Find Academic Supervisors

David B. LindellView profile

Assistant Professor

• Computational Imaging
• Computer Vision
• Non-Line-of-Sight Imaging
• +5 more

David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.

Abbas SamaniView profile

Professor

• Biomedical Engineering
• Medical Imaging
• Computational Modeling
• +5 more

Prof. Abbas Samani is a Professor at the Department of Electrical and Computer Engineering and holds a joint appointment in the Department of Medical Biophysics at Western University . He is a core faculty member of the Biomedical Engineering Graduate Program and an Associate Scientist at Imaging Research Laboratories of Robarts Research Institute . His academic journey includes a Ph.D. from the University of Waterloo, an M.Sc. from the University of Tehran, and a B.Sc. from Amirkabir University of Technology. His research focuses on biological tissue computational modeling and its applications in medical imaging, intervention, and image analysis . He develops computer/image-assisted tools for minimally invasive disease diagnosis and therapy , targeting heart disease, cancer, and lung disease . Key projects include myocardium biomechanical modeling , handheld medical devices for breast cancer screening , and lung disease diagnostics via CT image segmentation . His recent publications emphasize ultrasound elastography , finite element modeling , and inverse problems in biomechanics , primarily in journals like IEEE Transactions on Computational Imaging and Translational Oncology . His work spans both computational modeling and medical device development . Selected Graduate Supervision : Ph.D. Candidates : Seyed Hassan Haddad, Elham Karami, Seyed Mohammad Hesabgar Graduated Ph.D. Students : Ali Sadeghi Naini, Seyed Reza Mousavi M.Sc. Students : Cristian Linte, Patrick Courtis, Joseph O'Hagan, Hatef Mehrabian, Hirad Karimi, Hosein Amooshahi, Seyed Mohammad Hesabgar, Nastaran Ghadarghadr, Shadi Shavakh, Ehsan Salamati, Ehsan Omidi Teaching Contributions : Graduate: BME9519B/CAMI9519B/ECE9202B/ECE9022B - Advanced Image Processing and Analysis , MBP9530A - Human Biomechanics and Biomedical Applications Undergraduate: ECE4438B - Advanced Image Processing and Analysis , ES1050 - Introductory Engineering Design and Innovation Studio , MBP3330F - Human Biomechanics and Biomedical Applications Research Affiliations : Robarts Research Institute - Associate Scientist at Imaging Research Laboratories Western University - Core Faculty, Biomedical Engineering Graduate Program

Ting HuView profile

Associate Professor

• Artificial Intelligence
• Bioinformatics
• Data Mining
• +7 more

Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods

Ankush AgarwalView profile

Associate Professor

• Mathematical finance
• Data science in finance
• Financial statistics
• +3 more

Ankush Agarwal is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. His research focuses on mathematical finance, financial statistics, and Monte Carlo methods, with applications to risk management and derivatives pricing. He supervises PhD students in quantitative finance and has taught courses on Monte Carlo methods and advanced financial modeling at Western University. Education: PhD in Mathematics from Tata Institute of Fundamental Research (2015) Research interests span regime-switching models, longevity risk hedging, stochastic differential equations, and rare event simulation. His work combines theoretical probability with computational techniques for financial applications. Recent publications include studies on McKean-Vlasov SDEs, implied Sharpe ratio estimation, and optimal portfolio strategies under stochastic volatility. These works demonstrate his expertise in stochastic processes and financial engineering. Supervision: Current PhD advisees include Ying Liao, Buchun Wang, and Shuya Zhang at the University of Glasgow. Former advisees include Yongjie Wang and Yihan Zou.

Majid BahramiView profile

Professor

• Sustainable Thermodynamic Energy Systems
• Waste-Heat Driven Sorption Technology
• Energy Storage Systems
• +7 more

Majid Bahrami is a Professor and Graduate Student Supervisor in the School of Mechatronic Systems Engineering at Simon Fraser University (SFU) , part of the Faculty of Applied Sciences . He holds the Tier 1 Canada Research Chair in Alternative Energy Conversion Systems and is a Fellow of ASME and Fellow of CAE . His research focuses on sustainable thermodynamic energy systems, waste-heat utilization, energy storage, and advanced thermal technologies. Education: - Ph.D., University of Waterloo, Canada (2004) - M.Sc., Amir Kabir University of Technology, Iran (1995) - B.Sc., Sharif University of Technology, Iran (1992) Research Interests: - Sorption-based thermal energy storage and heat pumps - Waste-heat recovery systems - Multiscale transport phenomena in functional materials - Biomimetic heat transfer enhancement - Advanced materials for energy applications Recent Contributions: - Developed novel sorption heat exchangers and graphite-based heat sinks. - Pioneered atmospheric water harvesting technologies (HAWGen system). - Authored over 150 peer-reviewed articles and holds multiple patents. - Collaborates with industries like Watergenics and AFCC . Awards & Recognition: - Canada’s Clean50 (2016, 2017) - Innovate BC Award (2018) - RBC Award (2024) - Inducted into Canadian Academy of Engineering (2019) Lab & Teams: - Lab for Alternative Energy Conversion (LAEC) : Focuses on sustainable energy systems. - Supervised over 50 graduate students, many securing academic and industry roles. - Active in NSERC CREATE HyTEM and Hybrid Thermal Electric Microgrid initiatives.

Giang TranView profile

Associate Professor

• Sparse Modeling
• Compressed Sensing
• Image Processing
• +2 more

Dr. Giang Tran is an Associate Professor in the Department of Applied Mathematics at the University of Waterloo, where she leads research in sparse modeling and computational mathematics. She holds a PhD from UCLA and previously served as a Bing Instructor at the University of Texas at Austin. Her research explores sparse optimization techniques with applications in medical imaging, dynamical systems, and data science. Recent publications focus on developing novel algorithms for sparse random feature expansions and dynamical system identification. She mentors numerous graduate and undergraduate researchers through projects on neural networks, transformers, and epidemic forecasting. Awards include the NSERC Discovery Grant and SIAM Student Paper Prize. Dr. Tran teaches advanced courses in numerical methods and functional analysis, contributing to curriculum development in computational mathematics.

Lyndia WuView profile

Assistant Professor

• Biomechanical engineering
• Biomedical instrumentation
• Biomedical signal processing
• +11 more

Lyndia Wu is an Assistant Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science, where she holds the prestigious Canada Research Chair in Wearable Brain Injury Sensing. She leads the SimPL (Sensing in Biomechanical Processes Lab) and maintains an active research program focused on biomechanics and medical device development. Her educational background includes: B.A.Sc. from the University of Toronto M.S. from Stanford University Ph.D. from Stanford University Postdoctoral Fellowship from Stanford University Dr. Wu's research program centers on developing novel sensing and data analytics technologies to study human biomechanics in health and disease states. Her primary research areas encompass brain injury or concussion biomechanics using advanced sensing, modeling, and machine learning approaches, as well as the development of innovative sensors and algorithms for studying sleep disorders like obstructive sleep apnea. She specializes in wearable sensors for brain health monitoring, traumatic brain injury mechanisms, and AI applications in healthcare settings. Analysis of her recent publications reveals a strong focus on sports-related head impacts (particularly in soccer), EEG monitoring following impacts, and sleep monitoring after concussions. Her work demonstrates interdisciplinary collaboration across biomechanical engineering, neuroscience, and clinical medicine, with publications spanning biomechanics, neurotrauma, biomedical instrumentation, and signal processing domains. Dr. Wu has received significant recognition for her work, including: Scholar Award from the Michael Smith Foundation for Health Research (2019) Junior Faculty Teaching Award from UBC Mechanical Engineering (2022) She actively supervises graduate students in Mechanical Engineering programs (MASc and PhD) and collaborates extensively across disciplines. Dr. Wu is affiliated with multiple research centers including the Institute for Computing, Information and Cognitive Systems (ICICS), Origins of Balance Deficits and Falls, and SmarT Innovations for Technology Connected Health (STITCH), reflecting her interdisciplinary approach to solving complex biomedical challenges. As director of the SimPL lab, she leads a research team developing cutting-edge sensing solutions for biomechanical processes with particular emphasis on brain injury prevention, monitoring, and recovery assessment through innovative engineering approaches.

Luis A. Ricardez-SandovalView profile

Associate Professor

• Process Systems Engineering
• Multiscale Modelling
• Computer-aided Catalyst and Materials Design
• +7 more

Luis A. Ricardez-Sandoval is an Associate Professor in the Department of Chemical Engineering at the University of Waterloo and holds a Tier II Canada Research Chair in Multiscale Modelling and Process Systems. His research group develops advanced computational tools for optimizing chemical processes across multiple scales. Doctorate: Chemical Engineering, University of Waterloo (2008) MASc: Chemical Engineering, Instituto Tecnologico de Celaya (2000) BASc: Chemical Engineering, Instituto Tecnologico de Orizaba (1997) The research group focuses on multiscale modelling and process systems engineering , particularly for CO2 capture , energy systems , and heterogeneous catalysis . Their work combines advanced mathematics, machine learning , and uncertainty analysis to optimize chemical processes before physical implementation. Recent publications emphasize dynamic system optimization under uncertainty, multiscale simulation , and CO2 conversion technologies . Key methodologies include probabilistic uncertainty quantification and economic predictive control . Scientific Awards : 1997: First Place, XII National Creativity Contest 1998: Best Student Award, Instituto Tecnologico de Orizaba 1999: Third Place, XIV National Creativity Contest 2000: J.M. Smith Award for Best MASc Student He has collaborated with international institutions like CONACyT-Mexico, China Scholarship Council, and Universidad de Los Andes. His teaching includes graduate courses in process control, optimization, and computer-aided design.

David MouldView profile

Professor

• Computer Graphics
• Computer Games
• Non-Photorealistic Rendering
• +4 more

David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.

Chris SimpsonView profile

Professor

• Health Policy
• Cardiology
• Medical Imaging AI
• +7 more

Dr. Chris Simpson is a Professor at Queen’s University and currently serves as Executive Vice President & Chief Medical Executive at Ontario Health. He previously held roles as Head of Cardiology (2006-2016) and Vice-Dean (Clinical) of the Faculty of Health Sciences (2016-2021). His expertise spans cardiology, health policy, and medical imaging AI. Simpson has authored over 350 peer-reviewed publications and holds fellowships in multiple medical societies. His research focuses on health policy (access to care, wait times), cardiac electrophysiology, and AI-driven medical imaging. Education: BSc (University of New Brunswick), MD (Dalhousie University), Cardiac Electrophysiology Fellowship (University of Western Ontario). Notable roles include CMA President (2014-2015), CAHS President (2020-2021), and leadership in organizations like CorHealth and SEAMO. Awards include Fellowships in the Canadian Academy of Health Sciences and American College of Cardiology. Research interests include medical fitness to drive, atrial fibrillation, sudden cardiac death, and AI applications in oncology imaging. He leads initiatives such as the Terry Fox Research Institute’s Marathon of Hope Network. Simpson also contributes to policy bodies including Ontario Health’s Quality Standards Committee and the World Medical Association.

Poman SoView profile

Associate Professor

• Multiscale Modeling
• Biomedical Engineering
• Microwave CAD
• +5 more

Poman So is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Victoria, Canada. He is a Professional Engineer (P.Eng) registered in British Columbia and holds memberships in IEEE (Senior Member), ACES, and CMBES. Dr. So co-founded Faustus Scientific Corporation, creator of MEFiSTo electromagnetic field modeling software. Prior to joining UVic in 1998, he worked as senior scientific staff at MDA in Quebec. Education: Ph.D. in Electrical Engineering (University of Victoria) M.A.Sc. and B.A.Sc. in Electrical Engineering (University of Ottawa) B.Sc. (University of Toronto) Research Interests: Focuses on multiscale electromagnetic field modeling, biomedical electromagnetics and instrumentation, time-domain microwave circuit analysis, and open-source computational electromagnetics (CEM) tools. His work integrates GPU-based parallel computing and meta-material applications in microwave CAD. Professional Activities: Administers the Talbot Memorial Fund Awards, established to honor engineering students based on academic excellence, community service, and leadership. Since 2007, the fund has awarded two $2500 scholarships annually. Teaching: Instructs courses including ECE 216 (Electricity and Magnetism), ECE 335 (Biosensors and Instrumentation), and ECE 521 (Computational Electromagnetics). Labs/Teams: Leads research in biomedical engineering instrumentation and microwave circuit design through Faustus Scientific’s MEFiSTo platform, emphasizing open-source software development.

Yun ZhangView profile

Professor

• Remote Sensing
• Image Processing
• Computer Vision
• +4 more

Dr. Yun Zhang is a Professor and Canada Research Chair in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick. He holds a PhD from the Free University of Berlin and has pioneered research in remote sensing, image processing, and computer vision since 2000. His patented technologies are licensed to global companies including PCI Geomatics and DigitalGlobe. Research Focus: Optical/radar image processing, digital photogrammetry, AI applications in geomatics, and sensor fusion for UAV systems. His work enables advanced geospatial analysis across environmental, urban, and defense sectors. Distinctions: First Giuseppe Inghilleri Award (ISPRS 2012) NSERC Synergy Innovation Award from Governor General of Canada (2011) ASPRS Talbert Abrams Grand Award (2005) Featured in CFI 20th Anniversary Book for breakthrough innovations Technology Impact: Solutions deployed by NASA, USGS, Google Earth, and DND Canada across five continents. Recognized among top 9 Canadian research achievements in AUTM's global case studies alongside MIT and Stanford innovations.

William R. CluettView profile

Professor

• System Identification
• Process Control
• Systems Biology
• +4 more

William R. Cluett is a Professor at the University of Toronto's Department of Chemical Engineering & Applied Chemistry within the Faculty of Applied Science and Engineering. He holds a B.Sc. from Queen’s University and a Ph.D. from the University of Alberta, and is a licensed Professional Engineer (P.Eng). Currently serving as Dean’s Advisor on Innovations in Undergraduate Education, Cluett bridges engineering principles with systems biology in his research. B.Sc., Queen’s University Ph.D., University of Alberta Cluett's research spans traditional process control and design, extending into systems biology where he collaborates with Professor Krishna Mahadevan. His work focuses on integrating engineering methodologies with biological systems, including multiscale modeling, dynamic metabolic engineering, and computational toxicology. His publications highlight trends in applying control theory to metabolic networks, developing algorithms for genome-scale modeling, and designing bistable cell factories. These contributions reflect interdisciplinary efforts between chemical engineering and computational biology. Scientific Awards & Recognitions: Fellow of Engineers Canada (2021) Medal for Distinction in Engineering Education (2021) OCUFA Teaching Award (2020) President’s Teaching Award (2018) Sustained Excellence in Teaching Award (2016) Bill Burgess Teacher of the Year Award (2014) Fellow, AAAS (2009) Fellow, Chemical Institute of Canada (1998) Syncrude Canada Innovation Award (1997) Cluett has contributed to major grants and collaborative projects in systems biology and metabolic engineering. He actively advises on undergraduate education innovations and maintains strong affiliations with the Department of Chemical Engineering & Applied Chemistry.

• Hydrogeology
• Mine Drainage
• Groundwater Contamination
• +4 more

Roger Beckie is a Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), part of the Faculty of Science. His research focuses on physical, geochemical, and biological processes in environmental systems, particularly in hydrogeology, mine drainage, and groundwater contamination. He holds a B.A.Sc. from the University of Waterloo and a Ph.D. from Princeton University, and is a Professional Engineer (P.Eng.). Key research areas include fugitive gas migration from energy wells, mine waste rock geochemistry, and groundwater hydrology. His work addresses challenges such as acid rock drainage prediction, metal attenuation mechanisms, and the impacts of subsurface gas migration in petroleum development regions. He collaborates with industry and government to advance environmental management strategies and has contributed to large-scale field experiments, including controlled gas release studies in northeastern British Columbia. Beckie supervises graduate students in interdisciplinary projects, emphasizing field investigations and numerical modeling. He is affiliated with UBC's Institute of Applied Mathematics and teaches advanced courses in groundwater hydrology and contamination. His research integrates hydrological, geochemical, and isotopic tools to address complex environmental systems, with applications in mining, oil and gas, and groundwater sustainability.

Yasser Iturria MedinaView profile

Assistant Professor

• Neuroimaging
• Neuroinformatics
• Precision Medicine
• +6 more

Yasser Iturria Medina is an Assistant Professor at the Montreal Neurological Institute (MNI) , McGill University, within the Department of Neurology and Neurosurgery . He is an associate member of the Ludmer Centre for Neuroinformatics and Mental Health and the McConnell Brain Imaging Centre . Academic Rank: Assistant Professor Key Affiliations: MNI, Ludmer Centre, McConnell Brain Imaging Centre His educational background includes: Undergraduate: Nuclear Engineering (2004), Higher Institute for Nuclear Sciences and Technology, Cuba MSc: Neurophysics and Neuroengineering (2006), Cuban Neuroscience Center PhD: Neuroimaging and Neuroinformatics (2013), National Center for Scientific Research and Havana’s University of Medical Science His research focuses on neuroinformatics for precision medicine , particularly in neurodegenerative diseases like Alzheimer's and Parkinson's. His lab develops multiscale brain models integrating molecular, imaging, and cognitive data to characterize pathogenic mechanisms and identify personalized interventions. Key research areas include: Neurodegeneration modeling Neurovascular interactions in Alzheimer's Multi-omics integration for disease subtyping Neuroimaging biomarkers across neurodegenerative spectra Computational modeling of amyloid-beta and tau propagation The article analysis reveals his emphasis on: Alzheimer's disease mechanisms (45% of recent works) Multi-omics and transcriptomic modeling (30%) Neurovascular and white matter pathology (20%) Machine learning applications in neuroimaging (15%) Development of tools like NeuroPM-box and MVComp toolbox His lab has been instrumental in creating NeuroPM-box , a software platform for integrating molecular, neuroimaging, and clinical data to characterize neurodegenerative progression and heterogeneity. He has also contributed to CAPTURE ALS , a comprehensive analysis platform for amyotrophic lateral sclerosis.