ICIAM in Tokyo and the 2022 IPAM program on Quantum Mechanics. No specific grants or awards are listed, but his involvement in major conferences underscores his professional standing.

Stephane Durocherمشاهده پروفایل

استاد مدعو

• Computational Geometry
• Discrete Algorithms
• Data Structures
• +۳ مورد دیگر

Stephane Durocher is an Adjunct Professor at Carleton University and a Professor in the Department of Computer Science at the University of Manitoba, Canada. His primary research focuses on computational geometry, discrete algorithms, and data structures, with applications in geometric optimization and wireless communication models. He holds a PhD from the University of British Columbia (2006). Roles: Adjunct Professor (Carleton), Professor (University of Manitoba) Research: Geometric algorithms, data structures, wireless network models, and algorithm design Teaching: Recent courses include Advanced Data Structures, Computational Geometry, and Automata Theory Labs: Leads the GADA Lab (Geometric Algorithms and Data Structures Laboratory) He has supervised over 20 graduate and undergraduate students, with active collaborations in computational geometry and theoretical computer science. His work is funded by NSERC Discovery and Accelerator grants, and he has served on program committees for major conferences like LATIN, CCCG, and WALCOM.

• Computational Complexity
• Algorithm Design
• Cognitive Science
• +۴ مورد دیگر

Dr. Harold Wareham is a tenured Professor in the Department of Computer Science at Memorial University of Newfoundland (MUN), Faculty of Science. He holds a Ph.D. from the University of Victoria and has been at MUN since 1999, advancing through roles including Assistant Professor, Associate Professor, and Full Professor. His research focuses on computational complexity theory applied to cognitive science, robotics, and computational biology. He has served on numerous academic committees and international conference program committees. Education: B.Sc. (Computer Science) and B.A. (Linguistics/Mathematics) from MUN, followed by an M.Sc. in Computational Biology and a Ph.D. in Parameterized Complexity in Computational Phonology from the University of Victoria. Research Interests Computational complexity of cognitive processes Algorithm design for robotics and swarm control Parameterized complexity in biological systems Machine learning interpretability Recent Work Trends Recent publications emphasize applying complexity theory to AI interpretability (e.g., neural network reverse-engineering) and cognitive science (segmentation processes). His robotics research explores algorithmic foundations for swarm construction tasks. He also investigates computational challenges in software reconfiguration and modularization. Awards 2017 Best Professor Award (MUN CS Graduate Society) Teaching Fellow at Radboud University (2014–) F. A. Aldrich Alumni Graduate Scholarship (1989) Grants & Advising Has supervised numerous courses across undergraduate and graduate levels, including AI topics, computational biology, and theoretical computer science. Active in grant review roles for NSERC and international funding bodies. Served on MUN committees for promotion, tenure, and graduate studies. Labs & Collaborations Associated with the MUN CS Theory Group and the Bio-inspired Robotics (BOTS) Lab. Collaborates internationally on projects like the Dagstuhl Complexity Workshops and cognitive science initiatives.