Find Academic Supervisors

• Uncertainty Quantification
• Bayesian Inference
• Scientific Machine Learning
• +7 more

Nathan Urban serves as Group Leader and Computational Scientist for the Applied Mathematics group at Brookhaven National Laboratory's Computational Science Initiative (CDS), a position held since 2020. Concurrently, he holds adjunct and guest appointments at Stony Brook University's Institute for Advanced Computational Science and Los Alamos National Laboratory's Statistical Sciences group. His interdisciplinary career bridges computational mathematics, statistics, and environmental science with emphasis on decision support under uncertainty. His educational foundation includes multiple degrees from Virginia Tech (1997): B.S. in Physics, Computer Science, and Mathematics, followed by advanced degrees from Penn State University (2006): Ph.D. and M.Ed. in Physics. Ph.D. in Physics, Penn State University (2006) M.Ed. in Physics, Penn State University (2006) B.S. in Physics, Virginia Tech (1997) B.S. in Computer Science, Virginia Tech (1997) B.S. in Mathematics, Virginia Tech (1997) Urban's research centers on uncertainty quantification and Bayesian inference methodologies applied to complex scientific systems. His work develops frameworks for probabilistic prediction, multi-model uncertainty analysis, reduced order modeling, and scientific machine learning. Key application domains include climate science, ice sheet dynamics, and molecular design, where he addresses challenges in decision making under uncertainty and optimal experimental design. His expertise spans computational statistics, hybrid physical-data driven modeling, and in-situ data analysis for large-scale simulations. Recent publications (2021-2024) reveal a pronounced shift toward integrating machine learning with physical modeling, particularly in climate and environmental systems. His work demonstrates innovative applications of active subspaces, Gaussian processes, and deep generative models to quantify epistemic uncertainties in ice sheet projections, molecular design, and extreme weather analysis. A consistent theme is developing scalable methods for uncertainty quantification in high-dimensional systems. Urban received significant recognition through the DOE Office of Science Early Career Research award (2013) in Biological and Environmental Research, supporting his foundational work in climate model uncertainty. Early Career Research award, DOE Office of Science (Biological and Environmental Research), 2013 As Applied Mathematics group leader at Brookhaven, Urban directs research in computational statistics and mathematical modeling for scientific applications. His team develops advanced methods for uncertainty quantification and decision support across multiple DOE mission areas, with strong collaborations in climate science and materials research.

Alison HoytView profile

Assistant Professor

• Biogeochemistry
• Carbon Cycle
• Climate Change Impacts
• +5 more

Alison Hoyt serves as Assistant Professor of Earth System Science at Stanford University's School of Earth, Energy & Environmental Sciences, with a courtesy Center Fellowship at the Woods Institute for the Environment and membership in Bio-X. Her research program investigates biogeochemical cycle responses to human impacts, specializing in vulnerable carbon stocks within tropical and Arctic ecosystems. Dr. Hoyt's primary research focuses on soil carbon dynamics across global ecosystems, particularly examining how mineral-organic interactions control carbon persistence in tropical soils of Africa and Southeast Asia, and methane emissions from Arctic wetlands. Her work integrates field measurements with modeling approaches to understand carbon cycling in degraded peatlands, drainage canal networks, and climate-sensitive regions. She maintains an active research group focused on carbon cycle science, as evidenced by her group website carboncycle.stanford.edu . Analysis of her 15 most recent publications reveals consistent research themes across biomes: soil carbon stabilization mechanisms (72% of works), tropical peatland hydrology and carbon emissions (40%), Arctic wetland methane dynamics (13%), and global soil carbon data synthesis (20%). Her methodological approach combines radiocarbon dating, ramped thermal analysis, satellite remote sensing, and field measurements across diverse pedoclimatic regions. Dr. Hoyt actively mentors graduate students across multiple programs, serving as Doctoral Dissertation Reader, Co-Advisor, and Master's Program Advisor for nine students. Her teaching portfolio includes courses on ecosystem restoration, soil management for climate mitigation, climate change perspectives, and independent research supervision across Earth Systems Science and Environment & Resources programs. She contributes significantly to community data infrastructure through leadership in the International Soil Radiocarbon Database (ISRaD) and collaborative synthesis efforts addressing soil carbon data integration challenges. Her research directly informs climate mitigation strategies through understanding soil carbon vulnerability and peatland management impacts.

Tony BostonView profile

Senior Lecturer

• Geographic information systems
• Remote sensing
• Machine learning applied to environmental information
• +5 more

Tony Boston is an Honorary Senior Lecturer at the Fenner School of Environment & Society at the Australian National University. With extensive expertise in geographic information systems, remote sensing, and machine learning applications for environmental data, he contributes significantly to earth science research and environmental monitoring initiatives across Australia. His educational background includes: PhD from the Australian National University (awarded November 8, 2024) Graduate Diploma in Computing Studies from the University of Canberra (awarded April 22, 1993) Bachelor of Science (Honors) from the Australian National University (awarded May 9, 1984) Tony's research focuses on the application of earth observations, in situ data, and AI foundation models for environmental monitoring and prediction. His work particularly emphasizes water resources management, land cover mapping, and environmental change detection through advanced machine learning techniques. He has developed expertise in integrating diverse data sources to create comprehensive environmental information systems that support evidence-based decision making in natural resource management, with a strong emphasis on developing national and international data standards. His recent publications demonstrate a consistent focus on applying convolutional neural networks and other machine learning approaches to environmental remote sensing data, particularly Landsat imagery. These works show an evolution from basic plant identification experiments to sophisticated land cover and vegetation mapping systems with practical applications for environmental monitoring across Australia. His research bridges the gap between theoretical machine learning advancements and practical environmental management needs, with increasing emphasis on temporal stability and accuracy in land cover classification. Tony has extensive professional experience, having served over 25 years in executive roles within the Australian Public Service, including positions at the Department of the Environment, National Library of Australia, and Bureau of Meteorology. At the Bureau of Meteorology, he was responsible for developing a national database of surface and groundwater information that underpins the Water Information Program established under Part 7 of the Water Act 2007. As co-chair of the WMO/OGC Hydrology Domain Working Group from 2013 to 2024, he made significant contributions to the development of WaterML2 international standards for hydrological data exchange.

• Landslide Susceptibility
• Geomorphology
• Natural Hazards
• +3 more

Chiara Martinello is a Researcher in Earth and Marine Sciences at the University of Palermo, Italy, with office hours held Tuesdays 3:00-5:00 PM at Via Archirafi, 20 - 4th floor. Her work focuses on advanced geomorphological analysis of landslide and coastal processes using statistical and machine learning methodologies. Her research spans landslide susceptibility mapping, coastal erosion vulnerability, and earthquake/tropical storm-induced geohazards. She addresses critical methodological challenges including incomplete landslide inventories, cell-size effects in modeling, and integration of preparatory/triggering factors. Fieldwork concentrates on Sicily (Italy) and El Salvador, with emphasis on practical risk assessment applications. Analysis of her 15 most recent publications reveals consistent innovation in statistical landslide modeling, particularly regarding inventory completeness validation and multi-typology susceptibility mapping. Recent work expands into coastal vulnerability indices and anthropogenic impacts on fluvial systems, demonstrating interdisciplinary integration of geospatial data, bathymetry, and hydrological modeling. No scientific awards are documented in available sources. Dr. Martinello supervises Master's theses as indicated by her 'Edited theses' role, though specific student names aren't provided. Research grant details remain undisclosed in public materials, though her extensive publication record suggests active project involvement. She operates within the Earth and Marine Sciences Department's research infrastructure at the University of Palermo, contributing to regional risk assessment initiatives through field data collection, statistical modeling, and validation frameworks for geohazard prediction.

• Marine Ecology
• Ocean Acidification
• Fisheries Management
• +3 more

Federico Quattrocchi is a Researcher at the University of Palermo 's Department of Earth and Marine Sciences. His work focuses on marine ecology, fisheries dynamics, and climate change impacts on Mediterranean ecosystems. Office hours: Tuesdays 11:00 AM–1:00 PM (Trapani or Principe di Napoli facilities) Email: federico.quattrocchi@unipa.it Research Interests Federico investigates: Species distribution modeling for deep-sea shrimp and fish Larval transport mechanisms in Mediterranean fisheries Ocean acidification effects on biogenic habitats and fish behavior Socioecological aspects of small-scale fisheries Climate change impacts on marine biodiversity Environmental drivers of chondrichthyan community dynamics Recent Article Trends His publications emphasize machine learning applications in marine resource management, long-term ocean acidification studies, and interdisciplinary approaches combining ecological and fisheries data across Mediterranean regions.

• Laser Interferometry
• Gravitational Wave Astronomy
• Space Interferometry
• +2 more

Dr. Jan-Niklas Reinhardt is a postdoctoral researcher at the Max Planck Institute for Gravitational Physics (AEI Hannover) , specializing in Laser Interferometry and Gravitational Wave Astronomy with a focus on Space Interferometry . His work combines quantum optics, precision measurement, and astrophysics to advance gravitational wave detection technologies and explore novel applications like asteroid tomography. Research Interests : Laser Interferometry, Gravitational Wave Astronomy, Space Interferometry, Quantum Optics, Precision Measurement, and Asteroid Studies. Technological Focus : Quantum noise reduction, high-finesse optical cavities, wavefront sensing, and digital phase meters for gravitational wave detectors. Recent Publications : Highlight trends in applying quantum-enhanced techniques to space missions, asteroid tomography, and thermal noise analysis in mirror coatings. Contact: janniklas.reinhardt@aei.mpg.de | Location : AEI Hannover, Callinstraße 38, 30167 Hannover, Germany.

Valerie SahakianView profile

Associate Professor

• Active Tectonics
• Ground Motion Seismology
• Marine Geophysics
• +4 more

Valerie Sahakian is an Associate Professor in the Department of Earth Sciences at the University of Oregon, affiliated with the College of Arts and Sciences. Her research focuses on active tectonics, ground motion seismology, and marine geophysics. Active Tectonics Ground Motion Seismology Marine Geophysics Recent publications analyze multi-fault earthquake rupture dynamics, Cascadia subduction zone hazards, and deep learning applications for real-time seismic monitoring. Trends in her work include tsunami earthquake ground motion modeling, submarine paleoseismology, and environmental seismology (e.g., tree-captured wind interactions). Her research also addresses technical challenges in seismic attenuation tomography, GNSS-based deformation tracking, and sedimentological records of earthquake recurrence. Fieldwork spans California fault zones (San Andreas, San Jacinto) and subduction zone analogs in the Himalaya.

Marcel AachView profile

Researcher

• Artificial Intelligence
• High-Performance Computing
• Machine Learning
• +3 more

Marcel Aach is a researcher at the Jülich Supercomputing Centre (JSC) within Research Centre Jülich, focusing on high-performance computing (HPC) and artificial intelligence (AI) integration. His work spans hyperparameter optimization, quantum-classical hybrid systems, and scalable deep learning architectures. Research Focus: AI-driven CFD simulations, medical imaging analysis, and edge AI optimization Infrastructure: Leverages JSC's HPC systems for large-scale computational tasks Recent publications highlight his contributions to resource-adaptive AI training, turbulent flow prediction using GRU models, and enhancing CT data accuracy for respiratory simulations. His work intersects HPC scalability, machine learning efficiency, and interdisciplinary applications. While no scientific awards are explicitly documented in the provided texts, his research demonstrates technical leadership in merging classical and quantum computing paradigms for AI optimization.

Kim LowellView profile

Adjunct Professor

• Machine Learning
• Deep Learning
• Geospatial Information Systems
• +6 more

Kim Lowell serves as a Research Scientist at the University of New Hampshire's Center for Coastal & Ocean Mapping (CCOM), with concurrent appointments as Adjunct Professor in Analytics and Data Science and Affiliate Research Professor in the Earth Systems Research Centre. His work centers on advancing bathymetric charting through machine learning and geospatial analytics at the Chase Ocean Engineering Lab in Durham, NH. His academic credentials include: M.Sc. in Forest Biometrics from University of Vermont Ph.D. in Forest Biometrics from Canterbury University, New Zealand M.Sc. in Data Science and Analytics from University of New Hampshire Lowell's research integrates machine learning, deep learning, and geospatial analysis to solve complex problems in ocean mapping and land management. He specializes in processing optical, radar, and lidar imagery while rigorously accounting for data uncertainties, with applications spanning bathymetric charting, hydrological modeling, and environmental monitoring systems. His technical expertise bridges computational science and environmental applications. Analysis of his 2017-2025 publications reveals consistent innovation in quantifying bathymetric uncertainty through machine learning, with recent work focusing on hydrographic survey optimization, underwater image reconstruction, and multi-temporal change detection. These contributions demonstrate interdisciplinary synergy between marine geodesy, computer vision, and spatial statistics. No scientific awards or honors are referenced in available materials. As instructor for IAM 999: Doctoral Research, Lowell supervises graduate work though specific advisees aren't listed. Grant funding details remain unspecified in the source text. He operates within UNH's Center for Coastal & Ocean Mapping (CCOM), a NOAA-affiliated research hub developing next-generation ocean mapping technologies through collaborative geospatial research.

Francesco PillaView profile

Professor

• Smart Cities
• Geospatial Analysis
• Urban Dynamics
• +11 more

Francesco Pilla is a Full Professor at University College Dublin's School of Architecture, Planning and Environmental Policy, specializing in Smart and Sustainable Cities. His work bridges urban technology, geospatial modeling, and citizen science initiatives. Coordinates EU Horizon projects: iSCAPE (air pollution control), OPERANDUM (flood risk reduction), WeCount (mobility systems), Connecting Nature (nature-based solutions) Leads SFI Investigator Grant on scalable mobility analytics Co-Director of Spatial Dynamics Lab Research focuses on: Geospatial big data & AI integration Participatory sensing with low-cost sensors Climate action through educational co-design Urban microclimate optimization Policy-driven AI consensus models Key articles demonstrate expertise in air quality monitoring, traffic safety analysis, and energy-efficient urban design. Awards include: Haagen-Smit Prize for atmospheric research Electric Vehicle Awards 2024 for eMobility innovation Academic Research Award from Financial Times Teaching responsibilities include: Advanced GIS (2018-2025) Smart Cities module (2019-2024) Research Methods coordination (2019-2024) Labs & Teams: Co-Director of Spatial Dynamics Lab, member of Science Foundation Ireland Energy Institute, and collaborator with MIT & IBM research labs.

• Artificial Intelligence
• Computer Vision
• Data Science
• +3 more

Rosario Milazzo is a PhD candidate (2022-2025) in Computer and Systems Engineering at the Department of Control and Computer Engineering (DAUIN), Polytechnic University of Turin, concurrently serving as an external lecturer and teaching assistant for Innovation Management and ICT Product Development in Management Engineering. His research centers on developing robust deep neural networks resilient to transient faults like radiation-induced errors in satellite and automotive systems. He earned Bachelor's (2020) and Master's (2022) degrees in Computer Engineering from the same institution, joining the GRAINS research group as a scholarship holder before commencing his NODES-funded PhD under advisors Lia Morra, Sophie Fosson, and Luca Sterpone. Research focuses on Artificial Intelligence with specialization in Computer Vision and fault-tolerant deep learning for critical infrastructure. His work integrates self-supervised learning, quantization, and architectural innovations to maintain accuracy/efficiency under hardware-induced faults in space and mobility applications, addressing real-world reliability challenges. Publication analysis (2020-2025) reveals a progression from agricultural photogrammetry to cutting-edge fault-tolerant AI, with recent works emphasizing medical imaging (mammography), satellite systems, and weather-event mitigation through graph/transformer architectures and self-supervised techniques. Funded by NODES (North West Digital and Sustainable), Milazzo contributes to the GRAINS research group's mission without current advisees. His scholarship position since 2022 supports collaborative development of intelligent systems for high-stakes environments. As a GRAINS (GRAphics and INtelligent Systems) member, he operates within a specialized environment advancing graphics, computer vision, and robust AI deployment, directly enabling his investigations into neural network resilience for satellite and automotive domains.

• Remote Sensing
• Machine Learning
• Geospatial Data Analysis
• +4 more

Dr. Emma Izquierdo-Verdiguier is a Researcher at the Institute of Geomatics within the University of Natural Resources and Life Sciences, Vienna (BOKU) . She specializes in machine learning algorithms for remote sensing data analysis , with particular focus on land surface phenology and cloud computing environments like Google Earth Engine. Her research spans two decades, including postdoctoral work at the University of Valencia (2017-2018) and University of Twente (2014-2017). She has developed Matlab toolboxes for nonlinear feature extraction and transitioned to JavaScript/Python-based cloud computing solutions for high-resolution image classification. Key methodologies involve kernel methods , multispectral image analysis , and data cube clustering . Recent publications focus on climate change impact assessment (2025), carbon flux modeling (2023), and soil organic carbon monitoring (2025), demonstrating her expertise in Earth Observation and machine learning integration . Her work frequently employs Google Earth Engine for continental-scale analyses. Scientific Awards: 2018 Travel Award sponsored by Remote Sensing 2017 Best Youth Oral Paper Award (X. Wu), ISPRS 2012 2nd Best Paper Award, IEEE IGARSS Student Competition

Shari Dunn-NormanView profile

Associate Professor

• Petroleum Engineering
• Hydraulic Fracturing
• Safety Engineering
• +4 more

Dr. Shari Dunn-Norman is an Associate Professor in the Department of Geological and Petroleum Engineering at Missouri University of Science and Technology (Missouri S&T). With over 40 years of experience in the petroleum engineering field, including 28 years at Missouri S&T and 8 years in industry with ARCO Oil & Gas, she specializes in hydraulic fracturing, lost circulation mitigation, and eco-friendly drilling technologies. Her work spans both theoretical and applied research, addressing critical challenges in unconventional reservoirs and sustainable drilling practices. Ph.D. in Petroleum Engineering, Heriot-Watt University (1990) B.S. in Petroleum Engineering, University of Tulsa (1978) Dr. Dunn-Norman's research focuses on: Optimizing hydraulic fracturing fluid systems and proppant transport Developing biodegradable drilling fluid additives from food waste Applying machine learning for lost circulation prediction and mitigation Geomechanical modeling for wellbore stability and fracture gradient estimation Environmental impact analysis of unconventional oil and gas operations Data-driven analytics for drilling efficiency and reservoir characterization Recent publications highlight advancements in: Eco-friendly drilling fluid formulations using peanut shells and mandarin peels Comparative analysis of gel and water fracture stimulation in shale plays Predictive models for equivalent circulation density and rate of penetration CO2 sequestration feasibility in shallow transition zones Statistical and neural network approaches for lost circulation treatment optimization She has served on key advisory committees including the EPA Hydraulic Fracturing Research Advisory Panel and SPE Star Scholarship Selection Committee.

Lars BernardView profile

Professor

• (Geo-)Information Infrastructures
• Geoinformation Usability
• Distributed Geoprocessing
• +7 more

Lars Bernard is Full Professor for Geoinformatics at the Faculty of Environmental Sciences, Technische Universität Dresden since 2015. He also holds the position of Chief Digitalization and Information Management Officer (CDIO) at TU Dresden since 2020 and serves as member of the TUD Rectorate. PhD in Geoinformatics (University of Münster, 2001) Diploma in Physical Geography (University of Münster, 1995) His research focuses on (Geo-)Information Infrastructures , Distributed Geoprocessing , and Smart Environmental Monitoring . He has extensive experience in developing geospatial standards through roles in AGILE, EuroSDR, and INSPIRE initiatives. His publications demonstrate a consistent emphasis on: Metadata provision and data quality frameworks Interoperability solutions for geospatial systems Climate information service architectures User-driven provenance mechanisms As co-chair of RFII (2020-) and spokesperson for NFDI4Earth (2019-), he leads major German research data initiatives. He has advised numerous students and coordinated TU Dresden's Master Geoinformation Technologies program until 2018. Current leadership roles include serving on scientific editorial boards for Environmental Modelling & Software and International Journal of Spatial Data Infrastructures Research .

Jing WeiView profile

Researcher

• Atmospheric Aerosols
• Particulate Matter Analysis
• Air Quality Monitoring
• +7 more

Dr. Jing Wei is an Assistant Research Scientist affiliated with the Earth System Science Interdisciplinary Center (ESSIC) and the Department of Atmospheric and Oceanic Science at the University of Maryland, College Park. She also holds appointments at NASA Goddard Space Flight Center and the Cooperative Institute for Satellite Earth System Studies (CISESS). Her research focuses on air quality monitoring through satellite remote sensing, big data analytics, and artificial intelligence applications. Dr. Wei specializes in atmospheric aerosols, particulate matter (PM) and chemical composition, and trace gases, utilizing satellite remote sensing, big data, and AI to assess the impacts of air pollution and extreme weather on the environment, health, and climate. Her work has resulted in the development of high-resolution air pollutant datasets for China (CHAP), the United States (USHAP), and globally (GHAP), which have been widely used in over 350 applied publications. Her research has produced over 100 SCI papers as (co-)first or corresponding author in leading journals including Nature Communications and The Lancet Planetary Health, with 7 ESI Hot papers (Top AGU James R. Holton Award (2022) Remote Sensing Young Investigator Award (2023) Stanford University List of the World's Top 2% Scientists since 2021 Top 0.1% highly cited authors in Atmospheric Sciences Dr. Wei serves as Editor of Earth System Science Data and Associate Editor of Journal of Geophysical Research: Atmospheres. She has secured research funding from NASA Earth Sciences and other organizations. Her work has been featured in major media outlets including CBS News, Yahoo News, US News, and The Hill.