جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Computer Vision
• Neural Networks
• +۵ مورد دیگر

Mohit Prabhushankar is a Research Fellow at the Georgia Institute of Technology's Department of Electrical and Computer Engineering, part of the College of Engineering. His work focuses on machine learning, computer vision, and explainable AI with applications in medical imaging, seismic interpretation, and uncertainty quantification. He has contributed to developing algorithms for active learning, self-supervised learning, and robust model evaluation. Research interests include hierarchical multimodal systems, counterfactual explanations, and domain adaptation in geoscience applications. His recent work addresses challenges in interpretability metrics, active learning strategies, and biomedical data analysis through projects like the OLIVES dataset for ophthalmic biomarkers and CRACKS for subsurface fault analysis. Publications span topics from action anticipation in robotics to neural network dynamics, emphasizing practical applications of AI in geoscience and healthcare. His research bridges theoretical advancements with real-world deployment considerations through frameworks like gradient-based anomaly detection and deployable clinical active learning systems. No scientific awards or grants are explicitly listed in available materials. Advising activities and student collaborations remain undocumented in the provided content.

• Pore-Scale Imaging
• Multiphase Flow Dynamics
• CO2 Sequestration
• +۴ مورد دیگر

Rukuan Chai is a Researcher in the Department of Earth Science & Engineering at Imperial College London, affiliated with the Royal School of Mines. He holds a Research Associate position in the pore-scale modelling and imaging group, focusing on multiphase flow in CO2 storage reservoirs. His work is part of the PETRONAS Project, collaborating with Prof. Martin Blunt and Dr. Branko Bijeljic. His research interests revolve around pore-scale imaging, CO2 sequestration mechanisms, geothermal reservoir dynamics, and molecular dynamics simulations. He employs advanced imaging techniques and computational models to understand fluid-rock interactions and enhance energy recovery processes. Recent publications highlight contributions in CO2 storage efficiency, geothermal-driven catalytic reduction, and low-salinity waterflooding mechanisms. His work bridges experimental data with computational modeling to address challenges in environmental geochemistry and sustainable energy systems. Rukuan’s research group actively explores carbon capture and storage (CCS) technologies, contributing to both academic and industrial advancements in reservoir engineering and climate change mitigation strategies.

• Water Resources Engineering
• Subsurface Flow and Transport
• Physics-Informed Machine Learning
• +۳ مورد دیگر

Professor Alexandre Tartakovsky is a faculty member at the University of Illinois at Urbana-Champaign's Department of Civil and Environmental Engineering. His primary research focuses on water resources engineering, subsurface flow and transport, and physics-informed machine learning. He holds a M.Sc. from Kazan State University (1994) and a Ph.D. from the University of Arizona (2002). His research integrates computational mathematics with geosciences, emphasizing stochastic modeling, uncertainty quantification, and multiscale phenomena. Notable contributions include physics-informed neural networks for inverse problems and Bayesian data assimilation techniques. Tartakovsky has held roles at Pacific Northwest National Laboratory (2004–2019) and the University of South Florida (2013–2014). Research Interests : Inference and parameter estimation, multiscale modeling, uncertainty quantification, physics-informed machine learning, pore-scale multiphase flow, and subsurface flow dynamics. Recent Publications : Recent work includes advancements in machine learning for geophysical inversion, stochastic control of power grids, and Bayesian surrogate models for dynamic systems. His 2025 articles highlight applications in load frequency control and randomized neural networks for uncertainty quantification. Awards : Recipient of the DOE Early Career Award (2008), Presidential Early Career Award (2008), and multiple fellowships. Active in SIAM and AGU professional societies. Labs/Teams : Hydrosystems Lab at UIUC, collaborating on computational hydrology and subsurface processes. Research spans from theoretical models to applied geoscience challenges.

Martin Fernøمشاهده پروفایل

استاد

• Reservoir Physics
• Multiphase Flow in Porous Media
• CO2 Storage
• +۵ مورد دیگر

Professor Martin Fernø is affiliated with the Department of Physics and Technology at the University of Bergen. His research focuses on reservoir physics, multiphase flow in porous media, CO2 and hydrogen storage, and enhanced oil recovery. He leads the Reservoir Physics – Energy Technology and CO2 Storage (CCUS) research group. Research interests include capillary heterogeneities, wettability effects, in-situ imaging via MRI/NMR/CT, and SCAL techniques. He has contributed to projects like HyPE (Hydrogen Storage in Subsurface Porous Media) and FluidFlower, a meter-scale CO2 storage laboratory. His work integrates experimental and computational methods to address energy transition challenges. Recent publications explore microbial interactions in hydrogen storage, CO2 mobility control via foam, and pore-scale dynamics. He advises PhD candidates and mentors master’s students in reservoir physics and energy technology. Key collaborations involve NORCE, Equinor, and the Research Council of Norway. Labs/Teams: FluidFlower Lab, Reservoir Physics Research Group Funding: HyPE (Research Council of Norway), CSSR (Center for Sustainable Subsurface Resources)

Jerome Keatingمشاهده پروفایل

استاد

• Statistical Estimation
• Pitman Nearness Criterion
• Reliability Theory
• +۳ مورد دیگر

Jerome Keating is the Peter T. Flawn Professor of Management Science and Statistics at the Carlos Alvarez College of Business, UTSA. He holds a Ph.D. in Mathematical Sciences from the University of Texas at Arlington, alongside M.A. and B.S. degrees in Mathematics from the same institution. His roles include Undergraduate Advisor of Record and former department chair (Management Science and Statistics, Demography). He has extensive experience in academia, industry (Bell Helicopter Textron, Los Alamos National Laboratory), and editorial roles. **Education**: Ph.D. in Mathematical Sciences, University of Texas at Arlington M.A. in Mathematics, University of Texas at Arlington B.S. in Mathematics, University of Texas at Arlington His research focuses on statistical estimation criteria (e.g., Pitman nearness), reliability theory, and environmental science applications. He has published over 50 peer-reviewed articles, two books, and contributed to projects like NASA’s Magnetospheric Multiscale mission and leak detection systems for underground tanks. **Awards**: President’s Distinguished Achievement Award for Teaching Excellence (1989, 2001) Chancellor’s Council Teaching Award (1995, 2001) Fellow of the American Statistical Association (1997) Don B. Owen Award in Statistics (2006) **Consulting & Grants**: Collaborated with NASA, Southwest Research Institute, Valero, and Mobil Research on reliability analysis and statistical applications. He co-advised two doctoral dissertations and served on numerous committees. His work spans editorial roles for journals like *STATS* and *Communications in Statistics*.

Hamdi Tchelepiمشاهده پروفایل

استاد

• Reservoir Engineering
• Energy Resources
• Subsurface Fluid Flow
• +۵ مورد دیگر

Hamdi Tchelepi is the Max Steineke Professor at Stanford University and a Senior Fellow at the Precourt Institute for Energy. His academic work focuses on reservoir simulation, subsurface energy systems, and computational geosciences. He holds a Ph.D. in Petroleum Engineering from Stanford (1994), an M.S. from King Fahd University (1988), and a B.S. from the University of Petroleum and Minerals (1985). Research interests span reservoir simulation methodologies, CO2 sequestration, battery technology, and multiphase flow dynamics in porous media. His work integrates numerical methods, machine learning, and experimental validation to address challenges in energy systems and environmental engineering. Publications emphasize computational frameworks for flow-geomechanics coupling, pore-scale modeling, and data-driven approaches for subsurface processes. His articles frequently explore stability criteria, algorithmic efficiency, and scalability in reservoir simulation. Notably, Tchelepi has developed open-source tools like GEOS for multiphysics subsurface simulation and contributed to the FluidFlower benchmark study for CO2 storage validation. His research bridges microscale phenomena with macroscale reservoir engineering applications.

Kamelia Atefi-Monfaredمشاهده پروفایل

دانشیار

• Geomechanics
• Bio-mediated Ground Improvement
• Energy Geotechnics
• +۲ مورد دیگر

Dr. Kamelia Atefi-Monfared is an Associate Professor of Civil Engineering leading research on coupled processes in geomechanics. Her work integrates experimental and computational approaches to address challenges in energy geotechnics and sustainable infrastructure. Key research areas: Bio-mediated soil stabilization Thermo-hydro-mechanical coupling in porous media Tunnel engineering in extreme environments Geothermal energy systems Particle transport in deformable media Recent publications demonstrate advanced modeling of fire damage in tunnels and bio-cementation processes. Experimental work includes multi-laboratory validation of rock tensile tests. Methodologically, research features coupled chemo-thermo-hydro-mechanical models validated through laboratory testing. Awards include the 2023 TUST Best Paper Award for work on fire-induced tunnel damage. Current projects investigate fine particle migration in geothermal reservoirs and bio-cemented soil characterization.

Craig Youngمشاهده پروفایل

استاد

• Marine Biology
• Deep-Sea Ecology
• Larval Development
• +۳ مورد دیگر

Craig Young is a Professor of Biology at the University of Oregon, affiliated with the Oregon Institute of Marine Biology (OIMB). His research focuses on marine invertebrate larval biology, deep-sea and subtidal ecology, and reproductive strategies of marine organisms. He investigates topics such as larval dispersal mechanisms, symbiotic relationships, and the impacts of environmental changes on marine ecosystems. His work integrates field expeditions, laboratory studies, and computational modeling to understand biodiversity in extreme environments like hydrothermal vents and methane seeps. Recent projects include analyzing volcanic ash effects on deep-sea communities and characterizing novel species from Gulf of Mexico seeps. Young has collaborated on interdisciplinary research funded by grants addressing larval connectivity, deep-sea biogeography, and oceanographic influences on recruitment patterns. Key Research Themes: Developmental biology, marine invertebrate diversity, deep-sea ecology, larval dispersal, hydrocarbon seep ecosystems. Funding Highlights: Collaborative NSF grants on seep mussel connectivity (2010), larval dispersal modeling (2019), and symbiont-larval interactions at hydrothermal vents (2018). Publications since 2020 have explored larval behavior, methane ice worms' microbiomes, and the ecological roles of echinoderms as host organisms. His lab develops tools like the SyPRID sampler for precision deep-ocean plankton sampling, enhancing understanding of meroplankton dynamics. Young's work bridges basic science with conservation, addressing threats like microplastic pollution and climate-driven changes in ocean environments.

• 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.

• Near Surface Geophysics
• Ground Penetrating Radar (GPR)
• Landmine Detection
• +۴ مورد دیگر

Dr. Iraklis Giannakis is a Lecturer at the University of Aberdeen's School of Geosciences. He holds a PhD from the University of Edinburgh (2015), with earlier degrees from Aristotle University of Thessaloniki. His research focuses on geophysics, machine learning, and GPR applications in landmine detection, planetary science, and forestry. Dr. Giannakis is a core contributor to the open-source gprMax software, used globally for FDTD simulations. He has published over 55 articles, with recent work emphasizing lunar and Martian radar data analysis, deep learning for subsurface imaging, and planetary surface characterization. Education: Bachelor's and Master's in Geophysics, Aristotle University of Thessaloniki (2009, 2011) PhD in Geophysics from the University of Edinburgh (2015), funded by DSTL and EPSRC Research Interests: Machine learning in geophysics, GPR for landmine detection, non-destructive testing, planetary subsurface exploration, and forestry applications. His work integrates computational geophysics with advanced signal processing and inversion techniques. Key Projects: gprMax: Open-source FDTD solver for GPR Lunar/Mars radar analysis (Chang’E-4, Yutu-2 missions) COST Action TU1208 collaborative research Industry partnerships (e.g., D-Box demining tools) Awards: Best Paper Award at the 15th International Conference on GPR (2015). Grants & Collaborations: Funded by Google Fiber, EPSRC, and international partnerships. Active in journals like Icarus and Geophysics . Labs/Teams: Part of the Planetary Geophysics group at Aberdeen, contributing to lunar and Martian surface studies using radar data.

Marc Boxbergمشاهده پروفایل

پژوهشگر

• Geophysical Imaging
• Radioactive Waste Disposal
• Ice Data Analysis
• +۳ مورد دیگر

Dr. Marc Boxberg is a researcher at RWTH Aachen University's Department of Geophysical Imaging and Process Observation, affiliated with the Faculty of Georesources and Materials Engineering. His work focuses on geophysical imaging, environmental risk assessment for radioactive waste disposal, and cryosphere-related planetary exploration. He leads research into seismic wave propagation, data integration frameworks, and robotic exploration systems. Key research interests include icy moon mission preparation, subglacial access technologies, and data-driven approaches for radioactive waste management. He contributes to interdisciplinary projects like the Ice Data Hub and Cryotwin digital infrastructure. Dr. Boxberg's expertise spans geophysical modeling, mission simulation tools (e.g., NEXD software), and field-testing of cryogenic exploration hardware. His recent publications highlight advancements in multi-physical data fusion, cryobot performance analysis, and uncertainty quantification in waste repository safety. Ongoing work includes astrobiology research for icy ocean worlds and development of actionable data hubs for long-term environmental monitoring.

Michael Martinمشاهده پروفایل

پژوهشگر

• Computational Fluid Dynamics
• Heat Transfer
• Advanced Manufacturing
• +۳ مورد دیگر

Michael Martin is a nationally recognized expert in National Renewable Energy Laboratory (NREL) , specializing in Computational Fluid Dynamics , Heat Transfer , and Advanced Manufacturing . His work addresses sustainability challenges in areas like green steelmaking and quantum computing energy efficiency. He joined NREL in 2017 and serves as practicum coordinator for the DOE Computational Science Graduate Fellowship and representative in the HPC4EI program . Research interests include: Computational Fluid Dynamics Heat Transfer Optimization Sustainable Computing Advanced Manufacturing Recent publications analyze: Climate change impacts on renewable energy resources Supercritical CO2 flow dynamics Quantum data center thermal parameters Geothermal reservoir machine learning models Scientific awards include: ASME Fellow (2023) Milton Van Dyke Award (2022) New Voices Cohort (2021) Active in professional networks like: American Physical Society AIAA DOE HPC4EI

Allon Percusمشاهده پروفایل

استاد

• Discrete Optimization
• Statistical Physics
• Network Models
• +۵ مورد دیگر

Allon Percus is the Joseph H. Pengilly Professor of Mathematics at the Institute of Mathematical Sciences at Claremont Graduate University, part of the Claremont Colleges. He holds a B.A. in Physics from Harvard University (1992) and a Ph.D. in Theoretical Physics from Université Paris-Sud (1997). His career includes roles as Associate Director of UCLA's Institute for Pure and Applied Mathematics (IPAM, 2003–2006) and technical staff member at Los Alamos National Laboratory (LANL). Percus specializes in discrete optimization, statistical physics, and network models, with applications to quantum algorithms, fracture networks, and computational complexity. He co-developed the Extremal Optimization method, widely applied in interdisciplinary fields like social networks and protein folding. His research has been funded by NSF, DOE, and others. Percus organizes conferences on combinatorics and computational complexity and teaches courses in scientific computing, discrete mathematical modeling, and statistical mechanics. Education: B.A. Physics, Harvard University, 1992 Ph.D. Theoretical Physics, Université Paris-Sud, Orsay, 1997 Research Interests: Discrete optimization and algorithm performance analysis Statistical physics models for combinatorial problems Random network structures and real-world data modeling Interdisciplinary applications in quantum computing and geology Recent work emphasizes machine learning for fracture network analysis and affective polarization dynamics in social networks. His publications span computational materials science, graph theory, and epidemic diffusion modeling. Awards and Grants include major funding from the Air Force Office of Scientific Research, National Science Foundation, and Department of Energy. He has led interdisciplinary teams at LANL and organized IPAM programs fostering cross-disciplinary collaboration between mathematicians and scientists. Labs/Teams: Part of Claremont’s Institute of Mathematical Sciences and collaborates with LANL teams on computational projects. Active in organizing conferences bridging combinatorics and computational complexity fields.

Steven Hansenمشاهده پروفایل

مدرس

• Geophysics
• Seismology
• Lithosphere Structure
• +۷ مورد دیگر

Steven Hansen is a Lecturer at the School of Engineering, Macquarie University . His research focuses on geophysics and seismology, particularly Earth's subsurface structure and processes. He specializes in lithosphere dynamics, active volcanoes, and seismic wave propagation modeling. Active research in Earth mantle geodynamics Expertise in seismic data inversion techniques Collaborator in North American seismic experiments Recent projects include SmartCrete CRC (2020-2027) and DP21: Cratonic roots and mantle geodynamics (2021-2024). His work leverages geophysical multi-observables and advanced data processing to study crust-mantle interactions. He actively participates in conferences like the American Geophysical Union meetings. Research Trends His publications emphasize seismic inversion methods (2023), magnetic gradient tensor analysis (2024), and mantle structure characterization. Key themes include lithospheric discontinuities (2023), fossil slabs (2021), and volcanic earthquake dynamics. Collaborations Steven collaborates with institutions across Australia and North America, working on projects involving seismic experiments in British Columbia, the Southern Rocky Mountains, and Central California. He has contributed to 27 research outputs since 2009.

• Machine Learning
• Computer Vision
• Data Science
• +۶ مورد دیگر

Qian Wang is a Professor at Southeast University's School of Computer Science and Engineering, with significant contributions across multiple research domains in computer science and engineering. Their work spans machine learning, computer vision, data science, and optimization algorithms, demonstrating interdisciplinary expertise. Research interests focus on advancing artificial intelligence methodologies with practical applications. Key areas include neural network architectures, feature selection techniques, image processing algorithms, and optimization methods. Their work bridges theoretical foundations with real-world applications in medical imaging, environmental monitoring, and power systems. The publication record shows a strong trend toward multi-disciplinary AI applications, particularly in medical diagnostics, environmental science, and engineering systems. Recent work emphasizes robustness in optimization algorithms, efficient neural network architectures, and practical implementations of deep learning techniques for specialized domains. As an active researcher, Qian Wang has contributed to numerous collaborative projects across institutions, with publications appearing in top-tier journals and conferences including IEEE Access, Pattern Recognition, and CVPR. Their work demonstrates consistent productivity with over 100 publications in the last five years.