جستجوی استاد راهنمای دانشگاهی

Salim Ahmedمشاهده پروفایل

استادیار

• Safety and Risk Engineering
• Process Systems
• Process Control
• +۳ مورد دیگر

Salim Ahmed is an Assistant Professor in the Department of Chemical Engineering at Memorial University of Newfoundland's Faculty of Engineering and Applied Science since February 2012. He holds a B.Sc. and M.Sc. in Chemical Engineering from Bangladesh University of Engineering and Technology (BUET) and a Ph.D. in Process Control from the University of Alberta. Prior roles include a postdoctoral fellowship at the University of Alberta (2006-2008), an Assistant Professorship at Qatar University (2008-2012), and a Lecturer position at BUET (1997-2000). His research focuses on safety and risk engineering, process systems, and model-based process control. Notable projects include an IgniteR&D grant-funded risk-based alarm system for process industries and early warning systems design. His expertise spans system identification, continuous-time models, model validation, and valve stiction analysis. Teaching responsibilities include undergraduate and graduate courses in Process Engineering and Oil and Gas Engineering. Awards include the 2010 Best Student Research Award (Energy and Environment category) at the Qatar Foundation Annual Research Forum. Research outputs include over 30 peer-reviewed articles, conference papers, and book chapters, emphasizing fault detection, data-driven modeling, and risk assessment in process systems. He has developed tools like StepID for step-response identification and AlarmSoft for alarm management.

• Computational Biology
• Systems Biology
• Genomics
• +۷ مورد دیگر

Gary Bader is a Professor at The Donnelly Centre, University of Toronto, specializing in Computational Biology and Systems Biology. His research focuses on integrating molecular interaction networks, pathway analysis, and 'omics data to understand disease mechanisms and develop clinical models. He leads the Bader Lab, which develops computational tools like Cytoscape and Pathway Commons, and explores single-cell genomics to study tissue function, cancer, and regenerative processes. Education: BSc Biochemistry (McGill University), PhD Biochemistry (University of Toronto), Postdoc at Memorial Sloan-Kettering Cancer Center. Affiliations: Donnelly Centre, Department of Molecular Genetics, Department of Computer Science, Lunenfeld-Tanenbaum Research Institute, Princess Margaret Cancer Centre, and CIFAR MacMillan Multiscale Program. Research interests include causal mechanistic modeling of disease (e.g., pediatric ependymoma therapies), ecosystem theories of tissue function, and open-source bioinformatics tools. Recent work emphasizes single-cell and spatial transcriptomics to map cellular heterogeneity in diseases like cancer, liver pathologies, and neurological disorders. Collaborations span computational methods, stem cell biology, and clinical translation.

• Medical Imaging
• Machine Learning
• Biomedical Engineering
• +۵ مورد دیگر

Jason Deglint is an Adjunct Professor at the University of Waterloo, affiliated with the Adjunct Faculty group. His work bridges medical imaging technologies and environmental science, leveraging machine learning for practical applications. Research Interests include Medical Imaging, Machine Learning, Biomedical Engineering, Environmental Monitoring, and Computer Vision. He focuses on developing AI-driven tools for real-time diagnostics in critical care settings, enhancing ultrasound imaging through deep learning, and creating privacy-preserving federated learning systems for ecological data analysis. Additionally, his research addresses toxin detection using microfluidic devices and advanced microscopy techniques for spectral and morphological analysis of biological samples. His recent publications highlight a dual focus on improving medical diagnostics and environmental monitoring. In medical imaging, he has explored deep learning models for lung ultrasound artifact identification and parenchyma classification, emphasizing real-time validation in intensive care units. In environmental science, his work on phytoplankton classification and harmful algal bloom detection demonstrates the application of federated learning and synthetic dataset generation. He also investigates multispectral microscopy and signal processing methods for non-invasive health measurements, as seen in projects like SAPPHIRE and SALT. Scientific Awards: Distinguished Paper Award (2015) In advising, Jason Deglint does not currently list any formal advisees. Details about grants or funding are not provided in the available texts. He collaborates on innovative projects such as SAPPHIRE and SALT, which involve advanced imaging systems and ocean model improvements for tracking sea lice. His contributions also extend to field-portable microscopes and real-time networked medical imaging solutions through collaborations with interdisciplinary teams.

Chris Eliasmithمشاهده پروفایل

استاد

• Theoretical Neuroscience
• Computational Neuroscience
• Neuromorphics
• +۶ مورد دیگر

Chris Eliasmith is a Professor jointly appointed in the Systems Design Engineering and Philosophy departments at the University of Waterloo, with a cross-appointment to Computer Science. He is the Founding Director of the Centre for Theoretical Neuroscience and holds the Canada Research Chair in Theoretical Neuroscience. His research focuses on mathematical modeling of neural systems, including the Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA), which underpin the world’s largest functional brain simulation, Spaun. He leads the Computational Neuroscience Research Group (CNRG), advancing neuromorphic computing, spiking neural networks, and cognitive modeling. Education: PhD in Neuroscience & Psychology (2000), Washington University in St. Louis MA in Philosophy (1995), University of Waterloo BASc in Systems Design Engineering (1994), University of Waterloo Research: His work integrates theoretical neuroscience with practical applications in robotics, AI, and neuromorphic hardware. Key contributions include the NEF, Spaun, and innovations like the Legendre Memory Unit (LMU) and Spatial Semantic Pointers (SSPs). His lab explores adaptive neural systems, decision-making models, and biologically plausible machine learning algorithms. Teaching: Recent courses include SYDE 556 (Simulating Neurobiological Systems), SYDE 750 (Topics in Systems Modelling), and PHIL 356 (Intelligence in Machines, Humans, and Other Animals). Awards: 2015 NSERC Polanyi Prize Labs & Projects: The CNRG develops tools like Nengo for simulating large-scale neural systems and collaborates on neuromorphic hardware platforms such as Loihi. Current projects span neural robotics, cognitive architectures, and biologically inspired AI systems.

• Remote Sensing
• Machine Learning
• Environmental Science
• +۳ مورد دیگر

Mingzhe Jiang is an Adjunct Assistant Professor at the University of Waterloo, focusing on remote sensing and machine learning applications in environmental science. His work emphasizes sea ice classification, SAR imagery analysis, and deep learning models for geospatial data. He is affiliated with the Adjunct Faculty group at the university. Research interests include developing advanced algorithms for sea ice dynamics, uncertainty quantification in climate models, and automated environmental monitoring systems. His contributions span Bayesian neural networks, graph convolutional networks, and semi-supervised learning frameworks for polar and maritime applications. Key publications (2016–2025) highlight innovations in SAR data processing, including hierarchical pipelines for sea ice segmentation, dual-polarized imagery analysis, and unsupervised clustering techniques. His work often integrates satellite data from platforms like RADARSAT-2 with machine learning to improve environmental parameter estimation. No scientific awards or grants are explicitly listed in the provided information. He has not reported advisees or laboratory affiliations in the text.

• Material Reclamation
• Material Selection/Substitution
• Advanced Manufacturing
• +۴ مورد دیگر

Reem Roufail is an Associate Professor in the Department of Systems Design Engineering at the University of Waterloo. Her work focuses on reducing industrial ecological footprints through innovative material selection and advanced manufacturing techniques. She specializes in heavy industries like mining, exploring energy-efficient solutions for comminution processes and mineral liberation. Her research integrates smart materials and renewable energy applications to address sustainability challenges. Education: Doctorate in Mining Engineering, University of British Columbia (2011) Master of Applied Science in Environmental Engineering, The American University in Cairo (1997) Bachelor of Science in Mechanical Engineering, The American University in Cairo (1992) Research Interests: Dr. Roufail's work spans material reclamation, smart material development, and design for energy efficiency. She investigates novel approaches to particle breakage mechanisms and mineral liberation in stirred mills, while also advancing composite materials for drilling applications. Her recent focus includes pandemic-impacted design education methodologies and interdisciplinary project-based learning. Publications: Her work trends emphasize sustainable mining technologies, design pedagogy innovation, and computational modeling (e.g., DEM simulations for mill performance analysis). Over 15 publications span topics from coiled tube rig optimization to post-pandemic teaching strategies. Awards: No awards explicitly listed in the provided materials. Advising & Grants: Currently accepting graduate students through formal applications. No grants mentioned, but her teaching portfolio includes advanced courses like SYDE 461/462 Capstone Projects and SYDE 285/286 material sciences.

Mohamed-Yahia Dabbaghمشاهده پروفایل

دانشیار

• Digital Signal Processing
• Image Processing
• Video Processing
• +۶ مورد دیگر

Mohamed-Yahia Dabbagh is an Associate Professor, Teaching Stream in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds a PhD from North Carolina State University (1989), an MSE from the University of Michigan (1981), and a BSE from the University of Aleppo (1976). His research focuses on digital signal processing, image/video processing, and VLSI multiprocessor implementation of algorithms for communication systems and computer vision. He has developed architectures for digital filters and explored video motion estimation, segmentation, and pattern recognition. Dr. Dabbagh has authored numerous articles in IEEE and other venues, including work on wireless sensor anomaly detection, smartphone application monitoring, and FPGA-based digital filter implementations. His teaching includes courses such as Signals and Systems (ECE 207), Linear Circuits (ECE 140), and Probability and Random Processes (ECE 316). His research has spanned VLSI environments, Bayesian classification for video processing, and real-time signal processing techniques. Notable contributions include multiprocessor filter implementations and adaptive motion estimation algorithms. Dr. Dabbagh is a Professional Engineer (PEng) registered in Ontario and has contributed to technical reports on imaging systems and geographic information systems during his tenure at Kuwait-based institutions.

Franz Newlandمشاهده پروفایل

دانشیار

• Engineering Education
• Lifelong Learning
• Space Mission Design
• +۴ مورد دیگر

Franz Newland is an Associate Professor at the School of Engineering Design and Teaching Innovation, University of Ottawa. With over two decades of experience in Space Engineering across industry and academia in Europe and Canada, his work bridges multidisciplinary learning, engineering education, and space systems. He focuses on lifelong learning, learning organizations, and integrating AI into space mission design and operations. His research interests span engineering education frameworks, organizational learning dynamics, and space mission architecture. Notable projects include the DESCENT CubeSat mission and the Real Engineering: Space program, emphasizing experiential and community-driven learning. Recent publications (2022–2025) highlight trends in capstone design methodologies, interdisciplinary education, and AI-driven satellite signal processing. Articles also explore conceptual models for lifelong learning and learner-led educational design. Dr. Newland’s work emphasizes collaboration, having contributed to national and international space projects. His efforts in curriculum development aim to modernize engineering education to address societal and technological challenges.

Kim Pittawayمشاهده پروفایل

دانشگاهی

• Narrative Nonfiction
• Journalism
• Online Publishing Strategies
• +۳ مورد دیگر

Kim Pittaway is an award-winning journalist and academic, serving as Cohort Director and Instructor in the MFA in Creative Nonfiction at the University of King’s College in Halifax. She previously held editorial leadership roles at Chatelaine , including Editor-in-Chief. Her research focuses on narrative nonfiction, journalism ethics, and online publishing strategies. Pittaway co-authored the critically acclaimed book TOUFAH: The Woman Who Inspired an African #MeToo Movement (2022), which explores gender-based activism in The Gambia. Education: BJ (Carleton University), MFA (Goucher College). She teaches courses like Nonfiction Publishing and advises on writing careers. Awards include the 2022 Queen’s Platinum Jubilee Medal for arts contributions and multiple National Magazine Awards. Her consulting work supports NGOs and educational institutions in strategic storytelling. Current projects include Unconditional (co-authored with Samra Zafar) and a memoir titled Grudge: What We Learn From What We Can’t Forgive . She leads the MFA program’s focus on both creative craft and industry practices, fostering a community of nonfiction writers.

Ali Hashemiمشاهده پروفایل

استادیار

• Perception and cognition
• Learning and memory
• Pedagogy

Ali Hashemi is an Assistant Professor in the Department of Psychology at Wilfrid Laurier University's Faculty of Science. His research explores perception and cognition, learning and memory, and pedagogical approaches. His work employs behavioral and electrophysiological methods to investigate neural processing across the lifespan. Research focuses on visual perception mechanisms, cognitive aging, and neurodegenerative disease biomarkers. Recent studies examine perceptual learning dynamics, face recognition biases, and COVID-19 neurological impacts using EEG and behavioral paradigms. Publications demonstrate consistent investigation into visual cognition plasticity across adulthood, with applications in early dementia detection and cognitive rehabilitation techniques.

Peter Lelievreمشاهده پروفایل

دانشیار

• Geophysical Inversion
• Computational Geophysics
• Mathematical Modeling
• +۴ مورد دیگر

Peter Lelievre is an Associate Professor in the Department of Mathematics and Computer Science at Mount Allison University. His research focuses on advancing geophysical inversion technology to improve subsurface imaging for applications in flood risk management, resource exploration, and environmental monitoring. He emphasizes collaborative, interdisciplinary approaches combining mathematics, computer science, and geophysics. His work addresses computational challenges in geophysical inversion, including unstructured mesh modeling, machine learning integration, and uncertainty quantification. He leads a research group prioritizing diversity and innovation, with current projects involving geophysical imaging of agricultural dykes and seafloor massive sulphide deposits. Recent publications span magnetic surface inversion, transient electromagnetic data analysis, and gravity modeling of crustal systems. Lelievre collaborates on initiatives like the AARMS-CRG Numerical Solution of Geophysical Inverse Problems, fostering discussion on optimization methods and joint inversion strategies. His team includes postdoctoral fellows, undergraduate researchers, and technical staff, with notable contributions to software development for geophysical analysis.

Chelsea D. Beyerمشاهده پروفایل

استادیار

• Counseling Psychology
• Body Image
• Eating Disorders
• +۲ مورد دیگر

Chelsea D. Beyer is an Assistant Professor of Psychology in Trinity Western University's Faculty of Humanities and Social Sciences. Her expertise encompasses body image, embodiment across the lifespan, eating disorder development/treatment, and human sexuality. She holds a PhD in Counseling Psychology from the University of British Columbia, an MA from Trinity Western University, and a combined BA (Honours) in Psychology and BHK. As a Registered Clinical Counsellor (BCACC), she teaches courses including Data Analysis, Abnormal Psychology, and Tests and Measurement. Her research employs qualitative methodologies to explore embodiment in clinical contexts. Her recent publications investigate embodiment measurement techniques and narrative approaches to anorexia recovery, contributing to Routledge volumes on healthy embodiment and eating disorders.

Harold Eysterمشاهده پروفایل

پژوهشگر ارشد

• Socio-Environmental Systems
• Urban Ecology
• Climate Change Adaptation
• +۳ مورد دیگر

Harold Eyster is a Gund Postdoctoral Fellow at the University of Vermont , working with Rachelle Gould and Brian Beckage. He holds a PhD from the University of British Columbia (2021) under Kai Chan and a BA in Environmental Science and Public Policy from Harvard University. His research focuses on socio-environmental systems, urban ecology, and climate change adaptation, with a particular emphasis on renewable energy transitions and human-nature relationships. He currently serves as an Applied Climate Scientist at The Nature Conservancy in Colorado, applying transdisciplinary methods like remote sensing, Bayesian models, and GIS to study renewable energy siting and urban cooling dynamics. His work bridges ecological and social sciences to address sustainability challenges through just and inclusive frameworks. Key interests include: Urban biodiversity and tree-mediated climate regulation Renewable energy impacts and spatial planning Relational values in conservation decision-making Methodologies for interdisciplinary research His research has been published in journals like Journal of Animal Ecology , PNAS , and Conservation Biology , with a focus on actionable insights for policymakers and practitioners.

Helge Rhodinمشاهده پروفایل

استاد

• 3D Computer Vision
• Machine Learning
• Computer Graphics
• +۳ مورد دیگر

Helge Rhodin is a full professor at Bielefeld University (since March 2024) and an affiliate at the University of British Columbia (UBC). His research focuses on advancing 3D computer vision, machine learning, and computer graphics to enable non-intrusive human-computer interaction, particularly in augmented reality (AR) and virtual reality (VR) environments. He previously held positions as an Assistant Professor at UBC, a postdoctoral researcher at EPFL, and a PhD candidate at the Max-Planck-Institute for Informatics under Christian Theobalt. Education & Career: PhD in Computer Science, Max-Planck-Institute for Informatics (2016) Postdoctoral Researcher, EPFL (2016–2020) Assistant Professor, UBC (2020–2024) Full Professor, Bielefeld University (2024–present) Research Interests: Rhodin’s work spans 3D human pose estimation, neural rendering, AR/VR applications, and generative models. Key contributions include real-time motion capture systems (e.g., XNect), self-supervised learning frameworks, and synthetic biomarker development for movement disorders. Publications: His work has been published at top venues like CVPR, ICCV, and SIGGRAPH, with notable achievements like the Best Paper Award at CRV 2023 and an IEEE VR Honorable Mention in 2019. Research trends emphasize human-centric computer vision and interdisciplinary applications in healthcare and sports analytics. Awards & Recognition: Best Paper Award at CRV 2023 IEEE VR Best Journal Paper Honorable Mention (2019) Advising & Labs: He leads a multidisciplinary team at Bielefeld, previously mentoring over 30 students at UBC. His group focuses on developing open-source tools and frameworks for 3D vision and AR. Current research directions include mirror-aware neural humans and scalable neural rendering techniques.

• Mineralogy
• Geometallurgy
• Hyperspectral Imaging
• +۳ مورد دیگر

Cassady Harraden is a postdoctoral research fellow at the Mineral Deposit Research Unit (MDRU) within the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia. She holds a PhD in geometallurgy from the University of Tasmania (2018), where she developed methods for geotechnical rock mass and grain size assessment using multi-sensor systems. Her current research focuses on porphyry exploration and vectoring in British Columbia, with emphasis on mineralogy and mineral chemistry derived from LIBS data. Prior to UBC, she spent four years at Corescan advancing hyperspectral data interpretation and geometallurgical/geotechnical applications of automated core logging technology. Her expertise spans analytical techniques like Laser-Induced Breakdown Spectroscopy (LIBS), micro-XRF, and LA-ICP-MS, applied to mineral exploration, orebody characterization, and geotechnical assessments. Key contributions include real-time orebody knowledge via pLIBS, integration of hyperspectral and structural data for robust interpretations, and automated core logging methodologies for deposit characterization. She has published extensively on porphyry systems, geometallurgical domain mapping, and the application of advanced spectroscopic tools in mining geology. Education: PhD in Geometallurgy (University of Tasmania, 2018) Research Themes: LIBS mineralogy, automated core analysis, porphyry exploration, geometallurgical modeling Collaborations: MDRU, Corescan, CODES group (Australia) Her work bridges field-based analytical innovation with practical geotechnical and metallurgical applications, contributing to more efficient mineral resource evaluation strategies.