Find Academic Supervisors

Anmar KhadraView profile

Professor

• Mathematical Biology
• Computational Biology
• Complex Systems
• +12 more

Anmar Khadra is a Full Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research integrates mathematical and computational approaches to study complex biological systems across neuroscience, immunology, and cell biology, with a strong emphasis on developing predictive models that bridge multiple scales of biological organization. Dr. Khadra's research focuses on the interface of complex systems, nonlinear and stochastic dynamics, biophysics, numerical computations, and artificial intelligence. His work employs multiscale complex systems and computational AI-based models to study cellular and network dynamics in various physiological systems. Key research areas include: Neural rhythms and ion channel regulation of neural excitability System-level neural responses during rest and upon receiving stimuli Spontaneous and evoked calcium responses in neuronal and other cellular systems T cell activation mechanisms by pMHC-coated nanoparticles Cellular migration patterns and protein network regulation His recent publications (2024-2025) demonstrate a sophisticated integration of biophysical modeling with experimental validation across multiple physiological domains. The research spans from molecular-level investigations of ion channels to system-level analyses of neural populations and immune responses. A notable trend is the development of computational tools like ElecFeX for electrophysiological data analysis, alongside theoretical frameworks for understanding complex biological phenomena such as the sensitivity-specificity trade-offs in T cell responses and neural coding mechanisms. Dr. Khadra leads the Khadra Lab, which develops computational models that closely adhere to physiological properties of biological systems. These models bridge multiscale components and predict emergent behavior, generating testable predictions that guide experimental work conducted by collaborators. His research has significant implications for understanding fundamental biological processes as well as potential applications in neurological disorders, immune-related conditions, and cellular dysfunction.

Miroslav GrmelaView profile

Researcher

• Thermodynamics
• Transfer processes
• Mesoscopic dynamics
• +3 more

Miroslav Grmela is a Researcher at the Department of Chemical Engineering in Polytechnique Montréal , and a member of the Research Center for High-Performance Polymer and Composite Systems (CREPEC) . His work spans thermodynamics, transfer processes, and multiscale modeling of complex fluids. Grmela’s research focuses on non-equilibrium thermodynamics , contact geometry in kinetic dynamics , and mesoscopic theories of polymer suspensions, superfluids, and nanocomposites. He has extensively explored the role of energy and entropy in multiscale systems, with recent publications addressing geometric formulations of thermodynamics and neural network applications to non-symplectic mechanics. Analysis of his 15 most recent articles reveals trends in multiscale thermodynamics , non-Fourier heat conduction , GENERIC formalism , and quantum hydrodynamics . His work often bridges geometric mechanics with thermodynamic consistency. Grmela has supervised 10 graduate students (7 PhD, 3 Master’s) in projects involving nanocomposite thermal conductivity , polymer rheology , and powder suspension simulations . He has no listed scientific awards. His research intersects with fluid dynamics , polymer science , and statistical mechanics , emphasizing mathematical structures like Poisson brackets and Hamiltonian formulations . The CREPEC laboratory provides institutional support for his studies on polymers and composites.

Mehdi PouraghaView profile

Associate Professor

• Geomechanics
• Constitutive Modeling
• Granular Materials
• +7 more

Mehdi Pouragha is an Associate Professor in the Department of Civil and Environmental Engineering at Carleton University's Faculty of Engineering and Design in Ottawa, Canada. He holds a BSc and MSc from Sharif University of Technology in Iran and a PhD from the University of Calgary. His academic career focuses on advanced geomechanics research with applications in both theoretical and practical engineering contexts. Dr. Pouragha's research spans multiple interconnected areas within geomechanics, with particular emphasis on constitutive modeling of geomaterials, micromechanics of granular materials, thermo-hydro-mechanical behaviors, and computational geomechanics. His work integrates theoretical approaches with advanced numerical methods to address complex problems in soil mechanics, permafrost engineering, and unsaturated soil behavior. His research program bridges fundamental micromechanical understanding with practical engineering applications, particularly in cold regions and climate change adaptation contexts. Analysis of Dr. Pouragha's recent publications reveals a strong trend toward multiscale modeling approaches that integrate discrete element methods with continuum mechanics. His work increasingly incorporates machine learning techniques to enhance computational efficiency while maintaining physical accuracy. The research spans both fundamental theoretical developments in constitutive modeling and practical applications in permafrost engineering, tailings management, and soil-structure interaction problems. His publications consistently demonstrate a focus on connecting microscale mechanisms to macroscale material behavior. Dr. Pouragha teaches several core civil engineering courses including Geotechnical Engineering, Fundamentals of Geomechanics, Numerical Methods in Geotechnical Engineering, and Professional Practice. His teaching portfolio reflects his research expertise while providing students with both theoretical foundations and practical engineering skills.

Ben AshbyView profile

Associate Professor

• Mathematical Biology
• Ecology and Evolution
• Host-Parasite Interactions
• +5 more

Ben Ashby is an Associate Professor in the Department of Mathematics at Simon Fraser University's Faculty of Science. His research focuses on mathematical biology, particularly the ecology and evolution of host-parasite interactions. He uses mathematical models to study parasite spread, virulence evolution, and the impact of host traits like resistance or mating strategies. Education: DPhil in Zoology from the University of Oxford (2014). Research Interests: Ben’s work spans infectious diseases, the evolution of diversity, sexual selection, and niche evolution. He explores how traits evolve in response to pathogens and how ecological factors influence evolutionary dynamics. His models address topics like defensive symbionts, age-structured resistance, and coevolutionary timescales. Labs/Teams: He is part of the Mathematics, Genomics & Prediction in Infection & Evolution (MAGPIE) group at SFU, applying mathematical approaches to infectious disease challenges. Teaching: Currently teaches MATH 360 D100 (Introduction to Biomathematics) in Fall 2025. Past courses include advanced topics in epidemiological modeling and evolutionary dynamics. Office & Contact: Located in K9525, reachable at ben_ashby@sfu.ca or via his research website ecoevotheory.com .

Dazhi JiangView profile

Professor

• Structural Geology
• Tectonics
• Geomechanics
• +3 more

Dr. Dazhi Jiang is a Professor at Western University's Department of Earth Sciences and a Distinguished Professor at Northwest University, China. His research focuses on linking structural geology with tectonics through microstructural analysis, numerical modeling, and fieldwork. He develops self-consistent micromechanical models to study multi-scale deformation in Earth's lithosphere, with applications to regions like the Canadian Shield and North China Craton. His work integrates field observations, lab experiments, and computational methods to understand large-scale tectonic processes. His research interests include deformation mechanisms, fabric development, and rheological heterogeneity. Key contributions include studies on metamorphic core complexes, quartz flow laws, and viscous inclusion dynamics. Dr. Jiang teaches courses in structural geology, field mapping, and continuum micromechanics at Western and collaborating institutions. Scientific recognition includes his Distinguished Professor title at Northwest University. He actively supervises graduate students and seeks candidates for projects in the Canadian Cordillera and East China, focusing on microstructure evolution and cratonic thinning mechanisms. Publications emphasize numerical modeling, tectonic deformation, and rheological analysis. His lab, the Structural Geology & Tectonics Laboratory, supports advanced research in deformation mechanics and lithospheric dynamics.

Tien D. BuiView profile

Professor

• Digital Image Processing
• Handwritten Document Analysis
• Signal Processing
• +4 more

Tien D. Bui is a Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal, Canada. He has been affiliated with Concordia for over 30 years, having joined the university in 1984 as a full professor. Previously, he held positions at McGill University and earned his education from Carleton University, the University of Ottawa, the University of Toronto, and York University. His roles include Chair of the Department of Computer Science (1985–1990) and Associate Vice-Rector Research (1992–1996). He currently serves on multiple research boards, including the Board of Governors at Concordia University and editorial roles for journals like the International Journal of Wavelets, Multiresolution and Information Processing. His research focuses on image processing, document analysis, machine learning, and pattern recognition. Notable contributions include work on wavelet-based image segmentation, handwritten document analysis, and applications of sparse representations in texture classification. He has supervised over 20 graduate students and authored/co-authored over 150 publications, including the book Computer Transformation of Digital Images and Patterns (1989). Bui has held visiting positions at the Istituto per le Applicazioni del Calcolo in Rome and the University of California, Berkeley. He has received significant research grants and served on numerous grant selection committees and federal/provincial panels. His work has practical applications in medical imaging, biometrics, and computational fluid dynamics. Current lab members include doctoral and master’s students focusing on advanced topics like control systems and algorithm design. Bui’s academic leadership extends to teaching courses such as Control Systems and Applications, Mathematics for Computer Science, and advanced design and analysis of algorithms.

• Materials Science
• Manufacturing Engineering
• Machining Technology
• +4 more

Mohammad Islam is an Assistant Professor (Part-Time) in the Department of Health Research Methods, Evidence, and Impact. He specializes in advanced materials and manufacturing processes, with a focus on PVD coatings, tribology, and machining performance optimization. His research explores the interplay between coating deposition parameters, material properties, and wear behavior in aerospace and industrial applications. He teaches Regression Analysis ( HTHRSM 753 ) courses, emphasizing statistical methodologies in health research. Key research themes include PVD coating design for Ti6Al4V alloys, ultrasonic-assisted machining, and green cooling technologies. His work addresses challenges in tool longevity, surface roughness reduction, and sustainable manufacturing practices. Recent studies investigate multiscale numerical analysis of residual stresses and self-adaptive coatings for extreme machining conditions. No scientific awards or grants are explicitly mentioned in the provided texts. No advising roles or lab affiliations are detailed. His contributions span experimental and computational approaches to enhance material performance and machining efficiency.

Jessica LinView profile

Associate Professor

• Partial Differential Equations
• Probability Theory
• Stochastic Homogenization
• +3 more

Jessica Lin is an Associate Professor in the Department of Mathematics and Statistics at McGill University, holding the Canada Research Chair in Partial Differential Equations and Probability (Tier 2). She specializes in stochastic homogenization, elliptic and parabolic equations, and reaction-diffusion systems. Prior to McGill, she was a Van Vleck Visiting Assistant Professor at the University of Wisconsin-Madison, and earned her Ph.D. from the University of Chicago in 2014 with undergraduate studies at NYU. Her research focuses on partial differential equations and probability, particularly in nonlinear phenomena, stochastic processes, and their applications to mathematical physics and materials science. Key areas include front propagation in stochastic environments, nondivergence form equations, and phase transition models in periodic media. Dr. Lin has contributed to foundational work on quantitative homogenization estimates for elliptic and parabolic equations, asymmetric cooperative motion, and barycentric Brownian systems. Her recent articles explore generalized front propagation, anisotropic surface tensions, and KPZ universality in random growth models. She has been recognized with the Canada Research Chair (Tier 2), and her work spans over 15 peer-reviewed articles since 2013. Her research combines analytical techniques with probabilistic methods, addressing multiscale problems in applied mathematics.

Caroline ColijnView profile

Professor

• Genomic Epidemiology
• Phylogenetics
• Mathematical Modeling of Infectious Diseases
• +4 more

Caroline Colijn is a Professor and Canada 150 Research Chair in the Department of Mathematics at Simon Fraser University (SFU), within the Faculty of Science. Her research focuses on the intersection of mathematics, statistics, evolution, and epidemiology, with a particular emphasis on understanding pathogen transmission and evolution using genomic data. She holds a PhD in Applied Mathematics from the University of Waterloo (2004). Her work spans genomic epidemiology, phylogenetics, and mathematical modeling of infectious diseases such as tuberculosis and SARS-CoV-2. She has contributed to tools like TransPhylo for inferring transmission trees and has advised multiple graduate students and postdoctoral researchers. Key achievements include her 2023 Fellowship from the Royal Society of Canada and her role as a leader in pandemic modeling during the COVID-19 crisis. Recent publications highlight her work on genomic clustering, transmission dynamics, and vaccine optimization. Her research often integrates genomic, clinical, and epidemiological data to inform public health strategies. Colijn’s team, the MAGPIE group, focuses on advancing methods to analyze pathogen evolution and control outbreaks.

Nicolas DoyonView profile

Professor

• Computational Neuroscience
• Mathematical Modeling
• Poisson Nernst Planck Equations
• +5 more

Nicolas Doyon is a Professor at the Department of Mathematics and Statistics , Université Laval, Canada. His research bridges mathematical modeling and neuroscience , focusing on ion transport, neural networks, and neurodegenerative diseases. He collaborates with experimentalists and companies like Doric Lenses . Education: B.Sc., Université Laval M.Sc., University of Montreal Ph.D., Theoretical Mathematics Postdoctoral Fellowship, Quebec City Institute of Mental Health Research Interests: Dr. Doyon investigates chloride homeostasis via the KCC2 cotransporter , its role in synaptic inhibition, and implications for diseases like epilepsy and autism. He develops finite element models for Poisson-Nernst-Planck equations to study electrodiffusion in complex neural geometries. Scientific Awards: Star Teacher (2016, Faculty of Science and Engineering) NSERC Individual Grant (35K$/year, 2014-2019) FQRNT Establishment Grant (20K$/year, 2013-2015) Students: Supervises Ph.D. and Master's students including Tahmineh Azizi (dynamical systems), Frank Boahen (dendritic spines), and Vincent Ouellet (analytic number theory).

Peijun GuoView profile

Professor

• Geotechnical Engineering
• Geomechanics
• Numerical Modeling
• +5 more

Dr. Peijun Guo is a Professor in the Department of Civil Engineering at McMaster University. His research focuses on geomechanics, geotechnical engineering, and numerical modeling of complex geotechnical systems, including soil behavior under cyclic loads, granular material dynamics, and climate change impacts on infrastructure. He holds a B.Sc. and M.A.Sc. from Southwest Jiaotong University (SWJTU) and a Ph.D. from the University of Calgary. Dr. Guo is a licensed Professional Engineer (P.Eng.) in Ontario. Research interests include experimental characterization of geomaterials, multiscale modeling of geo-mechanical systems, and the application of advanced numerical methods. His work addresses challenges such as hydraulic fracturing, ground thermal pumping, and railway-induced vibrations. He teaches courses like CIV ENG 704 (Special Topics in Applied Advanced Geotechnology) and CIV ENG 743 (Fundamentals of Soil Behaviour). Dr. Guo’s contributions span over 100 peer-reviewed publications, emphasizing granular materials, soil-structure interaction, and infrastructure resilience. His academic service includes advising on geotechnical projects and collaborating on initiatives like the $1M-funded research on small modular reactors. Dr. Guo’s lab focuses on advancing resilient infrastructure systems through innovative engineering solutions and interdisciplinary approaches.

Simon HespView profile

Professor

• Materials Science and Engineering
• Asphalt Materials Science
• Polymer-Modified Asphalt
• +6 more

Simon Hesp is a Professor in the Department of Chemistry at Queen’s University, Kingston, Canada, affiliated with the Faculty of Arts and Science. His research focuses on asphalt materials science, including low-temperature fracture behavior, polymer-modified binders, and pavement performance evaluation. He has led over 30 pavement trials across Ontario and collaborated with industry on sustainable materials innovations. Education: Ph.D. (1991) – Chemical Engineering and Applied Chemistry – University of Toronto M.Sc. (1987) – Chemistry – University of Toronto Ing. (1985) – Chemical Engineering and Applied Chemistry – Hogere Technische School, Amsterdam Research Interests: Materials engineering and resources engineering Functional materials in materials science Recycled plastics and bio-rejuvenation in asphalt Thermoreversible aging mechanisms Quality assurance testing protocols for asphalt binders Awards & Recognition: No explicit prizes/fellowships are listed, though his extensive industry collaborations and >$10M in research funding highlight his impact. Grants & Students: Secured ~$10M in research funding from government/industry Mentored ~100 undergraduate/graduate students Lab equipped with advanced asphalt characterization tools Labs & Facilities: Specialized asphalt testing laboratory at Queen’s University with state-of-the-art equipment for rheological analysis, thermal characterization, and failure testing.

• Computer Vision
• Machine Learning
• Statistics
• +3 more

Marcus A. Brubaker is an Associate Professor at York University's Department of Electrical Engineering and Computer Science within the Lassonde School of Engineering. He also holds an Adjunct Professor status at the University of Toronto's Department of Computer Science and serves as a Research Scientist at Google DeepMind. His primary affiliations include co-directorship of the Computational Vision and Imaging Lab (CVIL) at York University and membership in the Vector Institute as a Faculty Affiliate. Brubaker's research spans Computer Vision, Machine Learning, Statistics, and their applications to problems like cryo-electron microscopy (cryo-EM) and 3D molecular reconstruction. Education: He earned a Ph.D. in Computer Science from the University of Toronto (2011), with a thesis on physical models of human motion. His M.Sc. (2006) and B.Sc. (2004) in Computer Science were also from the University of Toronto. Research focuses include cryo-EM structure determination (notably the cryoSPARC software), normalizing flows, and probabilistic modeling. His work bridges computational imaging, statistical inference, and deep learning. Key contributions include advancements in cryo-EM algorithms, generative models, and camera noise analysis. He co-founded Structura Biotechnology and has contributed to open-source tools like Stan's math library. Publications emphasize novel view synthesis, neural radiance fields, sensor noise modeling, and cryo-EM reconstruction techniques. His research trends highlight integration of physics-based models with machine learning for solving complex imaging problems. Awards and recognition include impactful contributions to cryo-EM through cryoSPARC, a Nature Methods paper in 2017, and leadership in academic-industrial collaborations. Brubaker's labs and teams focus on advancing imaging technologies and their applications in structural biology and computer vision.

• Geomorphology
• Hydrology
• Remote Sensing
• +3 more

Jaclyn Cockburn is an Assistant Professor in the Department of Geography at the University of Guelph. Her research focuses on fluvial geomorphology, hydrology, and environmental processes, with an emphasis on river restoration, sediment dynamics, and climate-landscape interactions. She employs cutting-edge technologies like LiDAR and remote sensing to study aquatic ecosystems and geomorphic processes in diverse environments, including Arctic regions and urban streams. Her work spans topics such as ice-covered stream hydraulics, varve-based climate proxies, and the impacts of urbanization on river morphology. She has contributed to methodological advancements in geomorphometric analysis and digital soil mapping. Notably, she has addressed systemic issues in academia through a 2019 paper examining gender dynamics in academic careers. Dr. Cockburn's research bridges field observations, computational modeling, and policy-relevant applications. Her articles consistently address interdisciplinary challenges in environmental science, emphasizing the interplay between physical processes and ecological outcomes.

Vahid RezaniaView profile

Professor

• Biophysics
• Complex physical systems
• Dynamical systems
• +6 more

Vahid Rezania is a Professor and Chair of the Department of Physical Sciences at MacEwan University since 2004. He holds a PhD in Theoretical Physics (Institute for Advanced Studies, Iran) and has previously served as a Postdoctoral Fellow and Research Associate at the University of Alberta (2000-2005). Education: PhD in Theoretical Physics (Institute for Advanced Studies, Iran, 2000) MSc in Physics (Shiraz University, Iran, 1995) BSc in Physics (Shiraz University, Iran, 1994) His research integrates biophysics , theoretical physics , and computational modeling to study complex biological systems. Current projects include simulating drug metabolism in the liver and analyzing electrostatic properties of microtubules for cancer therapy applications. Recent publications highlight his interdisciplinary approach: 2024 work on fibrotic liver modeling , 2021 studies of microtubule conductivity , and 2020-2019 collaborations on TTField therapy for glioblastoma. These span computational biology , nonlinear dynamics , and nanobiophysics . Scientific Awards: Strategic Research Grant (2015, 2020) Research Project Grants (2010, 2014, 2018) Dissemination Grants (2008, 2010, 2018) PhD Scholarship (Iranian Ministry of Culture, 1995-2000) First rank graduate (Shiraz University, 1994) Collaborations: University of Alberta University of Calgary University of California, Santa Barbara University of Wisconsin, Milwaukee He has supervised senior student research projects and developed physics courses from first-year to advanced levels. His work bridges mathematical modeling with experimental validation across biological and quantum systems.