جست‌وجوی استاد و پژوهشگر

Ozgur Yilmazمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Signal Processing
• Compressed Sensing
• +۵ مورد دیگر

Ozgur Yilmaz is a Professor in the Department of Mathematics at the University of British Columbia (UBC). He is the Director of the Pacific Institute for the Mathematical Sciences (PIMS) and has held roles such as Interim Deputy Director at PIMS and Deputy Director at the Banff International Research Station (BIRS). His research focuses on applied harmonic analysis, signal processing, compressed sensing, and seismic signal processing. Education: PhD in Applied and Computational Mathematics from Princeton University (2001), B.Sc. in Mathematics and Electrical Engineering from Boğaziçi University (1997). Research Interests: Mathematical problems in analog-to-digital conversion, blind source separation, sparse approximations, compressed sensing, and their applications in seismic exploration. He has contributed to advancements in sigma-delta quantization, low-rank matrix recovery, and compressed sensing algorithms. Funding: Recipient of NSERC Discovery Grants, UBC Data Science Institute grants, and leadership in collaborative research groups (CRGs) on high-dimensional data analysis and applied harmonic analysis. His work bridges theoretical mathematics with practical applications in signal processing and AI-driven medical imaging. Students and Postdocs: Supervised numerous PhD and MSc students in areas like compressed sensing, seismic data reconstruction, and machine learning. Current advisees include Aaron Berk and Xiaowei Li. Former students hold positions at academic institutions and tech companies. Labs and Collaborations: Affiliated with UBC’s Data Science Institute (DSI), Centre for Artificial Intelligence Decision-making and Action (CAIDA), and the Institute of Applied Mathematics (IAM). Collaborates on projects integrating AI with scientific discovery, such as retinal biomarker identification using deep learning.

Brandon Karchewskiمشاهده پروفایل

استاد آموزشی

• Computational Geoscience
• Engineering Geology
• Natural Hazards
• +۲ مورد دیگر

Brandon Karchewski is an Associate Head (Undergraduate) and Teaching Professor in the Department of Earth, Energy and Environment at the University of Calgary. He earned his PhD in Civil Engineering from McMaster University and teaches courses including Engineering Geology, Computational Methods, and Natural Disasters. His research program focuses on: Computational methods in geophysics and geomechanics Geoscience education innovation Climate change impacts on frozen soils Inverse modeling applications Karchewski has pioneered virtual field experiences and developed open-source tools for modeling climate impacts on permafrost. His educational research examines metaphor use in geoscience communication and field pedagogy. Recent computational work includes Python-based permafrost modeling and stochastic inversion methods for biogeochemical transport. He leads the geophysics field school program emphasizing team-based learning. Award recognition includes: Geoscience Teaching Award (2019) Best Poster Award for teaching innovation research (2018) Team Teaching Excellence award (2016) Multiple teaching assistant awards

Colin G. Farquharsonمشاهده پروفایل

استاد

• Geophysics
• Electromagnetic Modeling
• Geophysical Inversion
• +۷ مورد دیگر

Colin G. Farquharson is a Professor in the Department of Earth Sciences at Memorial University of Newfoundland. His career spans roles including Assistant Professor (2008-2018), Associate Professor (2014-2018), and full Professor since 2018. He holds a Ph.D. in Geophysics from the University of British Columbia (1995) and a B.Sc. (Honours) in Geophysics from the University of Edinburgh (1990). His research focuses on forward modeling and inversion of geophysical electromagnetic data, particularly surface geometry inversion (SGI), meshfree methods for unstructured grids, and joint inversion techniques. Key projects include SGI for hydrothermal vent systems, 3D EM modeling for mineral exploration, and computational methods on unstructured tetrahedral meshes. His work addresses challenges in uranium exploration, subsurface imaging, and geological interface reconstruction. Publications emphasize advancements in electromagnetic theory, inversion algorithms, and applications in mineral and hydrocarbon exploration. Notable collaborations include work with Peter Lelièvre on surface-based inversion and stochastic optimization. Research groups include the CAG Group and FacetModeller team, focusing on integrating geological models with geophysical data. Awards and recognitions are not explicitly listed, but his extensive contributions to geophysical methodology and exploration geophysics are well-documented. He advises numerous graduate students and leads projects funded by NSERC, industry partnerships, and international collaborations.

Nicholas Kevlahanمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Fluid Dynamics
• Geophysical Fluid Dynamics
• +۵ مورد دیگر

Dr. Nicholas Kevlahan is a Professor in the Department of Mathematics and Statistics at McMaster University. He holds a BSc in Physics from the University of British Columbia (1985–1989), a PhD in Applied Mathematics from the University of Cambridge (1990–1994), and completed a Marie Curie postdoctoral fellowship at École Normale Supérieure in Paris (1998). He has held visiting positions at institutions including Université Grenoble-Alpes, INRIA, and the University of Cambridge. His research focuses on advanced computational and mathematical methods for fluid dynamics, including the development of the WAVETRISK code for adaptive climate modelling, data assimilation techniques, fluid-structure interaction, and compressive sampling. His work integrates interdisciplinary approaches across applied mathematics, geophysics, and engineering. Key research areas include geophysical fluid dynamics, numerical analysis, partial differential equations, and wavelet-based methods. He has published extensively on topics such as ocean circulation models, turbulence simulation, and adaptive numerical methods. Dr. Kevlahan teaches courses in numerical methods, differential equations, asymptotic analysis, and mathematical physics. He mentors a diverse group of PhD, MSc, and BSc students in his active research laboratory.

Ruohong Jiaoمشاهده پروفایل

استادیار

• Thermochronology
• Orogenic Processes
• Landscape Evolution
• +۱ مورد دیگر

Dr. Ruohong Jiao is an Assistant Professor in Earth and Ocean Sciences at the University of Victoria, currently on leave. Specializing in fission-track and (U-Th)/He thermochronology, his research investigates orogenic processes and landscape evolution through field studies and computational modeling. Research focuses on tectonic exhumation patterns, mountain building processes, and fluvial system responses to tectonic forcing. Recent methodological innovations include applications of physics-informed neural networks for thermochronological data inversion. Field areas span Vancouver Island, Tibetan Plateau, Tian Shan, and Yangtze River regions. Publications demonstrate expertise in reconstructing tectonic histories using low-temperature thermochronometry, with emphasis on coupling between surface processes and deep crustal dynamics. Professional service includes graduate student committee supervision.

Pooneh Maghoulمشاهده پروفایل

استاد

• Geotechnical Engineering
• Earthquake Engineering
• Solid Mechanics
• +۱۱ مورد دیگر

Pooneh Maghoul is a Full Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. She founded and directs the Sustainable Infrastructure and Geoengineering Laboratory (SIGLab), and is a member of Astrolith (Lunar Research Unit) and the Geotechnical Research Group (GRG). With over 14 years of academic and industrial experience in Canada and France, she chairs the Education Committee of the Canadian Geotechnical Society (CGS) and serves on its Board of Directors, as well as the Canadian Permafrost Association (CPA). Ph.D. (2007–2010), M.Sc. (2006–2007) - École des Ponts ParisTech Postdoc (2010–2012) - Université Laval B.Sc. (2002–2006) - University of Tehran Her research focuses on poromechanics of complex porous media and subsurface systems under multi-hazard conditions, including climate change , earthquakes , and lunar environments . Key areas include permafrost stability , bio-inspired drilling , non-destructive testing , and lunar subsurface exploration . Recent publications highlight her work on ultrasonic characterization of frozen soils , climate-resilient Arctic infrastructure , and AI-driven permafrost thaw prediction . She has pioneered waterless drilling technology adaptable for lunar regolith exploration and developed geothermal energy solutions for cold regions. 2023 : Canada's Outstanding Young Geotechnical Engineer 2022 : Canadian Geotechnical Society Recognition Multiple : Best Paper Awards in International Journals She has supervised 2 Ph.D. and Master’s students, including Cui, S. (2023) and Afsharipour, M. (2023). Her work bridges earthquake engineering , computational mechanics , and space geotechnics , with recent grants from the Canada Foundation for Innovation and Fonds de recherche du Québec.

Jan Dettmerمشاهده پروفایل

دانشیار

• Geophysical Inverse Problems
• Bayesian Methods
• Seismology
• +۴ مورد دیگر

Dr. Jan Dettmer is an Associate Professor in the Department of Earth, Energy, and Environment at the University of Calgary's Faculty of Science. His research focuses on quantitative analysis of Earth structures through geophysical data inversion, specializing in Bayesian methods for uncertainty quantification. His work spans seismology, acoustical oceanography, and tsunami hazard prediction, with applications ranging from shallow seabed characterization to deep mantle structures. Research interests include: Probabilistic inversion methods for earthquake source parameters and earth structure Wave propagation modeling in complex media Computational algorithm development for large-scale inverse problems Integration of supercomputing (CPU/GPU clusters) in geophysical analysis Recent publications demonstrate strong focus on geophysical inversion techniques, computational methods, and applications to energy and environmental challenges. Awarded the Faculty of Science Research Award for early career excellence (2019).

• Groundwater Hydrology
• Contaminant Transport
• Hydraulic Tomography
• +۵ مورد دیگر

Walter Illman is a Professor of Physical and Contaminant Hydrogeology at the University of Waterloo's Department of Earth and Environmental Sciences, Faculty of Science. He has been actively engaged in research and teaching since completing his PhD at the University of Arizona in 1999. Illman is a member of The Water Institute at the University of Waterloo and teaches courses including Flow and Transport Through Fractured Rocks (EARTH 439), Physical Hydrogeology (EARTH 458), Physical Processes in Groundwater Systems (EARTH 650), and Flow and Transport in Fractured Rock (EARTH 658). His educational background includes a PhD in Hydrology and Water Resources from the University of Arizona (1999) and a BSc (Honours) in Earth Sciences from the University of Washington (1994). Professor Illman specializes in groundwater hydrology, with particular expertise in both saturated and unsaturated zones. His research employs mathematical modeling (both analytical and numerical), laboratory experiments, and field investigations to understand fluid flow and contaminant transport in porous and fractured geologic media. Key areas of focus include Dense Non-Aqueous Phase Liquid (DNAPL) source zone characterization, geostatistical inverse modeling, bioremediation, natural attenuation of organic contaminants, and hydraulic tomography for aquifer characterization. His work bridges theoretical hydrogeology with practical applications for groundwater management and contamination remediation. Illman's publication record demonstrates a sustained focus on hydraulic tomography as a method for characterizing aquifer heterogeneity, with increasing attention in recent years to integrated surface water-groundwater modeling and climate change impacts on water resources. His research spans laboratory experiments, field investigations, and mathematical modeling across various hydrogeological settings. Among his notable recognitions are the 2003 Outstanding Paper award at the International Symposium on Groundwater Problems Related to Geo-Environment, the 2002 American Geophysical Union Editors' Citation for Excellence in Refereeing, and several prestigious fellowships during his doctoral studies at the University of Arizona. Professor Illman has supervised numerous graduate students working on advanced topics in hydrogeology and has secured research funding to support his laboratory and field investigations. His work often involves collaboration with researchers at other institutions to address complex groundwater contamination problems. His research group maintains laboratory facilities for conducting controlled experiments on fluid flow and contaminant transport, and regularly conducts field studies to validate theoretical models and investigate real-world groundwater systems.

• Seismic Data Processing
• Signal Processing
• Microseismic Monitoring
• +۲ مورد دیگر

Jubran Akram is an Adjunct Assistant Professor in the Department of Earth, Energy, and Environment within the Faculty of Science at the University of Calgary. His research focuses on advanced geophysical signal processing techniques. Primary research areas include: Microseismic data processing and event detection Wavefield separation and noise suppression methods Machine learning applications in seismic analysis Velocity modeling and hypocenter localization His publications (2018-2024) demonstrate consistent focus on developing computational methods for seismic data enhancement, including machine learning approaches for phase picking, noise reduction, velocity calibration, and event location.

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.

• Artificial Intelligence
• Machine Learning
• Inverse Problems
• +۳ مورد دیگر

Anran Xu is a Researcher in the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia (UBC), within the Faculty of Science. Their work focuses on applying machine learning techniques to solve inverse problems across multiple physics domains, including geophysics, cosmology, and biomedical imaging. Education and Supervision: Anran completed their Master’s thesis under Prof. Qinya Liu at the University of Toronto (UofT), focusing on earthquake-related inverse problems. They also collaborated with Dr. Keir Rogers (UofT) on cosmology projects and with Luca Calatroni and Laure Blanc-Féraud at CNRS on fluorescence microscopy applications. Research Interests: Central to their work is the development of test-time learning and neural network parameterizations to enhance geophysical inversion methods. Their research spans AI-driven solutions for parameter estimation in complex physical systems, emphasizing regularization techniques and neural field applications. They explore cross-disciplinary challenges in geophysics, cosmology, and microscopy through computational frameworks. Publications: Their recent articles highlight contributions in leveraging CNNs for implicit regularization in DC resistivity inversions and reparameterizing geophysical inverse problems using neural networks. These works reflect a trend toward integrating modern ML architectures with traditional geophysical methodologies. Labs/Teams: While specific lab affiliations are not detailed, their collaborations suggest involvement with UBC’s geophysical research groups and international partnerships with institutions like CNRS.

Mostafa Naghizadehمشاهده پروفایل

پژوهشگر

• Seismic Data Processing
• Inverse Problems
• Mineral Exploration
• +۳ مورد دیگر

Mostafa Naghizadeh is a Researcher affiliated with the Metal Earth project at Laurentian University's Harquail School of Sustainable Mining. His expertise lies in seismic data processing, inverse problems, and mineral exploration geophysics. He holds a B.Sc. in Mining Engineering from Shahid Bahonar University of Kerman (Iran), an M.Sc. in Geophysics from the University of Tehran, and a Ph.D. in Geophysics from the University of Alberta. Dr. Naghizadeh's research focuses on advancing seismic imaging techniques, environmental sustainability in seismic surveys, and crustal architecture studies. His work integrates geophysical methods with machine learning to enhance mineral exploration efficiency. He has contributed to projects like the Metal Earth initiative, which maps mineral endowments in Canada's Archean terranes. Education: B.Sc. Mining Engineering, Shahid Bahonar University of Kerman M.Sc. Geophysics, University of Tehran Ph.D. Geophysics, University of Alberta His research interests emphasize seismic data optimization, inverse problem solutions, and environmental adaptations for geophysical surveys. Recent work includes developing EcoSeis—low-footprint acquisition methods—and analyzing fault systems in gold-endowed regions. He has received numerous awards, including the SEG Karcher Award (2011), CSPG Baillie Award (2009), and multiple scholarships for academic excellence. Professionally, he is a certified Geoscientist (APEGA) and holds editorial roles at SEG's Geophysics and CSPG's Recorder journals. Key contributions include advancing seismic design for sustainability, crustal-scale imaging, and mineral prospectivity mapping in the Abitibi and Wabigoon belts. His interdisciplinary approach bridges geophysics with environmental and computational sciences.

Hersh Gilbertمشاهده پروفایل

دانشیار

• Geophysics
• Seismology
• Crustal Structure
• +۱ مورد دیگر

Hersh Gilbert is an Associate Professor in the Department of Earth, Energy, and Environment at the University of Calgary's Faculty of Science. He holds a PhD in Geophysics from the University of Colorado and a BA in Physics from Washington University. His research focuses on geophysical methods for understanding crustal structure and tectonic processes, with particular expertise in seismology, Earth imaging techniques, and geothermal resource assessment. His work frequently incorporates advanced signal processing and machine learning approaches to analyze seismic data. Gilbert's extensive publication record demonstrates a consistent focus on crustal dynamics, earthquake mechanisms, and energy-related geoscience applications. His articles frequently examine tectonic processes in Western Canada and Alaska, geothermal exploration methods, and carbon sequestration monitoring. He has received notable recognition including the 2018 Outstanding Publication Award from the Geological Society of America and a 2017 Editor's Highlight from the American Geophysical Union.

Lindsey J. Heagyمشاهده پروفایل

استادیار

• Geophysics
• Data Science
• Inverse Theory
• +۴ مورد دیگر

Lindsey J. Heagy is an Assistant Professor in the Department of Earth, Ocean, and Atmospheric Sciences at the University of British Columbia. Her research focuses on geophysics, data science, and inverse theory, with applications in resource exploration, groundwater, and environmental studies. She leads a research group developing machine learning and inversion methods for geophysical data analysis. Key affiliations: University of British Columbia (Department of Earth, Ocean, and Atmospheric Sciences) Her work emphasizes open science, contributing to projects like SimPEG (an open-source Python package for geophysical simulations) and GeoSci.xyz (collaborative educational resources for geophysics). She advocates for reproducible workflows and open-source software in the geosciences. Research interests include electromagnetic methods, inverse problem solutions, and interdisciplinary applications of data science. Notable contributions include advancements in 3D electromagnetic modeling, UXO detection via machine learning, and open-source tools for airborne and ground-based geophysical data analysis. Publications span peer-reviewed journals like Geophysical Journal International and Exploration Geophysics , focusing on numerical methods, inversion techniques, and open-source software development. Her work bridges computational geophysics with real-world challenges in environmental monitoring and resource management. She collaborates on projects like SimPEG and GeoSci.xyz, fostering community-driven advancements in geophysical education and research. Her efforts in capacity building include geophysical training in regions like Myanmar to improve water security.

• Geophysics
• Inverse Problems
• Computational Methods
• +۵ مورد دیگر

Eldad Haber is a Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), affiliated with the Faculty of Science. He holds memberships in the Institute of Applied Mathematics and the CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. His research focuses on advancing computational methods at the intersection of geophysics, machine learning, and optimization, with applications in inverse problems, electromagnetic imaging, and reservoir characterization. Key areas of investigation include geophysical inversion techniques, machine learning for data-driven modeling, and the development of efficient algorithms for large-scale problems. Haber’s work integrates disciplines such as geophysics, computer science, and applied mathematics to address challenges in subsurface imaging, environmental monitoring, and resource exploration. Recent research trends in his publications emphasize the application of neural networks to inverse problems, optimization under uncertainty, and multiscale methods for improving computational efficiency. His work on ‘Synthetic Geology’ and ‘Graph Neural Networks’ highlights innovative approaches to geoscientific challenges using deep learning. Collaborations with industry and academic partners have yielded practical solutions for airborne electromagnetic data inversion and mineral prospectivity mapping. His contributions span theoretical advancements in adjoint methods, stochastic optimization, and data assimilation, alongside applied projects in hydrogeology and mineral exploration. Haber’s publications reflect a commitment to bridging computational innovation with real-world geophysical applications.