جست‌وجوی استاد و پژوهشگر

Yueqin Huangمشاهده پروفایل

استادیار

• Signal Processing
• Machine Learning
• Data Analytics
• +۴ مورد دیگر

Yueqin Huang is an Assistant Professor in the Department of Information Science Technology at the University of Houston's Cullen College of Engineering. She holds a PhD in Communication and Information Systems from Xiamen University and was a visiting scholar at Duke University (2008-2010). Her research integrates signal processing, machine learning, and data analytics for subsurface characterization applications including: Energy transition technologies Geophysical inversion methods Electromagnetic modeling Real-time resource monitoring Her publication portfolio demonstrates extensive work in deep learning-enhanced inversion techniques for geophysical data, uncertainty quantification, and multi-physics integration for resource exploration. Research consistently focuses on computational efficiency and real-time applications in energy contexts.

Alison Malcolmمشاهده پروفایل

استاد

• Seismic Imaging
• Full-Waveform Inversion
• Uncertainty Quantification
• +۳ مورد دیگر

Alison Malcolm is a Professor in the Earth Sciences Department at Memorial University of Newfoundland, specializing in seismic imaging and geophysical methods. Her research focuses on uncertainty quantification, nonlinear imaging techniques, and full-waveform inversion (FWI). She leads a vibrant research group advising PhD, Master’s, and undergraduate students in topics such as subsurface imaging, rock physics, and CO2 sequestration monitoring. She holds a Ph.D. from Colorado School of Mines and a B.Sc. from the University of British Columbia. Affiliations: Memorial University of Newfoundland (MUN) Education: Ph.D. in Geophysics (2005, Colorado School of Mines), B.Sc. in Geophysics (University of British Columbia) Her research interests span uncertainty quantification in FWI, nonlinear elastic wave interactions, and subsurface monitoring. Current projects include experimental studies on crack effects in nonlinear elasticity (collaboration with Xuan Feng and James TenCate) and Bayesian inversion methods for CO2 reservoirs. She teaches advanced courses like Wave Equation Modeling, Processing and Inversion Algorithms and Seismic and Potential Fields Methods . Her lab conducts experiments on rock samples to understand nonlinear wave coupling, with applications in reservoir characterization and fault mechanics. Collaborations include MIT, ETH Zurich, and industry partners like ExxonMobil and Chevron. Alumni from her group hold positions in academia and industry worldwide. Key research themes include: Quantifying subsurface uncertainties using probabilistic methods Developing FWI algorithms for targeted imaging Exploring nonlinear phenomena for subsurface property estimation Her work integrates computational methods with laboratory experiments and field data, addressing challenges in energy exploration and environmental monitoring.

Xuemin Tuمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +۳ مورد دیگر

Xuemin Tu is a Professor in the Department of Mathematics at the University of Kansas, serving as the Scheduling Officer. His research focuses on scientific computing, numerical analysis, and applied mathematics, emphasizing domain decomposition methods, BDDC algorithms, and data assimilation. He is affiliated with the College of Liberal Arts & Sciences and actively contributes to interdisciplinary computational science. Research interests include iterative methods for partial differential equations, numerical solutions of fluid dynamics problems, and the development of efficient algorithms for large-scale simulations. His work bridges theoretical analysis and practical applications in engineering and geophysics. Key trends in his recent articles (2018–2025) highlight advancements in BDDC algorithms for fluid and porous media systems, implicit sampling techniques for data assimilation, and machine learning applications in image recognition. He also explores stochastic models for parameter estimation and uncertainty quantification. No scientific awards or grants are explicitly listed in the provided text. Advising roles or student mentorship details are not included, though his research areas suggest contributions to graduate training in computational mathematics. Labs or collaborative teams are not detailed here, but his affiliations imply involvement with computational science initiatives at the University of Kansas.

Ebru Bozdağمشاهده پروفایل

دانشیار

• Computational Seismology
• Global Seismic Tomography
• Full-Waveform Inversion
• +۳ مورد دیگر

Ebru Bozdağ is an Associate Professor at the Colorado School of Mines, jointly appointed in the Department of Applied Mathematics and Statistics and Geophysics , and serves as Director of Graduate Studies. She holds affiliations with the Center for Space Resources and is a Faculty Fellow at the Payne Institute . Previously, she was an Associate Professor at Université Côte d’Azur (France) and a researcher at Princeton University. Education: PhD in Geophysics, Utrecht University (2005–2009) MSc & BSc in Geophysics, Istanbul Technical University (2000–2002 & 1996–2000) Research Interests: Focuses on computational seismology, including full-waveform inversion, global seismic tomography, and planetary seismology (e.g., Mars InSight mission). Specializes in 3D seismic wave simulations and adjoint techniques for Earth’s interior imaging. Recently expanded to exploration-scale problems and computational optimizations. Awards & Grants: Chaire d’Excellence (France) NSF SCOPED Project (collaborative multi-institutional) Leadership Resource Allocation (LRAC) on Frontera supercomputer PRACE award for Marconi100 GPU system Advising: Supervised PhD student Caio, who defended in 2021. Actively recruiting students with backgrounds in geophysics, computational sciences, or mathematics. Labs & Teams: Leading computational efforts in global seismology, including collaborations on the Mars InSight mission and high-performance computing projects on systems like Fugaku and Titan.

John Bradfordمشاهده پروفایل

استاد

• Ground-penetrating radar
• Reflection seismology
• Wavefield modeling
• +۳ مورد دیگر

John Bradford serves as Professor of Geophysics and Vice President for Global Initiatives at the Colorado School of Mines, where he bridges academic leadership with institutional strategic vision in geophysical sciences. His academic foundation includes: PhD in Geophysics from Rice University BS in Engineering Physics from the University of Kansas BS in Physics from the University of Kansas Bradford's research program centers on advanced geophysical methodologies, with core expertise in ground-penetrating radar systems, seismic wave propagation analysis, and subsurface imaging techniques. His work integrates computational modeling with field applications to solve complex problems in near-surface characterization, emphasizing material property estimation and dynamic process monitoring across environmental and resource exploration contexts. Analysis of his recent publications reveals a cohesive research trajectory focused on multi-parameter inversion frameworks, particularly the synergistic integration of ground-penetrating radar with electrical resistivity methods. His publications demonstrate methodological innovations in joint inversion algorithms, surface wave analysis for shear-wave velocity profiling, and time-lapse GPR applications for hydrological monitoring, with significant extensions into glaciological studies using multi-offset radar systems for historical climate reconstruction.

Jeffrey Shraggeمشاهده پروفایل

استاد

• Geophysics
• Seismology
• Computational Physics
• +۸ مورد دیگر

Jeffrey Shragge is a Professor of Geophysics at the Colorado School of Mines , where he also serves as Director of the Center for Wave Phenomena. His research spans computational geophysics, focusing on wave propagation, inversion, and imaging techniques. PhD in Geophysics, Stanford University MSc in Geophysics, University of British Columbia BScH in Physics, Queen’s University Shragge's research interests include: 3D/4D Seismic Imaging : Reverse-Time Migration (RTM), wave-equation migration, and elastic time-reverse imaging. Wave Propagation & Inversion : Acoustic/elastic wave modeling, full-waveform inversion, and velocity estimation. Emerging Technologies : Distributed Acoustic Sensing (DAS), GPU computing, and CUDA/OpenMP/MPI programming. Applied Geophysics : Near-surface geophysics for mining, glaciology, and agricultural development. His scientific awards include the 2025 REI Award, 2022 T.K. Young Award, and multiple SEG and Mines accolades for teaching and research excellence. He has advised PhD students like Matt Saul, who received a 2013 SEG Best Student Paper award. Shragge leads the Center for Wave Phenomena, advancing research in seismic interferometry, microseismic monitoring, and computational methods for anisotropic media.

Samuel Maddenمشاهده پروفایل

استاد

• Database Systems
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Samuel Madden is the Faculty Head of Computer Science and Distinguished College of Computing Professor at MIT's Department of Electrical Engineering and Computer Science (EECS). He joined MIT in 2004, earning a PhD from UC Berkeley (2003) and BS/MEng from MIT (1999). His research focuses on database systems, machine learning integration, and high-performance data processing. He co-leads the Data Systems for AI Lab and Data Systems Group. Key achievements include co-founding Cambridge Mobile Telematics, receiving ACM Fellow (2020), and awards like the NSF CAREER Award and SIGMOD Codd Innovations Award. His work bridges database systems with modern AI applications, emphasizing scalable and efficient data processing. Research interests span database analytics, query processing, cloud computing, and applying machine learning to system design. He has published extensively on topics like query optimization (e.g., Abacus, Kairos), AI-driven data systems (Palimpzest, Symphony), and scalable analytics (RITA, Cackle).

• Numerical and Computational Mathematics
• Computational Fluid Dynamics
• Adjoint-Based Sensitivity Analysis
• +۳ مورد دیگر

Daiane Iglesia Dolci is a Researcher in the Department of Mathematics at Imperial College London’s Faculty of Natural Sciences. Her work focuses on computational and applied mathematics, particularly adjoint-based sensitivity analysis, fluid-structure interaction, and high-performance computing methods. She contributes to the open-source Firedrake project under collaboration with David Ham. Current Position: Research Associate in Computational Mathematics (2023–present) Education: PhD (University of São Paulo, 2015–2020), MSc (UNESP, Brazil, 2012–2014), BSc (University of Mato Grosso do Sul, Brazil, 2008–2011) Her research integrates numerical methods for time-dependent systems, computational fluid dynamics, and optimization. Key areas include sensitivity analysis for fluid flows and the development of efficient algorithms for adjoint simulations. Collaborations emphasize practical applications in engineering and geophysics. Publications highlight advancements in adjoint-based methodologies, mesh adaptation, and FWI optimization. Her work bridges theoretical mathematics with computational tools for real-world fluid dynamics challenges.

Domenico Giardiniمشاهده پروفایل

استاد

• Seismology
• Geodynamics
• Earth's mantle and core structure
• +۷ مورد دیگر

Domenico Giardini is a Full Professor at the Department of Earth and Planetary Sciences, ETH Zurich , since 1997. He holds a doctorate in Physics from the University of Bologna (1987) and has held academic positions at Harvard University, National Institute of Geophysics in Rome, and University of Rome III. Giardini has directed the Swiss Seismological Service (until 2011) and the Swiss Competence Center for Energy Research – Supply of Electricity (SCCER-SoE). Born: March 19, 1958, Bologna, Italy Education: PhD in Physics, University of Bologna Current Roles: Full Professor, ETH Zurich President, International Association of Seismology & Physics of the Earth Interior (IASPEI) Co-PI, ESA Pathfinder & NASA InSight missions Research Interests span seismology, geodynamics, planetary seismology (Mars), gravitational wave detection, and induced seismicity. His work integrates deep earthquake dynamics, Earth's mantle/core structure, and seismic hazard assessment. Recent Publications focus on Mars impact events, LISA Pathfinder magnetic noise, Rotondo granite stress anisotropy, and BedrettoLab hydraulic experiments, reflecting his interdisciplinary expertise in geophysics and space missions. Scientific Leadership includes coordinating projects for the European Commission, UNESCO, UN, NASA, ESA, and OECD. He co-founded the Global Earthquake Model initiative and directed the UN/IDNDR Global Seismic Hazard Assessment Program. Advising has resulted in over 60 PhD theses completed under his supervision at ETH Zurich since 2010. His grants include Magnetotelluric investigation of active rifting and geothermal resources in Ethiopia (ETH-02 19-1).

Hansruedi Maurerمشاهده پروفایل

استاد

• Geophysical tomography
• Statistical experimental design
• Environmental geophysics
• +۴ مورد دیگر

Prof. Hansruedi Maurer is a Professor at ETH Zurich's Department of Earth Sciences, holding a position in the Institute of Geophysics where he leads the Applied and Environmental Geophysics Group. His research focuses on geophysical tomography and statistical experimental design for environmental and engineering applications, with significant contributions to CO2 storage monitoring, geothermal energy exploration, and glaciology. Maurer earned his Diploma and Doctorate in Geophysics from ETH Zurich in 1993, completing his thesis on microseismicity and seismotectonics of the western Swiss Alps. He received his Habilitation in 2002 and was awarded the title of Professor in 2007. His academic journey includes visiting positions at the Geological Survey of Canada (1996) and the University of British Columbia (2005). His research centers on tomographic inversion techniques and experimental design optimization for geophysical data acquisition. Recent work examines seismic anisotropy in crystalline rock, hydraulic stimulation monitoring, and CO2 mineralization processes, with applications spanning underground laboratories, alpine environments, and geothermal reservoirs. His methodologies enhance subsurface characterization for energy and environmental projects. Analysis of his 2024-2025 publications reveals strong emphasis on multidisciplinary monitoring of hydraulic stimulations in the Bedretto Underground Laboratory, CO2 storage validation in Iceland, and glacier hydrology studies. Key trends include integration of distributed acoustic sensing with traditional seismic methods, advanced full-waveform inversion techniques, and cross-disciplinary approaches combining geophysics with geomechanics and hydrology. Scientific recognition includes: Best Poster Award of the Society of Exploration Geophysics (1998) Best Paper Award in Geophysics (2004) Maurer serves as Associate Editor for the journal Geophysics and participates in the Swiss Geophysical Commission. He teaches graduate courses including Seminar in Applied and Environmental Geophysics and Exploration and Environmental Geophysics, mentoring students through research projects in the Applied and Environmental Geophysics Group. His work receives support from Swiss and international grants focused on geothermal energy development and carbon storage validation projects like VALTER and EASYGO. He directs research activities at the Bedretto Underground Laboratory for Geosciences and Geoenergies, coordinating multidisciplinary teams for hydraulic stimulation experiments and reservoir characterization. Current efforts involve dense seismic monitoring arrays, tracer flow reconstruction in fractured rock, and development of real-time inversion techniques for subsurface process monitoring.

• Geophysics
• Metamaterials
• Wave Propagation
• +۴ مورد دیگر

Henrik Rasmus Thomsen is a Researcher at the Institute of Geophysics, ETH Zürich, affiliated with the Exploration and Environmental Geophysics (EEG) group. He holds a doctoral degree from ETH Zürich (2021) and previously served as a Postdoctoral Researcher at the Chair of Structural Mechanics and Monitoring. His research focuses on metamaterials, elastic wave propagation, and non-destructive testing (NDT) using advanced techniques like ultrasound computed tomography and full-waveform inversion. He leads the Innosuisse-funded project developing quantitative methods for NDT of composite materials. Key projects include the MATRIX project (machine for time reversal and immersive wave experimentation) and the MetaVEH project (metasurface energy harvesting). His work spans theoretical, experimental, and applied domains, with contributions to metamaterial design, wavefield control, and structural health monitoring. Recent publications explore guided wave-based digital twins, phononic metamaterials for speech classification, and elastic wave control in granular media. Thomsen collaborates with institutions like the Acoustical Society of America and journals including Advanced Functional Materials and Philosophical Transactions of the Royal Society .

Pranav Karveمشاهده پروفایل

عضو هیئت علمی

• Probabilistic Diagnosis
• Probabilistic Prognosis
• Optimization under Uncertainty
• +۷ مورد دیگر

Pranav Karve is a Research Associate Professor at Vanderbilt University's School of Engineering, specializing in Risk, Reliability, and Resilience in engineering systems. His research integrates physics-based and data-driven models to address uncertainty in engineering systems, with applications spanning structural engineering, geophysics, petroleum engineering, biomedical imaging, aerospace engineering, composites manufacturing, and electrical engineering. Ph.D., Civil Engineering, The University of Texas at Austin M.S.E., Civil Engineering, The University of Texas at Austin B.E., Civil Engineering, Government College of Engineering, Pune, India His work focuses on advancing methodologies for probabilistic diagnosis and prognosis, with key applications including blast-hardened building design, kidney stone detection, and optimal power grid operations. Dr. Karve's interdisciplinary research bridges multiple engineering disciplines to enhance system resilience and performance under uncertain conditions. No specific articles or grants are detailed in the provided text. His research is supported by collaborations across Vanderbilt's LASIR group and other institutions.

• Scientific Machine Learning
• Deep Learning
• Computational Imaging
• +۵ مورد دیگر

Youzuo Lin is an Associate Professor at the School of Data Science and Society (SDSS), University of North Carolina at Chapel Hill. Previously, he served as a Senior Scientist at Los Alamos National Laboratory (2010–2024). He holds a Ph.D. in Applied and Computational Mathematics from Arizona State University (2010). His research focuses on scientific machine learning (AI for Science), deep learning, and computational methods applied to medical imaging (e.g., ultrasound tomography, brain imaging), geoscience (subsurface imaging, CO2 sequestration), and seismology (earthquake detection). He leads projects like OpenFWI and EFWI benchmark datasets for seismic inversion, BrainPuzzle for ultrasound brain imaging, and EarthquakeGen for earthquake simulation using GANs. Recent work emphasizes hybrid physics-driven and data-driven approaches, including physics-informed data augmentation and diffusion models for FWI. His methods address challenges such as narrow data acquisition apertures, limited training datasets, and computational efficiency in resource-constrained environments. He co-developed Edge-InversionNet for edge device inference and contributed to quantum algorithms for geoscience problems. Lin’s research has been applied to carbon sequestration monitoring, medical diagnostics, and geophysical exploration. He is an editorial board member of the IEEE Open Journal of Signal Processing and actively contributes to Kaggle competitions (e.g., Geophysical Waveform Inversion). His group welcomes Ph.D. students, interns, and visiting researchers. Openings emphasize expertise in machine learning, computational physics, and domain-specific applications.

Mauricio Sacchiمشاهده پروفایل

استاد

• Geophysics
• Inverse Problems
• Seismology
• +۵ مورد دیگر

Mauricio Sacchi is a Professor in the Department of Physics at the University of Alberta, Faculty of Science. He holds the academic rank of Professor since 2005. His research focuses on geophysics, inverse problems, and seismology, with a particular emphasis on seismic data processing and imaging techniques. He has been Chair of the Department of Physics at UofA from 2010–2015 and 2016–2021. Education: BSc from Universidad Nacional de La Plata (UNLP), Argentina (1988), and PhD in Geophysics from the University of British Columbia (UBC), Canada (1996). Prior to his current role, he served as Assistant Professor (1997–2001) and Associate Professor (2001–2005) at the University of Alberta. Research Interests: Development of algorithms for multi-dimensional seismic data reconstruction, de-noising, and sparsity-promoting methods. Current projects include optimal acquisition design for simultaneous source acquisition, full waveform inversion, and waveform imaging in applied and global seismology. Notable Awards: 2012 CSEG Medal, 2014 SEG Honorary Lecturer, 2015 CSEG Distinguished Speaker, 2019 Vigil Kauffman Gold Medal, and 2023 Killam Annual Professor. Teaching: Courses include Computational Physics (PHYS 420/580) and Introduction to Seismic Imaging (GEOPH 326). He has advised numerous graduate students, including Wenlei Gao, Ke Chen, Gian Matharu, and Iliana Papathanasaki.

Daniel Tradمشاهده پروفایل

دانشیار

• Seismic Imaging
• Full-Waveform Inversion
• Computational Geophysics
• +۲ مورد دیگر

Dr. Daniel Trad is Associate Professor and Canadian Society of Exploration Geophysicists Chair in Exploration Geophysics at the University of Calgary. His research develops computational methods for seismic data analysis, with applications in energy exploration and environmental monitoring. Core innovations include machine learning-enhanced full-waveform inversion techniques and high-dimensional seismic data reconstruction algorithms. His lab focuses on solving inverse problems in geophysics through integrated mathematical modeling and high-performance computing. Teaching includes advanced courses in Geophysical Signal Processing and Imaging/Inversion. Research outputs contribute to improved subsurface characterization for resource exploration and carbon sequestration projects.