Katherine Duncker Romanak is a Research Professor at the Bureau of Economic Geology within The University of Texas at Austin , specializing in geochemical monitoring for geological CO₂ storage. She has led environmental monitoring for major CCS projects, pioneered process-based soil gas monitoring methods, and contributed to global CCS regulations. Education : PhD in Geology (UT Austin, 1997), MS in Geology (UT Arlington, 1988), BS in Geology (Southern Methodist University, 1984) Her research focuses on CO₂ leakage detection, isotope systematics, vadose zone dynamics, and groundwater protection. She develops innovative protocols for environmental monitoring and stakeholder engagement, with applications in marine and terrestrial CCS projects. Her recent publications examine monitoring complexity impacts on stakeholder acceptance, AI-driven anomaly detection in soil gas data, and regulatory frameworks for CCS compliance. She holds two patents for process-based CO₂ detection systems and has contributed to policy development at the UNFCCC COPs and California's CCS protocols.
Professor Peyman Mostaghimi is a Professor of Minerals and Energy Resources at the University of New South Wales (UNSW) Sydney within the Faculty of Engineering, School of Civil and Environmental Engineering. He joined UNSW in 2014 after completing postdoctoral research at Imperial College London, where he focused on multiphase flow and transport in porous media. Professor Mostaghimi earned his PhD from Imperial College London and holds an MSc in Mechanical Engineering with specialization in Fluid Mechanics along with dual BSc degrees in Mechanical and Chemical (Reservoir) Engineering from Sharif University of Technology. His educational background has provided a strong foundation for his interdisciplinary research at the intersection of engineering, geoscience, and computational methods. His research spans computational fluid dynamics, natural gas extraction, subsurface reservoir simulation, pore-scale modeling of displacement processes, and flow and transport in porous media with applications to energy extraction, mineral recovery, subsurface hydrology, and environmental studies. Mostaghimi's work integrates advanced computational techniques with experimental validation to address complex challenges in subsurface resource management. He has pioneered approaches combining machine learning with traditional numerical methods to enhance the accuracy and efficiency of digital rock physics simulations. His recent publications (2024-2025) demonstrate a strong focus on multi-scale modeling approaches, bridging pore-scale phenomena to continuum-scale applications. His research group has made significant contributions to understanding multiphase flow in porous media, hydrogen storage, CO2 sequestration, and mineral characterization using cutting-edge imaging and computational techniques. The trend shows increasing integration of machine learning and deep learning techniques with traditional physics-based modeling to solve complex subsurface engineering problems. Professor Mostaghimi has received international recognition including being ranked among the world's top 2% scientists in Energy by Stanford University (2020). He serves as a Council Member for the International Society for Porous Media and as an Associate Editor for the Journal of Petroleum Science and Engineering. His professional affiliations include active membership in SPE (Society of Petroleum Engineers), InterPore, AGU (American Geophysical Union), and IAMG (International Association for Mathematical Geosciences). As director of the MUTRIS research group (http://mutris.unsw.edu.au), Professor Mostaghimi leads a team investigating multiphase flow and transport in porous media. His research has significant implications for energy transition technologies including carbon capture and storage, hydrogen storage, and sustainable mineral extraction. The group's work combines experimental techniques like X-ray micro-CT imaging with advanced computational methods to develop predictive models of subsurface processes.
L. Oostwegel is a Researcher at GFZ German Research Centre for Geosciences, working within the Seismic Hazard and Risk Dynamics department. The researcher specializes in earthquake exposure modeling, building stock characterization, and multi-hazard risk assessment using geospatial data and open-source information. Oostwegel's research focuses on seismic hazard and risk assessment, with particular emphasis on building exposure modeling using OpenStreetMap and remote sensing data. Key research areas include earthquake risk modeling, flood risk assessment, landslide risk management, and the development of tools for multi-hazard risk assessment. The researcher has made significant contributions to understanding building stock characteristics at various scales, from individual buildings to global assessments. Analysis of recent publications reveals a strong focus on utilizing volunteered geographic information and Earth observation datasets for risk assessment. Oostwegel has developed methods for assessing the completeness of OpenStreetMap building data globally and has created tools like 'risk-calculator' for multi-hazard risk assessments. The research shows an interdisciplinary approach combining geophysics, geospatial analysis, and disaster risk reduction. Top-down or bottom-up in earthquake exposure modeling (2025) Safe Haven – Landslides: A Serious Game for Enhancing Risk Awareness (2025) A model of European buildings (2024) From Shelters to Skyscrapers: Worldwide Building Exploration (2024) Seismic loss assessment sensitivity study (2023) Oostwegel has collaborated extensively with researchers including Evaz Zadeh, T., Schorlemmer, D., and others across multiple projects focused on natural disaster risk assessment. The research has practical applications in urban planning, disaster risk reduction, and climate adaptation strategies, particularly for coastal megacities and areas prone to seismic activity.
Diana Acero Allard is a Researcher III-Geoscience at the National Renewable Energy Laboratory (NREL), where she leads projects focused on techno-economic analysis for subsurface storage systems and geothermal resource assessment . Her work bridges advanced geothermal technologies with social and economic equity considerations. Education: Master's in Geophysics and Bachelor's in Geology from Universidad Nacional de Colombia. Her research interests include: Advanced characterization of geothermal systems using machine learning algorithms Utilization of volatile components from fluid inclusions for production prediction in conventional geothermal systems Low-temperature geothermal solutions for decarbonization Workforce development and skills transition between oil & gas and geothermal industries Geothermal energy’s role in social justice and local economies Diana's publications highlight her expertise in geothermal district energy systems , thermal storage optimization , and techno-economic modeling , particularly for data center cooling and ambient-temperature loop applications. Her collaborative research spans geothermal engineering, reservoir modeling, and energy policy frameworks. Professional affiliations include membership in networks like: Asociación Geotérmica Colombiana GeoLatinas Geothermal Rising Women in Geothermal
Roles and Affiliations : Professor of Mathematics at New Mexico Tech. Member of the Mathematics Department since at least 1992, actively involved in teaching and research. Maintains a research-focused website with links to publications, projects, and codes. Education : BS in Computer Science, MS and PhD in Mathematics from Rensselaer Polytechnic Institute. Research Interests : Specializes in optimization and inverse problems, including linear/nonlinear programming, geophysical inverse problems, and applications in hydrology and geophysics. Co-authored a widely used textbook Parameter Estimation and Inverse Problems (3rd ed., 2018). Active in developing computational tools like CSDP for semidefinite programming and CRONUScalc for cosmogenic nuclide dating. Publications : Over 50 peer-reviewed articles in journals such as Computational Optimization and Applications , Soil Science Society of America Journal , and International Journal of Remote Sensing . Focus areas include optimization algorithms, geophysical modeling, and remote sensing applications. Consulting and Grants : Available as a consultant for numerical analysis and inverse problems. Supervised projects involving data assimilation, convex optimization, and compressive sensing. Mentored students on funded research projects. Labs/Teams : Collaborates with interdisciplinary teams in geophysics (e.g., Rick Aster, Cliff Thurber) and soil science (Jan Hendrickx). Involved in sensor test facilities for landmine detection and environmental monitoring.
Fauzia Ahmad is an Associate Professor in the Department of Electrical and Computer Engineering at the College of Engineering, Temple University. She has previously held the position of Research Professor and Director of the Radar Imaging Lab at Villanova University. Her research is supported by major U.S. federal agencies, with over $7M in awarded research funding as Principal or Co-Principal Investigator. Ph.D. in Electrical Engineering, University of Pennsylvania, 1997 Dr. Ahmad's research focuses on statistical signal and array processing , computational imaging , and multi-modal sensing . Her work spans applications in through-the-wall radar , ground-penetrating radar , remote patient monitoring , and structural health monitoring . She employs advanced techniques in compressive sensing , sparse reconstruction , and machine learning to solve real-world sensing challenges. The recent publications highlight a strong trend in radar micro-Doppler signature analysis for human activity recognition, coprime array signal processing for super-resolution, and tensor decompositions in MIMO radar and communications. Her work increasingly integrates machine learning with traditional signal processing for robust detection and classification in noisy, real-world environments. Dr. Ahmad has received several prestigious honors: Fellow of the IEEE Fellow of the SPIE Chair of the IEEE Dennis J. Picard Medal Committee (2018-2020) She has served as an Associate Editor for multiple top-tier journals including IEEE Transactions on Signal Processing , IEEE Transactions on Aerospace and Electronic Systems , and IEEE Transactions on Computational Imaging , where she is currently a Senior Area Editor. She has led major conference series such as the SPIE Compressive Sensing and SPIE Big Data conferences. Her research is conducted through the Multi-modal Sensing and Imaging Lab , where she mentors students and collaborates on interdisciplinary projects involving radar, communications, and biomedical applications.
Dr. Fahimeh Mirchooli is a postdoctoral researcher in the Department of Geography at the University of Bonn, Germany, working within the AG Klaus research group. Her work focuses on hydrology, soil erosion, and sustainability of catchment systems. She holds a PhD in Natural Resource Engineering from Tarbiat Modares University, Iran, and has held academic research roles at Sari Agricultural Sciences and Natural Resources University and Hakim Sabzevari University. PhD, Natural Resource Engineering (Watershed Science and Engineering), Tarbiat Modares University, Iran, 2020 Visiting Researcher, University of Salzburg, Austria, 2019 MSc, Natural Resource Engineering (Watershed Management), Isfahan University of Technology, Iran, 2013 BSc, Natural Resource Engineering (Rangeland and Watershed Management), University of Tehran, Iran, 2011 Dr. Mirchooli's research centers on soil erosion and conservation , sediment-water interactions , spatial modeling using machine learning , and catchment health and sustainability . She integrates geospatial analysis, environmental modeling, and data science to assess land degradation, ecosystem services, and environmental risk. Her work often involves remote sensing, GIS, and hybrid modeling approaches combining empirical and machine learning techniques. Her recent publications (2018–2024) reflect a strong trend in environmental risk modeling , land degradation assessment , and machine learning applications in hydrology and soil science . Topics include gully erosion susceptibility, dust emission risk, flood modeling, and watershed sustainability. The interdisciplinary nature of her research spans ecology, geoscience, agricultural science, and public health . Scientific awards and honors include: Ranked 1st among PhD students in Watershed Management Sciences and Engineering (2020) 6th rank in national PhD entrance exam for Watershed Management (2015) George Forster Fellowship (2025) Sabbatical Scholarship from Ministry of Science, Research and Technology, Iran (2019–2020) Dr. Mirchooli has received research grants including the George Forster Fellowship and a Sabbatical Scholarship. While no formal advisees are listed, her collaborative publication record indicates active mentorship and team-based research. She is involved in hydrology and sustainability research within the AG Klaus group, contributing to projects on catchment health, erosion modeling, and environmental monitoring.
Jon Woodruff is a Professor in the Department of Earth, Geographic, and Climate Sciences at the University of Massachusetts Amherst, where he also serves as University Co-Director of the USGS Northeast Climate Adaptation Science Center and is a member of the Leadership Circle for the NSF Center for Braiding Indigenous Knowledges and Science. His research focuses on coastal hazards, sediment dynamics, and climate change impacts on coastlines. His research interests center on understanding how coastlines evolve under the combined pressures of climate change and human activity. He investigates sea-level rise, storm frequency and intensity, sediment transport, and coastal erosion, with an emphasis on developing science-based strategies for mitigation and adaptation. His work integrates field observations, geophysical data, and modeling approaches to predict future coastal change. Jon Woodruff's scholarly output reflects a strong focus on coastal geomorphology and climate resilience, though specific publications are not listed in the provided text. His work spans disciplines including Earth Sciences, Environmental Science, and Climate Policy, with clear societal applications in hazard preparedness and sustainable coastal management. He is actively involved in major federally funded science centers focused on climate adaptation and the integration of Indigenous and scientific knowledge systems. Jon leads the Sediment and Coastal Dynamics Lab (Sedimentology and Coastal Hazards Groups), a research team dedicated to studying sediment transport, paleotempestology, and coastal vulnerability. The lab supports graduate research and interdisciplinary collaboration across geosciences and climate policy.
Graeme J. Marlton is a researcher in atmospheric physics, affiliated with the University of Reading, where he completed his PhD in 2016. His work centers on atmospheric electricity, gravity waves, and the development of balloon-borne instrumentation for atmospheric measurement. He has published extensively in leading journals such as Geophysical Research Letters , Physical Review Letters , and Philosophical Transactions of the Royal Society A . Research Interests: Dr. Marlton's research spans a range of topics in atmospheric science, including cloud electrification, charge emission, precipitation modification, infrasound, and atmospheric turbulence. He applies both theoretical and experimental approaches, often involving novel instrumentation deployed on balloons or drones. The most recent articles highlight a consistent focus on atmospheric electricity , particularly in clouds, dust, and volcanic plumes, and the use of remote and balloon-borne sensing to study gravity waves and ionization effects. There is a strong interdisciplinary thread, combining physics, meteorology, and engineering, with applications in climate science and geoengineering. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: The text does not list any students or formal advisees. There is no mention of grants or funding sources, though his involvement in large collaborative projects (e.g., ARM Tethered Balloon System) suggests participation in funded research initiatives. Labs and Teams: While no specific lab or research group is named, Dr. Marlton appears to be part of a collaborative network at the University of Reading, working closely with researchers such as R. G. Harrison, K. A. Nicoll, and A. Charlton-Perez on atmospheric instrumentation and phenomena.
Muhammed Anik is a Researcher and Teacher in the Department of Earth Sciences at Utrecht University's Faculty of Geosciences. His work focuses on experimental rock deformation, utilizing the Earth Simulation Lab facilities located at Princetonlaan 4, Utrecht. He can be contacted via m.anik@uu.nl or +31 30 253 2103. Research interests center on understanding deformation mechanisms in geological materials under controlled laboratory conditions, contributing to broader applications in tectonics and geodynamics. His experimental approaches likely involve high-pressure/temperature apparatus to simulate crustal processes. No awards or grants are explicitly mentioned in the provided information. The Earth Simulation Lab serves as his primary research environment, though specific lab projects or collaborations are not detailed here.
Tony Zhongshun Shi is an Assistant Professor in the Department of Industrial and Systems Engineering at the Tickle College of Engineering, University of Tennessee Knoxville (UT). He directs the System-driven AI Lab (SAIL) and is an Affiliated Faculty of the Machine Tool Research Center and the Institute of Advanced Materials and Manufacturing at UT. Prior to joining UT, he was a Postdoctoral Research Associate at the University of Wisconsin-Madison. Education: PhD in Management Science and Engineering, Peking University, 2017 BS in Pure and Applied Mathematics, China University of Geosciences (Beijing), 2011 His research focuses on the integration of AI, human, and complex systems through trustworthy system-driven AI methods, with applications in smart manufacturing, digital twin modeling, simulation, and engineering innovation. He emphasizes the development of AI-enabled simulation tools and digital frameworks for dynamic systems, leveraging scientific machine learning and mathematical optimization. His work bridges theoretical AI advancements with practical engineering solutions. While specific publications are not listed in the text, his research trends indicate a strong focus on AI for scientific discovery, manufacturing systems, and dynamic system control. His interdisciplinary approach combines industrial engineering principles with cutting-edge AI methodologies. Scientific Awards: No awards listed in the provided text. He teaches courses such as Artificial Intelligence for Complex Systems, AI-enabled Manufacturing Systems, Advanced Design of Experiments, and Statistical Methods in Industrial Engineering. He advises research through the System-driven AI Lab (SAIL) and collaborates with interdisciplinary centers to advance materials and manufacturing innovation. His work is supported by research in modeling, simulation, and optimization of dynamic systems. Labs and Affiliations: Director, System-driven AI Lab (SAIL) Affiliated Faculty, Machine Tool Research Center, UT Affiliated Faculty, Institute of Advanced Materials and Manufacturing, UT
Serveh Kamrava is an Assistant Professor at the Colorado School of Mines, Department of Chemical and Petroleum Engineering. Her research bridges Chemical, Environmental, and Petroleum Engineering with advanced computational methods, focusing on energy storage, fluid dynamics, and machine learning applications in complex systems. Research Interests: Her work centers on developing physics-guided machine learning models to predict flow and transport in heterogeneous and porous materials. She aims to improve energy storage systems by enhancing efficiency and longevity through data-driven and multiscale modeling techniques. Key areas include deep learning for material reconstruction, fluid flow simulation, and dispersion prediction in geophysical and environmental contexts. Publication Trends: Her recent publications (2019–2021) demonstrate a strong focus on integrating deep learning with physical laws in porous media, particularly in geological and membrane systems. The work spans journals in geophysics, computational materials, environmental science, and chemical engineering, showing interdisciplinary impact. Scientific Awards: 2022 NSF Award 2022 ACS-PRF DNI Award Advising and Grants: Dr. Kamrava is actively recruiting PhD students, indicating ongoing research funding and mentorship activities. She likely holds active grants, including the NSF award, supporting her work in machine learning applications for energy and environmental systems. She advises prospective students with backgrounds in machine learning and computational fluid dynamics. Labs and Teams: She leads a research group focused on the interface of engineering and data science, utilizing state-of-the-art machine learning to solve multiscale problems in energy and environmental engineering. The group emphasizes Python-based computational modeling and interdisciplinary collaboration.
Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.
Prof. Christine Alewell is a Full Professor in Environmental Geosciences at the University of Basel since 2003, affiliated with the Faculty of Philosophy and Natural Sciences and the Department of Environmental Sciences. Her research focuses on soil chemistry, biogeochemical processes, and stable/radiogenic isotope applications in environmental systems. She has held visiting research positions at SUNY Syracuse (USA) and Umeå University (Sweden), supported by grants from the German Research Foundation (DFG) and DAAD. Key research areas include soil degradation, sediment source attribution, nutrient cycling in ecosystems, and the interaction between upland and wetland environments. Her work integrates field studies, isotopic analysis, and modeling to address environmental challenges such as soil erosion and climate change impacts. Awarded the prestigious 'World's 2% of Scientists' recognition (2021–2023), she has also been highlighted in Nature Communications for her global phosphorus research. Her leadership roles include chairing the Gordon Research Conference on Catchment Science (2005) and serving as vice-chair (2004). Prof. Alewell’s lab (FG Alewell) collaborates internationally on projects like the BITÖK Monitoring Program and contributions to understanding alpine and Arctic ecosystems through interdisciplinary approaches.
Prof. Dr. Benny Selle is a Professor of Hydrology and Water Protection at the Berlin University of Technology , affiliated with the Department of Civil Engineering and Geoinformation . His research focuses on hydrological system variables , process models , and causal interactions in water quality and management. Education: Diploma in Geography (University of Leipzig, 2001) with minors in Geology and Economics. Doctorate in Soil Physics (University of Bayreuth, 2005). Habilitation in Mathematics and Natural Sciences (University of Tübingen, 2014). Re-habilitation in Agriculture, Civil Engineering and Environment (University of Rostock, 2024). Research Interests include dissolved organic carbon mobilization , contamination processes in water bodies , and innovative water management solutions . His work often integrates bottom-up/top-down modeling approaches to address ecological and social relevance of hydrological relationships. Committee Activities: Active member of the Catchment Hydrology committee (European Geoscience Union, since 2008). Member of the Commission for the Opinion on Appointment Procedures at Berlin Tech (since 2017). Associate Editor of Hydrology and Water Management (HyWa) (since 2018). Member of the working group Deadwood in Water Management (DWA AG GB 2.20, since 2019). Member of the Academic Senate (Berlin Tech, since 2021). Teaching Philosophy emphasizes independent learning , student discussion , and supporting diverse academic potentials . He supervises theses on topics such as urban water management , cleaning urban drains , and remediation of contaminated sites . Professional Experience spans institutions like the University of Bayreuth , WESS (Tübingen) , and University of Potsdam , with a career in Australia as a Hydrologist & Systems Modeller (2005-2010).
