جست‌وجوی استاد و پژوهشگر

Ian Frigaardمشاهده پروفایل

استاد

• Non-Newtonian Fluid Mechanics
• Visco-plastic Fluids
• Petroleum Engineering
• +۳ مورد دیگر

Ian Frigaard is a Professor in the Department of Mechanical Engineering at the University of British Columbia (UBC), affiliated with the Faculty of Applied Science. He also holds an appointment in the Department of Mathematics. His research group operates in UBC's Complex Fluids Lab, focusing on interdisciplinary studies combining mathematical, experimental, and computational approaches. Education: B.Sc. (University of Wales) M.Sc. (University of Oxford) D.Phil. (University of Oxford) C.Math. (Certificate in Mathematics) Research Interests: Professor Frigaard specializes in non-Newtonian fluid mechanics, particularly the mechanics of visco-plastic (yield stress) fluids. His work addresses industrial challenges in petroleum engineering, including well cementing, leakage prevention, and abandonment techniques related to GHG emission control and environmental protection. Research methodologies span theoretical modeling, experimental validation, and computational simulations. Publication Trends: Recent work (2021–2023) emphasizes bubble dynamics in complex fluids, displacement flows in annular geometries, wellbore integrity modeling, and stochastic risk assessment for oil/gas operations. Publications frequently appear in top-tier journals like the Journal of Fluid Mechanics and Journal of Non-Newtonian Fluid Mechanics . Awards & Honors: CSME Fluid Mechanics Medal (2024) Stanley G. Mason Award, Canadian Society of Rheology (2022) Killam Research Prize, UBC (2019) Academic Leadership: Leads a research group of 10+ graduate students and postdocs. Provides summer internships and collaborates extensively with the petroleum industry. Research is supported by industrial partnerships and institutional grants. Facilities: Conducts experiments in UBC's Complex Fluids Lab, equipped for advanced rheological measurements and flow visualization.

Peter K. Kitanidisمشاهده پروفایل

استاد

• Groundwater Flow
• Hydrologic Modeling
• Inverse Problems
• +۳ مورد دیگر

Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling

Konstantinos Karapiperisمشاهده پروفایل

استادیار

• Solid Mechanics
• Multiscale Modeling
• Data Science
• +۷ مورد دیگر

Konstantinos Karapiperis is a Tenure Track Assistant Professor at EPFL's Laboratory of Multiscale Modeling of Materials (LMD), within the School of Architecture, Civil and Environmental Engineering (ENAC). His research integrates mechanics , multiscale modeling , and data science to study geomaterials and structural materials. PhD in Applied Mechanics (minor in Applied Mathematics), Caltech Postdoctoral Researcher & Lecturer, ETH Zürich (Marie Skłodowska-Curie Fellowship) Research focuses on granular materials , architected materials , and nonlocal modeling using techniques like Level-Set Discrete Element Method (LS-DEM) and machine learning . Recent work explores fracture control via graph neural networks and thermodynamics-informed models. Selected scientific award: Marie Skłodowska-Curie Fellowship Teaches courses in Soil Mechanics and Multiscale Modeling . PhD students include Thomas Henzel and Hrishikesh Gopakumar Menon. His Data-Driven Mechanics Laboratory (LMD) develops predictive tools for granular and structured material behavior.

Carlos Torres-Verdinمشاهده پروفایل

استاد

• Borehole Geophysics
• Petrophysics
• Formation Evaluation
• +۸ مورد دیگر

Carlos Torres-Verdín is a Professor and holds the Brian James Jennings Memorial Endowed Chair and Zarrow Centennial Professorship in Petroleum Engineering at The University of Texas at Austin's Hildebrand Department of Petroleum and Geosystems Engineering, within the Jackson School of Geosciences. He earned a B.S. in Geophysical Engineering from the National Polytechnic Institute of México (1983), an M.Sc. in Electrical Engineering from UT Austin (1985), and a Ph.D. in Engineering Geoscience from UC Berkeley (1991). His research focuses on petrophysical and geophysical characterization of subsurface regions using well logging, seismic, and multi-physics data. Key areas include borehole geophysics, rock physics, reservoir characterization, and hydraulic fracturing. He has received numerous accolades, including the 2020 Virgil Kauffman Gold Medal (SEG) and the 2017 Conrad Schlumberger Award (EAGE). His work integrates advanced numerical methods and machine learning to enhance reservoir evaluation and CO2 sequestration monitoring. Torres-Verdín teaches courses such as PGE 358 (Formation Evaluation) and directs the Formation Evaluation Joint Industry Research Consortium, fostering industry-academia collaboration. Awards & Honors 2020 Virgil Kauffman Gold Medal, SEG 2019 Anthony F. Lucas Gold Medal, SPE 2017 Conrad Schlumberger Award, EAGE 2017 Lockheed Martin Excellence in Engineering Teaching Award Research & Teaching His recent studies address challenges in unconventional reservoirs, fluid dynamics in nanoporous media, and real-time geosteering. He has published over 150 peer-reviewed articles, emphasizing innovation in inversion techniques, NMR applications, and reservoir simulation.

• Geohazards
• Landslides
• Debris flows
• +۵ مورد دیگر

Scott McDougall is an Associate Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC). He specializes in geohazards, particularly landslides and related risks, with a focus on improving risk assessment and mitigation strategies. His work integrates field data, statistical analysis, numerical modeling, and laboratory experiments to understand landslide dynamics. Education: PhD in Geological Engineering, UBC (2006) BASc in Civil Engineering, University of Toronto (1998) Research Interests: Landslide mobility and runout modeling Tailings dam breaches and their impacts Risk evaluation frameworks for geohazards Shoreline erosion and landslide-generated waves Applications of machine learning in hazard prediction Key Contributions: Development of the UBC Geohazards Research Team to advance landslide risk reduction Leadership in the CanBreach project to improve tailings dam breach analysis Pioneering probabilistic runout prediction models for rock avalanches and debris flows Awards: Engineering Geology Best Paper Award 2024 CDA Published Paper Award Advising & Grants: Supervised over 15 graduate students and postdoctoral fellows Active industry collaborations with mining and engineering firms Funded by NSERC, Mitacs, and industry partnerships Labs & Teams: UBC Geohazards Research Team CanBreach Collaborative Research and Development Project

James Alexandre Gouletمشاهده پروفایل

استاد

• Machine Learning
• Structural Health Monitoring
• Bayesian Methods
• +۳ مورد دیگر

James Alexandre Goulet is a Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. His research focuses on Machine Learning Methods for Civil Engineering applications such as structural health monitoring (SHM) and infrastructure maintenance planning. He leads the Canari project for online change point detection in SHM and contributes to open-source libraries like cuTAGI for Bayesian neural networks. Affiliations : Chair in Machine Learning for Infrastructure Monitoring at Polytechnique Montréal, IVADO Institute member, and GRS (Structural Engineering Research Group) member Expertise : Building engineering, structural safety, applied probability, learning theories Recent research trends include Bayesian state-space models, LSTM neural network integration for infrastructure forecasting, and uncertainty quantification in SHM systems. His work emphasizes probabilistic methods and analytical inference over black-box approaches. Teaching includes courses on structural reliability and probabilistic data analysis for civil engineers. He supervises graduate students in topics ranging from damage detection algorithms to stochastic deterioration modeling of infrastructures.

Michael Pyrczمشاهده پروفایل

استاد

• Subsurface Data Analytics
• Geostatistics
• Machine Learning
• +۳ مورد دیگر

Michael Pyrcz is a Professor in the Hildebrand Department of Petroleum and Geosystems Engineering and holds the rank of Associate Professor in the Jackson School of Geosciences at the University of Texas at Austin. He is the recipient of the B. J. Lancaster Professorship in Petroleum Engineering and the George H. Fancher Centennial Teaching Fellowship in Petroleum Engineering. His research focuses on subsurface data analytics, geostatistics, and machine learning applications in energy systems and CO2 sequestration. Pyrcz teaches widely, including through online lectures and GitHub workflows, and has authored over 50 peer-reviewed publications and a textbook on spatial data analytics. His work integrates machine learning with geoscience challenges, such as uncertainty quantification in reservoir modeling and CO2 storage site evaluation. He leads initiatives in energy data analytics through the Freshman Research Initiative and collaborates with industry on workflow development. Key research areas include generative AI for subsurface models, stochastic methods for fracture networks, and anomaly detection in geologic monitoring. Education: Background in petroleum engineering and geosciences (details not explicitly provided). Grants/Advising: Extensive industry collaboration and mentorship roles at Chevron prior to UT Austin. Labs/Teams: Maintains active GitHub repositories (GeostatsGuy), YouTube lecture series (GeostatsGuyLectures), and social media outreach (X/GeostatsGuy).

Yong Zhangمشاهده پروفایل

استاد

• Hydrogeology
• Hydrology
• Environmental Geochemistry
• +۴ مورد دیگر

Yong Zhang is a Professor in the Department of Geological Sciences at the University of Alabama, serving as Undergraduate Program Director. His research focuses on stochastic hydrology, contaminant transport in soil/water/aquifers (including heavy metals, PFAS, microplastics, and antibiotics), and surface water-groundwater interaction. He has held postdoctoral positions at the University of California, Davis; Desert Research Institute; and Colorado School of Mines. Current research includes the 'Groundwater 2070' project in Baldwin County, Alabama, addressing climate change impacts and seawater intrusion. Education: BS in Hydrogeology and Geo-Engineering (Nanjing University, 1993), PhD in Hydrology and Water Resources (Nanjing University, 1998). He teaches courses like GEO 101, GEO 306, and specialized topics such as Fractional Calculus and Hydrogeophysics. His work integrates fractional calculus with hydrogeology, yielding models for non-Fickian transport and pollutant source identification. Students under his advisement include Jonathan Frame, Chaloemporn Ponprasit, and Hossein Gholizadeh. Research outputs emphasize environmental geochemistry, numerical modeling, and groundwater sustainability. Notable collaborations involve Dr. Geoff Tick on co-advised PhD students.

Sanjay Srinivasanمشاهده پروفایل

استاد

• Petroleum Reservoir Characterization
• Reservoir Recovery Processes
• Integration of Seismic Data
• +۷ مورد دیگر

Sanjay Srinivasan is a Professor of Petroleum and Natural Gas Engineering and the John and Willie Leone Family Chair in the Department of Energy and Mineral Engineering at Penn State University. He serves as Director of the EMS Energy Institute and leads the Penn State Initiative for Geostatistics and GeoModeling Applications. His research focuses on petroleum reservoir characterization, CO2 sequestration, and integration of seismic data in reservoir models through advanced geostatistical and machine learning methods. Ph.D., Petroleum Engineering, Stanford University M.S., Petroleum Engineering, University of Southern California B. Tech, Petroleum Engineering, Indian School of Mines Srinivasan’s work addresses reservoir recovery processes, unconventional reservoirs, and subsurface energy security. His methodologies include probabilistic modeling, data assimilation, and AI-driven workflows for fracture network mapping and porous media generation. Key applications span Gulf of Mexico deepwater plays and geological carbon storage. Recent publications highlight trends in: Reinforcement learning for geostatistical workflows and well optimization Physics-informed GANs for 3D porous media modeling Probabilistic integration of geomechanical and geostatistical inferences Machine learning approaches for seismic fracture identification CO2 sequestration in heterogeneous reservoirs Scientific awards include Distinguished Member (SPE, 2022), SPE Faculty Pipeline Award (2012), Cox Visiting Fellowship (Stanford, 2010), and SPE Southwest Region Reservoir Description Award (2009).

Peter J. Thomasمشاهده پروفایل

استاد

• Systems Neuroscience
• Computational Neuroscience
• Theoretical Biophysics
• +۷ مورد دیگر

Peter J. Thomas is a Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University's College of Arts and Sciences, with secondary appointments in Electrical Engineering and Computer Science, Cognitive Science, and Biology. He serves as Co-Editor-in-Chief of Biological Cybernetics and leads the Computational Biomathematics Laboratory. Primary Affiliation: Department of Mathematics, Applied Mathematics, and Statistics Secondary Affiliations: Department of Electrical Engineering and Computer Science, Department of Cognitive Science, Department of Biology Leadership: Co-Editor-in-Chief of Biological Cybernetics Thomas earned his B.A. in Physics and Philosophy from Yale University (1990), M.S. in Mathematics from the University of Chicago (1994), and both M.A. in Conceptual Foundations of Science and Ph.D. in Mathematics from the University of Chicago (2000). His research spans mathematical neuroscience, theoretical biophysics, and information theory applications to biological systems. Thomas specializes in understanding how noise and stochasticity affect neural coding, developing mathematical frameworks for gradient sensing in cells, and applying graph theory to biological networks. His work on stochastic shielding has provided novel approaches to simplifying complex stochastic models while preserving essential dynamics. His research bridges theoretical mathematics with experimental neuroscience through collaborations with the Chiel laboratory and others. Thomas's recent publications demonstrate a strong focus on stochastic oscillators, sensory feedback mechanisms, and information theory applications to biological systems. His work consistently develops novel mathematical frameworks to address specific biological questions, with significant contributions to understanding phase dynamics in neural oscillators and information processing in biochemical signaling. Core Fulbright Scholar Program (2013) Simons Fellow in Mathematics Program (2014) Multiple NSF grants as Principal Investigator Co-Editor-in-Chief of Biological Cybernetics Thomas has mentored numerous students at all levels, from undergraduates to postdoctoral researchers. His laboratory has produced successful scholars who have gone on to faculty positions at institutions like New Jersey Institute of Technology and the University of Nevada, Reno. He has actively organized workshops at the Banff International Research Station and served on editorial boards for leading journals in computational neuroscience. The Computational Biomathematics Laboratory focuses on developing mathematical frameworks to understand neural dynamics, cellular signaling, and pattern formation. The lab maintains strong collaborations with experimental neuroscience groups and has made significant contributions to understanding rhythmic neural systems, respiratory control mechanisms, and information processing in biological systems.

James Smithمشاهده پروفایل

استاد

• Decision Analysis
• Management Science
• Operations Research
• +۴ مورد دیگر

James Smith is a Professor in Decision Science at the Tuck School of Business, Dartmouth College. He holds a PhD (1990), MS (1986), and BS (1984) from Stanford University. Education: PhD, MS, BS - Stanford University His research focuses on Decision Analysis , Operations Research , and Stochastic Optimization , with applications in energy systems, medical decision-making, and technology adoption. Recent work includes dynamic programming approaches to energy systems under uncertainty and information relaxation techniques in stochastic dynamic programs. Key publication trends: 15 most recent articles span 2025–2004, emphasizing stochastic dynamic programming , resource allocation , and risk evaluation across energy, finance, and public health domains. Scientific awards: Jack Byrne Distinguished Professor in Decision Science He has collaborated extensively in Operations Research and Management Science journals, with notable coauthors like David Brown, Canan Ulu, and Robert Winkler. His work addresses theoretical and applied problems in portfolio management, commodity pricing, and sequential decision modeling.

• Geomaterials Physics
• Soft Matter
• Structured Materials
• +۱۳ مورد دیگر

Dr. Nicolas Francois is an Associate Professor in the Department of Materials Physics at Australian National University (ANU), specializing in experimental geomaterials physics, soft matter, and fluid hydrodynamics. He leads the X-ray Tomography and Applications Research Group, combining curiosity-driven and applied research in out-of-equilibrium systems. ARC Industry Fellow (2024-2030): Improving Australian iron ore comminution for green steel production ARC DECRA Fellow (2016-2018): Biofilms in two-dimensional turbulent flows His research spans fundamental questions in: Fragmentation of solid materials Autonomous devices powered by chaotic flows Hydrodynamic waves Stochastic thermodynamics Granular matter Polymer rheology and applied areas in: Comminution of geomaterials Mechanics of fractured rocks Wave-energy conversion Environmental fluid mechanics Publications reveal a trajectory focused on X-ray tomography applications, granular dynamics, and turbulence-driven systems. He utilizes advanced imaging techniques to study material failure mechanisms and fluid-structure interactions, contributing to fields ranging from green steel production to biofilm dynamics. Current student projects and grants emphasize sustainable resource processing and fundamental fluid physics.

Liangping Liمشاهده پروفایل

دانشیار

• Hydrogeology
• Groundwater Modeling
• Data Assimilation
• +۵ مورد دیگر

Dr. Liangping Li is an Associate Professor in the Department of Geology and Geological Engineering at South Dakota School of Mines & Technology. He holds a Ph.D. from Technical University of Valencia and an M.S. from China University of Geoscience, with expertise in hydrogeology, groundwater modeling, and geothermal energy systems. Education: M.S., China University of Geoscience; Ph.D., Technical University of Valencia His research focuses on integrating machine learning with groundwater modeling, data assimilation, geostatistics, and optimization of geothermal energy systems. He has pioneered methods combining generative adversarial networks (GANs) and ensemble smoother techniques for inverse modeling in complex aquifers. Recent publications highlight his work on extremal optimization for well placement, progressive growing GANs for facies modeling, and stochastic inversion of fracture networks. His research trends emphasize computational innovation in subsurface flow simulation and sustainable groundwater management. Scientific Awards: NSF RII Track-4 Grant, NSF REU Site Grant, BLM Environmental Monitoring Grant, and appointments as Associate Editor for Advances in Water Resources and Mathematical Geosciences . Dr. Li teaches courses in groundwater engineering, statistical methods, and environmental field camp, while mentoring graduate and undergraduate researchers in subsurface energy and water resource projects.

Mustafa Onurمشاهده پروفایل

استاد

• Petroleum Engineering
• Reservoir Management
• Inverse Problem Theory
• +۴ مورد دیگر

Mustafa Onur is the McMan Professor and Chair of Petroleum Engineering at The University of Tulsa, where he directs the TU Petroleum Reservoir Exploitation Projects (TUPREP). He holds a Ph.D. and M.S. in Petroleum Engineering from The University of Tulsa and a B.S. from Middle East Technical University. Previously, he held professorships at Istanbul Technical University and Universiti Teknologi Petronas (Malaysia), including a Schlumberger Chair position. Research Focus: Dr. Onur specializes in inverse problem theory, mathematical optimization, and data science applied to reservoir management, geothermal systems, and uncertainty quantification. His work integrates machine learning with traditional reservoir engineering to solve complex problems in energy extraction and carbon sequestration. Publication Trends (2024-2025): His 15 most recent articles emphasize deep learning-based reservoir surrogates, CO₂ storage optimization, geothermal energy extraction, and constrained production optimization. Key innovations include Embed-to-Control frameworks, physics-driven interwell simulators, and stochastic optimization algorithms for uncertainty management in subsurface systems. Awards & Recognition: 2010 SPE Formation Evaluation Award 2014 SPE Distinguished Member 2018 SPE Reservoir Description and Dynamics Award Leadership: As TUPREP director, he leads advanced research in reservoir exploitation, focusing on practical applications of AI and optimization in petroleum and geothermal engineering. He serves as Associate Editor for SPE Journal and Journal of Petroleum Science and Engineering .

Gregory Ryskinمشاهده پروفایل

دانشیار

• Cosmology
• Fluid Dynamics
• Geophysics
• +۲ مورد دیگر

Dr. Gregory Ryskin is an Associate Professor in the Department of Chemical and Biological Engineering at Northwestern University. His research spans cosmology, geophysics, fluid dynamics, and theoretical physics, with a recent focus on cosmological models addressing dark energy and vacuum energy. He holds dual PhDs in Chemical Engineering (Caltech) and Theoretical Physics (St. Petersburg Polytechnic Institute). Research interests include cosmic expansion mechanisms, Earth's magnetic field generation, catastrophic geological events, and fundamental physics problems like Hawking radiation. His interdisciplinary work connects astrophysics with geophysical phenomena through fluid dynamical principles. Publications include theoretical studies of vanishing vacuum energy (2020), cosmic repulsion (2015), and Hawking radiation (2014), alongside earlier contributions to fluid dynamics of polymers and liquid crystals. His 2010 paper on abrupt Earth events received recognition from paleontologist David Raup. Dr. Ryskin's current work develops physics-based explanations for cosmological observations, providing alternatives to standard dark energy models through modified gravitational theories.