جست‌وجوی استاد و پژوهشگر

Daniel Wedgeمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Data Visualization
• +۷ مورد دیگر

Daniel Wedge is a Principal Research Fellow at the University of Western Australia's School of Earth Sciences, affiliated with the Centre for Data-driven Geoscience. He holds a Ph.D. in computer science and has industry experience in image/video processing algorithms. His research focuses on applying machine learning and computer vision to geophysical/geological datasets for automated analysis. Education: Ph.D. in Computer Science (details not specified). Research interests include computer vision, machine learning, data visualization, and their application to mineral exploration, iron ore analysis, and geophysical survey techniques. He contributes to UN Sustainable Development Goals related to industry and resources. Recent work highlights integration of neural networks with geophysical data (e.g., potential field analysis, FTIR spectroscopy), machine learning-enhanced magnetic grid resolution, and 3D geochemical modeling. His articles span 2021–2025, emphasizing interdisciplinary applications. Award: Vice Chancellor’s Award in Impact and Innovation (2015) Key grants include projects on data fusion for drillhole analysis (2018–2021), geological interpretation tools (2014–2017), and reducing 3D geological uncertainty (2014–2018). He collaborates with industry partners like Technological Resources Pty Ltd and the Geological Survey of Western Australia. His work is centered at the Centre for Data-driven Geoscience, advancing computational methods for geoscientific challenges.

Eugene Morganمشاهده پروفایل

عضو هیئت علمی

• Bayesian Inversion
• Seismic Data Analysis
• Reservoir Characterization
• +۵ مورد دیگر

Eugene Morgan is an Associate Teaching Professor and Associate Head for Undergraduate Education at the John and Willie Leone Family Department of Energy and Mineral Engineering at Penn State University. He also serves as Vice-Chair of Energy Engineering. Ph.D. in Civil & Environmental Engineering from Tufts University (2011) M.S. in Civil & Environmental Engineering from Tufts University (2008) B.S. in Earth Sciences from U.C. Santa Cruz (2005) Morgan's research focuses on: Bayesian inversion strategies for seismic survey data analysis Integration of seismic attenuation and rock physics modeling Machine learning applications in oil and gas production forecasting Geostatistical methods for subsurface energy reservoir characterization Development of probabilistic frameworks for hydraulic property estimation His recent work explores seismic data assimilation for carbon storage monitoring, time-series analysis of well production data, and spatial modeling for unconventional resource estimation. Publications highlight his contributions to: Attenuation-based inversion techniques Big data analytics in formation evaluation Multiscale geophysical risk mapping Offshore wind energy statistical modeling Key professional activities include: Participating member of National Risk Assessment Partnership (2017-2022) Guest Editor for Mathematical Geosciences (IAMG 2019 Special Issue) Scholarship Chair for SPE Pittsburgh Section (2014-2019) Faculty advisor for Penn State SPE Student Chapter (2014-2018)

• Remote Sensing
• Geographical Information Systems (GIS)
• Glacio-hydrology
• +۱۱ مورد دیگر

Dr. Anshuman Bhardwaj is a Senior Lecturer at the University of Aberdeen within the School of Geosciences and Department of Geography and Environment . He is actively involved in research areas related to remote sensing, GIS, and planetary science, with a focus on Martian landforms and glacial hazards. Research Interests : Remote Sensing, GIS, Martian Surface Processes, Glaciology, Coastal Dynamics Teaching : Courses on GIS evolution, environmental mapping, and geophysics Publications : 15+ recent works on urban expansion modeling, glacier dynamics, and Mars mapping Current Projects include GLOF resilience strategies, Himalayan glacier monitoring, and UAV-based environmental monitoring.

Hamid Darabiمشاهده پروفایل

پژوهشگر

• Geosciences
• Environmental sciences
• GIS
• +۱ مورد دیگر

Hamid Darabi is a Postdoctoral Researcher at the Department of Geosciences and Geography within the Faculty of Science at the University of Helsinki. His research spans multiple environmental and geospatial disciplines, with a focus on applying advanced computational methods to analyze environmental systems and dynamics. He maintains an active research profile with several publications appearing in 2024 and 2025 in high-impact environmental science journals. Dr. Darabi's research interests center on the application of geospatial technologies to pressing environmental challenges. His work integrates remote sensing , GIS , and machine learning approaches to address complex environmental phenomena. Key research themes include climate change impacts on mountain ecosystems, drought susceptibility mapping, vegetation dynamics monitoring, wildfire effects on specific habitats, and water resource management challenges. His methodological expertise spans both traditional geospatial analysis and cutting-edge deep learning applications for environmental data. Analysis of Darabi's recent publication record (2024-2025) reveals a strong interdisciplinary approach combining environmental science with computational methods . His work demonstrates increasing sophistication in applying artificial intelligence to environmental challenges, particularly in drought susceptibility mapping and vegetation trend analysis. The research consistently addresses real-world environmental management issues while advancing methodological approaches in geospatial analysis. His collaborations span multiple international institutions, reflecting the global relevance of his research topics.

• Artificial Intelligence
• Statistical Relational Learning
• Probabilistic Reasoning
• +۴ مورد دیگر

David Poole is a Professor of Computer Science at the University of British Columbia, where he has established himself as a leading researcher in Artificial Intelligence with an h-index of 52 and over 19,000 total citations. His academic career spans more than three decades, with publications dating from 1985 to 2025, demonstrating sustained research productivity and relevance in the field. Professor Poole's research focuses on the integration of logical and probabilistic approaches to artificial intelligence. His work bridges symbolic AI and statistical methods, creating frameworks that handle uncertainty while maintaining logical expressiveness. He has made seminal contributions to probabilistic Horn abduction, Bayesian networks, and statistical relational artificial intelligence. His theoretical work on the Independent Choice Logic (ICL) has provided foundational frameworks for representing and reasoning with uncertainty in complex domains. His applied research spans geological analysis, educational technology, and mineral exploration. Professor Poole has authored influential textbooks including 'Computational Intelligence: A Logical Approach' (1998, 1,915 citations) and 'Artificial Intelligence: Foundations of Computational Agents' (2010, 1,750 citations), which have shaped AI education globally. His recent publications (2020-2025) demonstrate continued innovation in knowledge hypergraphs, relational neural networks, and geological hazard mapping applications. His most impactful research areas include: Statistical Relational Learning (as evidenced by the 2016 book 'Statistical Relational Artificial Intelligence') Knowledge Graph Embeddings and Hypergraphs CP-nets for preference representation (1,293 citations) Lifted probabilistic inference techniques Applications of AI in geological hazard prediction Professor Poole maintains active research collaborations with institutions worldwide, including Google DeepMind, universities in Hong Kong, Germany, and the United States. His work continues to influence both theoretical foundations and practical applications of artificial intelligence.

• Computer Graphics
• Virtual Reality
• 3D Terrain Modeling
• +۲ مورد دیگر

Oscar Argudo Medrano is a researcher at the Universitat Politècnica de Catalunya's Facultat d'Informàtica de Barcelona (FIB) , affiliated with the ViRVIG research group (Visualization, Virtual Reality, and Graphic Interaction). His work focuses on computer graphics and 3D terrain modeling , with recent publications in Computer Graphics Forum , ACM Transactions on Graphics , and Cartography and Geographic Information Science . He collaborates extensively with Carlos Andújar, Antonio Chica, and Nuria Pelechano. Email: oargudo@cs.upc.edu Research Areas : 3D terrain analysis and synthesis Environmental simulation Vegetation and ecosystem modeling Procedural content generation Cultural heritage digitization Recent Article Trends : His recent publications emphasize terrain descriptors , glacier modeling , tree variations , and mechanical weathering simulations , with applications in virtual reality , digital heritage , and ecosystem visualization . Collaborative projects like European Cloud for Heritage OpEn Science highlight his work in cultural preservation and environmental impact analysis .

Alfred Akisanyaمشاهده پروفایل

استاد

• Solid Mechanics
• Fracture Mechanics
• Structural Integrity
• +۱۲ مورد دیگر

Professor Alfred Akisanya is a Personal Chair in the School of Engineering at the University of Aberdeen, where he conducts research and teaches in solid mechanics, structural integrity, and materials engineering. He holds a PhD from the University of Cambridge and is a Chartered Mechanical Engineer and Fellow of the Institution of Mechanical Engineers (IMechE). His research is highly applied, focusing on oil and gas well integrity, corrosion, and mechanical behavior of materials under extreme conditions. Education: BSc (Honours) Mechanical Engineering, University of Ibadan, Nigeria (1985) MSc Mechanical Engineering, University of Ibadan, Nigeria (1987) PhD Solid Mechanics, University of Cambridge, UK (1992) His research interests include micromechanics, fracture of bonded joints, structural integrity, corrosion, mechanics of powder compaction, and deformation of energy-absorbing structures. He has led and participated in numerous funded projects with industry partners such as Shell, Chevron, Total, and PTDF. His recent work explores chemo-thermo-poromechanical modeling, pipeline integrity, and microbiologically influenced corrosion. The trends in his recent publications indicate a strong focus on offshore infrastructure longevity, corrosion mechanisms in marine environments, geomechanics of reservoir rocks, and advanced modeling techniques for structural reliability. His work integrates experimental, computational, and field-relevant approaches to solve industrial challenges in energy and materials sectors. Scientific Awards and Recognitions: Fellow of the Institution of Mechanical Engineers (FIMechE) Chartered Mechanical Engineer (CEng) He has served as an external PhD examiner for leading universities including Cambridge, Oxford, and NTU Singapore, and is an Associate Editor for the International Journal of Mechanical Engineering Education . He has supervised multiple KTP and industry-funded projects, demonstrating strong academic-industry collaboration. He also contributes as a referee for top journals and grant proposals for EPSRC. Professor Akisanya is actively involved in teaching, delivering courses in stress analysis and solids and structures. He is currently accepting PhD students and continues to lead cutting-edge research in mechanical and petroleum engineering domains. His laboratory and research group collaborate with institutions and companies worldwide, focusing on real-world applications in energy and infrastructure.

Rachel Brackenridgeمشاهده پروفایل

مدرس ارشد

• Sediment Transport
• Contourites
• Petroleum Geology
• +۷ مورد دیگر

Dr. Rachel Brackenridge is a Senior Lecturer at the School of Geosciences, University of Aberdeen, based at Kings College. Her work integrates sedimentology, petroleum geology, and geohazards research with a focus on sustainable energy solutions aligned with UN SDGs. She specializes in contourite systems, subsurface storage (e.g., hydrogen, CCS), and energy transition technologies. Her teaching includes MSc Sustainable Energy Geoscience program leadership and courses on geophysics, reservoir modeling, and subsurface storage. Research interests span sediment transport dynamics, paleoceanographic influences on margin evolution, and applying 'Big Data' and machine learning in subsurface characterization. Notable projects include studies on North Sea hydrocarbon prospectivity, Indonesian submarine landslide mechanisms, and basin-scale storage potential in Permian evaporites. She co-authored a special issue on contourites and pioneered virtual field trip methodologies for geoscience education. Key contributions include defining play fairways in the Mid North Sea High and demonstrating the critical role of contourites in hydrocarbon reservoirs. Her work bridges academia and industry, addressing energy equality and subsurface utilization for renewable energy storage.

Yaniss Guigozمشاهده پروفایل

مدرس

• Geographic Information Systems (GIS)
• Spatial Data Infrastructures (SDI)
• Environmental Informatics
• +۱۰ مورد دیگر

Dr. Yaniss Guigoz is a Lecturer in the GeoHealth group at the Institute of Global Health, University of Geneva, and an expert in Geographic Information Systems (GIS) and Spatial Data Infrastructures (SDI) at UN Environment/GRID-Geneva. He is also affiliated with the enviroSPACE and GeoHealth laboratories at the University of Geneva, where he contributes to research and teaching in geomatics and environmental sciences. He coordinates the Certificate of Advanced Studies (CAS) in 'Geomatics for a Sustainable Environment' at the university. PhD in Environmental Sciences, University of Geneva (2016) Certificate of Specialization in Geomatics, University of Geneva Bachelor/Master in Geography, University of Geneva Dr. Guigoz's research focuses on the development and application of Spatial Data Infrastructures (SDI) and Earth Observation technologies to address environmental and global health challenges. His work spans geospatial data integration, accessibility modeling, climate variables, and sustainable development. He applies GIS to public health projects, such as optimizing emergency obstetric care networks and modeling access to urban green spaces. His research is deeply interdisciplinary, combining environmental science, computer science, and health geography. His recent publications (2017–2025) reflect a strong trend in leveraging Earth Observation data cubes, cloud-based processing, and open geospatial platforms to support sustainable development and environmental monitoring. Topics include shoreline detection, snow cover evolution, air temperature forecasting, and urban green space accessibility, with applications across Europe, Africa, and the South Caucasus. The work frequently involves international collaboration and contributes to UN-led initiatives and SDGs. Dr. Guigoz has been involved in several major international research projects, including FP7 (AFROMAISON, ECOArm2ERA, IASON, EOPOWER), H2020 ECOPOTENTIAL, and SCOPES initiatives (ARPEGEO, Sustainable Caucasus). He has also contributed to capacity-building efforts through training and teaching in GIS and SDI, particularly in Africa. He has held professional positions at the World Health Organization (2000–2010) and has been continuously engaged with UN Environment/GRID-Geneva since 2005 as a GIS officer, webmaster, and SDI specialist. His dual affiliation with the United Nations and the University of Geneva enables impactful translational research bridging science and policy.

Mika Murtojärviمشاهده پروفایل

عضو هیئت علمی

• Algorithms
• Programming
• Machine Learning
• +۸ مورد دیگر

Mika Murtojärvi is a Senior Researcher and University Teacher in Software Engineering at the University of Turku. His work bridges research and education in computer science, with a focus on algorithms, machine learning, and software systems. He holds a PhD in Computer Science (2016) and has over a decade of teaching experience. Research Interests: His research spans algorithm development for applications in computational geometry, biomedical informatics, e-learning, and parallel computing. Key areas include survival prediction in cancer, EEG-based neurological classification, GPU-accelerated geographic algorithms, and platform-independent educational tools. His work often integrates real-world data with machine learning and optimization techniques. The recent publications highlight a strong trend in applying algorithmic and machine learning methods to healthcare and environmental challenges. Topics such as survival prediction in prostate cancer, EEG-based Parkinson’s classification, and GPU-optimized coastal algorithms reflect interdisciplinary research at the intersection of computer science, medicine, and geosciences. The use of R packages and e-learning systems also shows a commitment to practical, software-based solutions. Scientific Awards: No awards mentioned in the text. Advising and Grants: Mika supervises BSc theses and has taught various core computer science courses. While specific grants are not listed, his research in biomedical and environmental computing suggests involvement in funded projects. He has contributed to tools like ePCR, indicating software development for research dissemination. Labs and Teams: He collaborates with researchers such as Antti Airola, Tapio Pahikkala, and Jari Björne, suggesting active participation in research teams focused on machine learning and biomedical informatics. His work on GPU algorithms also indicates ties to high-performance computing groups.

• Structural Geology
• Tectonics
• Mineral Deposit Discovery
• +۳ مورد دیگر

Dr. Alex Farrar is an Adjunct Researcher at the University of Tasmania's School of Natural Sciences. His research focuses on structural geology, tectonics, and the application of machine learning to mineral exploration. He specializes in the tectonic controls of porphyry copper deposits in the central Andes and has contributed to models explaining lithospheric architecture's role in deposit localization. His work bridges traditional geological field studies with advanced computational methods. Education: PhD in Geology from the University of Tasmania (2024 thesis). Research Interests: Structural analysis of ore deposits, machine learning in geoscience, and the interplay between tectonics and mineralization. His recent studies emphasize AI's potential to revolutionize critical mineral discovery. Key Collaborations: Co-authored papers with experts like R.S. Davies (machine learning) and D.R. Cooke (tectonic modeling). His 2023 model on central Andean lithospheric architecture has been widely cited in economic geology. Advising & Grants: No formal advisees listed, but collaborative grants evident through multi-author publications. Active in conferences like the Australian Earth Sciences Convention (2021). Labs/Teams: Affiliated with the CODES ARC (Centre for Ore Deposit Excellence) and contributes to interdisciplinary teams focused on Andean geology and AI applications.

R. Saravananمشاهده پروفایل

استاد

• Climate Modeling
• Atmospheric Dynamics
• Ocean-Atmosphere Interaction
• +۳ مورد دیگر

Professor R. Saravanan serves as Department Head in Texas A&M University's College of Geosciences. His research focuses on climate dynamics, ocean-atmosphere interactions, and climate modeling with emphasis on tropical cyclones, midlatitude storms, and low-frequency variability. He investigates how phenomena like El Niño and mesoscale ocean eddies influence weather patterns. Education: PhD (Atmospheric and Oceanic Sciences), Princeton University, 1990 MSc (Physics), Indian Institute of Technology, Kanpur, 1986 Postdoctoral Research, University of Cambridge (Applied Mathematics & Theoretical Physics), 1990-1993 Research Interests: Climate predictability across seasonal to millennial timescales, coupled ocean-atmosphere systems, and large-scale climate dynamics. Current projects explore the impact of ocean mesoscale eddies on midlatitude storms, statistical/machine learning approaches for rainfall analysis, and Atlantic tropical cyclone variability linked to ENSO and AMM modes. Key Contributions: Over 50 peer-reviewed publications including seminal works on ocean-weather linkages (e.g., Kuroshio eddies influencing Pacific weather patterns) and tropical cyclone modulation by climate modes. Active in climate model development through collaborations with NOAA and international initiatives. Awards: College of Geosciences Distinguished Research Award (2015) PIRATA Science Steering Committee member since 2010 Grants & Leadership: Principal investigator on NSF-funded projects exploring high-resolution climate modeling and coupled system interactions. Served as Journal of Climate editor (2007-2010) and NRC climate prediction committee member (2009).

Pejman Tahmasebiمشاهده پروفایل

دانشیار

• Geomechanics
• Subsurface Systems
• Machine Learning
• +۱۲ مورد دیگر

Dr. Pejman Tahmasebi is an Associate Professor in the Department of Geophysics at the Colorado School of Mines, where he leads research in geomechanics, subsurface systems, and machine learning applications. He is affiliated with the G4 Lab (tahmasebi.mines.edu), focusing on advanced computational modeling and data-driven methods for earth sciences. His research interests span geomechanics (from granular particles to large-scale systems and THMC processes), subsurface systems (energy, water, storage) using computational modeling and multiple-point statistics, machine learning with physics-guided AI frameworks, and fluid flow in porous media . His work integrates statistical, multiscale, and data analytics techniques to solve complex geoscientific challenges. Dr. Tahmasebi has received several prestigious awards, including international recognitions from IAMG and EAGE as a rising star, regional SPE awards in Reservoir Description and Data Analytics, and the Elsevier Best Paper Award. He serves on the editorial boards of Water Resources Research , Computers & Geosciences , and Fluids , handling submissions in machine learning and subsurface modeling. He has conducted postdoctoral research at Stanford University and the University of Texas at Austin, and worked at Caltech in the Department of Mechanical and Civil Engineering. His educational background includes a Ph.D. from a joint program supervised by Prof. Muhammad Sahimi at the University of Southern California, focusing on data analytics for porous media modeling. Dr. Tahmasebi advises graduate students and leads the G4 Lab, which specializes in developing novel statistical and multiscale techniques for geoscientific applications. His lab actively contributes to advancing physics-informed AI and uncertainty quantification in subsurface systems.

Nadja Rayمشاهده پروفایل

استاد

• Multiscale Modeling
• Reactive Flow and Transport in Porous Media
• Soil Science
• +۳ مورد دیگر

Professor Nadja Ray leads the Chair of Geomatics and Geomathematics at the Catholic University of Eichstätt-Ingolstadt. She specializes in multiscale modeling of reactive flow and transport in porous media, with a focus on soil systems. Alumnus of Friedrich-Alexander University Erlangen-Nürnberg (FAU), where she earned her doctorate in 2013 and habilitation in 2020 Key research areas: soil microstructure analysis, mathematical modeling for environmental applications, and computational geosciences Her work addresses critical issues in energy sustainability, climate science, and environmental protection through mathematical frameworks that bridge pore-scale and macro-scale soil behavior. Recent publications emphasize 3D microstructure modeling, machine learning applications for soil physics, and DFG-funded projects on rhizosphere dynamics. Awards include the Dr.-Klaus-Körper Prize (GAMM), Faculty Women’s Award (FAU), and FAU Habilitation Prize. She actively collaborates with geoscientists and leads DFG projects on soil systems, while contributing to the newly established Mathematical Institute for Machine Learning and Data Science (MIDS).

• Climate-induced Geohazards
• Artificial Intelligence in Geoscience
• Machine Learning Applications
• +۷ مورد دیگر

Dr. Te Pei is an Assistant Professor in the Department of Civil Engineering at The Grove School of Engineering, The City College of New York (CCNY). His research integrates geoscientific domain knowledge with artificial intelligence and machine learning to advance the understanding and forecasting of climate-induced geohazards, aiming to enhance the resilience of natural and built environments under climate change. His research interests span several key areas in geotechnical engineering and data-driven geoscience, including: Climate-induced geohazards and their causality AI/ML applications in geoscience and civil infrastructure Soil-structure interaction Granular mechanics Data-driven modeling in concrete science Numerical modeling and simulation His methodological expertise combines machine learning, numerical modeling, and laboratory testing to address complex geotechnical challenges. Dr. Pei has secured significant research funding, including: Recipient of the 2024 CUNY Interdisciplinary Research Grant Principal Investigator (PI) on a three-year project recently recommended for funding by the National Science Foundation (NSF) These grants support his ongoing work in developing predictive models for geohazards and advancing sustainable infrastructure resilience. He has authored over 20 journal and conference publications, reflecting active contributions to geotechnical engineering, geomechanics, and climate-resilient infrastructure. While specific titles are not listed, his publication record demonstrates strong interdisciplinary engagement at the intersection of civil engineering, climate science, and artificial intelligence. Dr. Pei's educational background includes: Ph.D. in Civil Engineering – Pennsylvania State University (2023) B.S. in Civil Engineering – Oklahoma State University (2017) B.S. in Civil Engineering – Southwest Jiaotong University (2017)