Find Academic Supervisors

Gregory J. CarboneView profile

Professor

• Climate Variability
• Drought Monitoring
• Climate Change Impacts
• +1 more

Gregory J. Carbone is a Professor in the Department of Geography at the University of South Carolina. His research investigates climate variability and change impacts on water resources and agriculture, with emphasis on drought monitoring systems and climate scenario development. He earned his Ph.D. from the University of Wisconsin-Madison (1990), M.A. from University of Kansas (1984), and B.A. from Clark University (1982). His research develops tools for drought assessment in data-scarce regions and examines how spatial scale affects climate impact assessments. Research focuses on drought monitoring techniques, climate extremes, and the application of climate information in water resource management. Recent work explores uncertainty in precipitation indices, agricultural sensitivity to drought, and regional climate projections. He co-developed the Carolinas Dynamic Drought Index tool used by water managers. His publications demonstrate consistent innovation in drought monitoring methodologies, with recent advancements in spatial visualization of climate impacts and statistical downscaling techniques. Research integrates geospatial analysis, remote sensing, and statistical modeling. Teaching excellence recognized through multiple awards: Michael J. Mungo Distinguished Professor of the Year (2025) Mortar Board Excellence in Teaching Award (2012) Mungo Undergraduate Teaching Award (2005) He has supervised 6 MS students and served on 50+ thesis committees. Major grants include $3.75 million from NOAA for the Carolinas Integrated Sciences & Assessments program. Professional service includes editorial roles for Physical Geography and leadership in the American Association of Geographers.

Graeme BraggView profile

Teaching Professor

• Parallel Computing
• Event-driven Systems
• Bioinformatics
• +5 more

Dr Graeme Bragg is a Senior Teaching Fellow at the University of Southampton within the Department of Electronics and Computer Science . His work spans teaching, research, and technical development with a focus on event-driven computing, bioinformatics, and computational modeling. He actively supervises PhD students and collaborates on interdisciplinary projects. Research Interests: Parallel computing, event-driven systems, genotype imputation, Petri net simulations, subglacial hydrology modeling Teaching: Specializes in hardware description languages and computational methods for engineering students Technical Expertise: RISC-V architecture, FPGA acceleration, bespoke compute fabric development His recent publications demonstrate expertise in applying event-driven computing to diverse problems including: 2025: Automated marking systems for SystemVerilog labs 2025: Seasonal dynamics in subglacial hydrology 2023: Genotype imputation using custom hardware 2022: Optimization algorithms and graph analysis Current research explores: Custom RISC-V FPGA clusters for bioinformatics Event-triggered systems for scientific simulations Parallel computing solutions for molecular modeling Contact: gmb@ecs.soton.ac.uk | +44 23 8059 2784

Yelva RoustanView profile

Lecturer

• Air Quality Modeling
• Atmospheric Chemistry
• Pollutant Deposition
• +3 more

Yelva Roustan is a Lecturer at CEREA (joint laboratory between École Nationale des Ponts et Chaussées and EDF R&D) since 2005, specializing in air quality modeling . Her research focuses on multiscale modeling approaches, source-concentration relationships, and pollutant deposition processes. She teaches at the National School of Bridges and Roads, sharing knowledge from her research in atmospheric transport and environmental science. Her work addresses urban pollution challenges, including the impact of low-emission vehicles on air quality, urban tree effects, and pollutant dynamics in street networks. She has developed the MUNICH street-network model and contributed to projects like EURODELTA for multi-pollutant analysis across Europe. Her methods integrate data assimilation, inverse modeling, and Bayesian techniques to improve emission source reconstruction and model accuracy. Key contributions include studies on radioactive release scenarios (e.g., Fukushima, 106Ru event) and urban infrastructure impacts on pollution. Her research bridges atmospheric science with engineering solutions for sustainable urban environments. She holds a PhD in Environmental Engineering from École Nationale des Ponts et Chaussées (2005) and an HDR in Numerical Modeling of Atmospheric Pollutants (2020).

Annette EickerView profile

Professor

• Geodesy
• Satellite Gravimetry
• Gravity Field Modeling
• +4 more

Prof. Dr.-Ing. Annette Eicker is a Professor of Geodesy and Adjustment Calculations at the HafenCity University Hamburg (HCU), where she has been serving since 2016. Prior to her current position, she was an Academic Councillor at the Institute of Geodesy and Geoinformation at the University of Bonn (2014-2016), and has held visiting research positions at NASA's Jet Propulsion Laboratory in Pasadena, USA (2015) and the University of Rennes 1 in France (2014). Her research focuses on satellite gravimetry, particularly utilizing GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On) mission data to monitor terrestrial water storage, study climate-related mass changes, and develop advanced methods for gravity field recovery. Her work bridges geodesy, hydrology, and climate science, with significant contributions to understanding global water cycle dynamics and developing next-generation gravity missions like MAGIC (Mass-change And Geosciences International Constellation). Analysis of her recent publications reveals a strong emphasis on improving the accuracy and applications of satellite gravity data for hydrological monitoring, with increasing focus on next-generation missions and daily gravity field solutions. Her research spans from fundamental method development (e.g., GROOPS software toolkit) to practical applications for water resource management and climate change monitoring. Prof. Eicker's work demonstrates leadership in the field of satellite gravimetry, with numerous publications in high-impact journals addressing critical challenges in Earth observation and climate monitoring. Though specific awards aren't mentioned in the provided materials, her extensive publication record and leadership in major projects like MAGIC indicate significant recognition within the geodetic and hydrological communities. Her research has strong implications for understanding climate change impacts on water resources, with applications in drought monitoring, flood risk assessment, and sustainable water management. She maintains active collaborations with international institutions including NASA's Jet Propulsion Laboratory and has contributed to major initiatives like the GlobalCDA Project, which integrates geodetic and remote sensing data with hydrological models.

• Hydrology
• Climate Change Impact Assessment
• Drought Monitoring
• +2 more

Ehsan Modiri is a researcher at the Department of Hydrosystem Modelling , Helmholtz Centre for Environmental Research (UFZ), Germany. His work focuses on climate change impacts on hydrological systems, drought monitoring, and environmental modeling using advanced computational frameworks. Affiliation: UFZ - Helmholtz Centre for Environmental Research Department: Hydrosystem Modelling Research Themes: Climate Change, Droughts, Hydrological Forecasting, Water Resource Management Research Interests: Modiri specializes in understanding hydrological responses to climate change, particularly in drought dynamics and soil moisture variability. His work bridges observational data with sophisticated modeling frameworks to improve predictability of water balance components under warming scenarios. Scientific Contributions: Recent publications highlight his role in developing high-resolution drought simulations, evaluating hydrological model performance, and analyzing groundwater responses to global warming. He participates in large-scale European hydrological projects and collaborates on climate-hydrology integration initiatives.

Anand PadmanabhanView profile

Academic

• Advanced Cyberinfrastructure
• CyberGIS
• Geospatial Data Science
• +5 more

Anand Padmanabhan is a Research Associate Professor in the Department of Geography & Geographic Information Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the School of Earth, Society & Environment within the College of Liberal Arts & Sciences. He holds a Ph.D. in Computer Science from the University of Iowa, alongside an MS in Computer Science (University of Iowa) and a BE in Computer Engineering (University of Mumbai). His research focuses on advanced cyberinfrastructure, cyberGIS, geospatial data science, and high-performance computing. He leads the spatial algorithms and systems team at the CyberGIS Center for Advanced Digital and Spatial Studies, developing cyberGIS capabilities to leverage advanced computing for geospatial innovation. His work emphasizes scalable geocomputation, cloud-based frameworks, and reproducible research environments, with contributions to tools like CyberGIS-Compute and EasyScienceGateway. Recent publications highlight advancements in science gateway frameworks, middleware systems, and geospatial education platforms. He serves as Online MS Program Adviser and has secured NSF and EPA grants for interdisciplinary projects. His work spans transdisciplinary training programs, urban sensing analytics, and integration of social media with geospatial data.

• Hydraulic Engineering
• Fluid Mechanics
• Water Resources Management
• +3 more

Michael C. Johnson is a Professor in the Department of Civil and Environmental Engineering at Utah State University (USU), affiliated with the Utah Water Research Laboratory (UWRL). He holds a PhD in Fluid Mechanics and Hydraulics from USU (1996), with research focused on hydraulic modeling, flow meter performance, valve analysis, and dam/spillway design. His work bridges physical and numerical methods, addressing challenges in water resources engineering and infrastructure. Education: PhD, Fluid Mechanics and Hydraulics (Groundwater, Hydrology, Water Resources), Utah State University, 1996 MS, Fluid Mechanics and Hydraulics, Utah State University, 1994 BS, Civil and Environmental Engineering, Utah State University, 1992 Research Interests: Johnson specializes in hydraulic structures, physical and computational fluid dynamics, flow meter calibration, and energy dissipation systems. His work emphasizes practical applications like spillway design, low-head dam safety, and turbine testing. Notable projects include the Lower Baker Dam Spillway model and Archimedes Turbine evaluations. His research often combines laboratory experiments with CFD simulations to optimize water infrastructure performance. Recent Articles Trends: His publications (2019–2025) highlight advancements in CFD modeling for pressure recovery, flow meter accuracy under disturbed conditions, and valve performance optimization. Key themes include improving meter testing protocols, mitigating turbulence effects, and enhancing hydraulic infrastructure resilience. Awards and Recognition: Outstanding Graduate Mentor, 2020 Multiple AWWA Best Paper Awards (2007, 2010, 2012) Outstanding Teacher Awards (2002–2003) Advising & Labs: Johnson has mentored over 25 graduate students, guiding research in areas like valve performance, spillway hydraulics, and meter calibration. He leads the UWRL’s hydraulics team, overseeing projects such as the Orville Dam Spillway model and large-scale physical modeling initiatives. His lab facilities support both experimental and computational approaches to water engineering challenges.

Tobias NeckelView profile

Associate Professor

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +4 more

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

• Geomorphology
• Geomorphometry
• Geoinformatics
• +6 more

Leszek Ludwik Gawrysiak is a Professor at the Department of Geology, Soil Science, and Geoinformatics , affiliated with the Faculty of Earth Sciences and Spatial Management at Maria Curie-Skłodowska University in Lublin, Poland. His academic career includes habilitation in Earth and Environmental Sciences (2019), a PhD in Geomorphology (2003), and a master’s in Physical Geography (1993), all from UMCS. Education: Master’s in Physical Geography (1993), UMCS PhD in Geomorphology (2003), UMCS Habilitation in Earth and Environmental Sciences (2019), UMCS His research focuses on Geomorphology , Geomorphometry , and Geoinformatics , with applications in Loess Erosion , Gully Dynamics , and Environmental Risk Assessment . He employs GIS, LiDAR, and remote sensing techniques to analyze terrain segmentation, forest cover changes, and geomorphological impacts on land use planning. Projects include studies on extreme meteorological events, geopark development, and Polish-Belarusian-Ukrainian water policy in the Bug River basin. Recent publications emphasize Loess Landscapes , Proglacial Valley Morphology , and Gully Erosion , with methodological advancements in Digital Terrain Modeling and Geomorphon Analysis . His work bridges computational methods (e.g., GPU-accelerated topographic parameter evaluation) with field-based geomorphological studies in Svalbard and Eastern Europe. Scientific Awards: Award from the Polish Geomorphological Society for best PhD thesis (2002) Team Award from the Rector of UMCS (2010) Individual Award from the Rector of UMCS (2013) He has contributed to understanding Loess Area Geomorphodiversity , Forest Cover Dynamics , and Climate Change Impacts on landscapes. His collaborations span interdisciplinary studies in gully erosion, geotourism, and Holocene soil erosion rates. Contact: leszek.gawrysiak@mail.umcs.pl | Phone: +48 81 537 68 71

Ryan M. PollyeaView profile

Associate Professor

• Geofluids
• Energy Resources
• Geologic CO2 Sequestration
• +4 more

Ryan M. Pollyea is an Associate Professor in the Department of Geosciences at Virginia Tech’s College of Science. His research focuses on geofluids, energy resources, and geologic CO2 sequestration, with expertise in numerical modeling of fluid flow in porous media and geospatial analysis. He leads the VT Hydrogeosciences Lab, investigating processes like CO2 sequestration in basalt reservoirs and fluid-induced seismicity linked to wastewater injection. Education: Ph.D., University of Idaho (2012); B.S., University of Dayton (1999). His team includes PhD student Wu Hao and MS student Gradyon Konzen, studying topics like capillary pressure uncertainty and hydraulic effects of wastewater injection. Research interests span coupled thermal-hydro-chemical-mechanical modeling, fracture network characterization via LiDAR, and reservoir-scale permeability changes. He emphasizes applying computational methods to address energy and environmental challenges, with a focus on carbon storage and induced seismicity mitigation. Teaching includes courses like Groundwater Hydrology, Reactive Transport Modeling, and Engineering Geology. His work integrates high-performance computing and machine learning to analyze large datasets, exemplified by collaborations on exascale simulation frameworks and semantic interaction tools for ensemble analysis. Current projects include the Virginia SWIFT initiative, assessing aquifer recharge risks, and evaluating carbon storage potential in basaltic formations. His lab’s research underscores the interplay between subsurface fluid dynamics and geological processes, with applications to sustainable energy and environmental management.

Fraser KingView profile

Assistant Professor

• Climate and Climate Change
• Radiation and Remote Sensing
• Synoptic Meteorology
• +13 more

Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.

Erik LinsteadView profile

Associate Professor

• Machine Learning
• GPU Programming
• Autism Spectrum Disorder
• +5 more

Dr. Erik Linstead is an Associate Professor and Senior Associate Dean at Chapman University, affiliated with the Fowler School of Engineering, School of Pharmacy, and George L. Argyros College of Business and Economics. His expertise spans Machine Learning, GPU Programming, Autism Spectrum Disorder, Assistive Technologies, Predictive Analytics, and Virtual Reality. Education: Bachelor of Science, Chapman University Master of Science, Stanford University Ph.D., University of California, Irvine Dr. Linstead's research integrates machine learning with diverse domains, including autism treatment, environmental monitoring, and software engineering. His recent publications focus on coral reef health, land surface temperature trends, and embedded machine learning systems. His scholarly work includes collaborations in remote sensing, medical informatics, and neurodiversity support. Articles highlight his interdisciplinary approach, applying AI to ecological challenges (e.g., Red Sea coral reefs, Nile Basin droughts) and human-centered technologies (e.g., VR therapy for autism, medication adherence analysis).

Igor JankovicView profile

Associate Professor

• Groundwater Hydrology
• Environmental Engineering
• Contaminant Transport
• +2 more

Igor Jankovic is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on groundwater flow and contaminant transport in heterogeneous aquifers, with particular emphasis on the impact of aquifer heterogeneity on solute movement and transport modeling. Education: PhD in Civil Engineering, University of Minnesota (1997) MS in Civil Engineering, University of Minnesota (1993) BS in Civil Engineering, University of Split, Croatia (1990) His work addresses critical issues in groundwater hydrology including: Advective transport mechanisms in heterogeneous media Breakthrough curve prediction and analysis Effective hydraulic conductivity modeling Upscaling of flow and transport parameters Application of the Analytic Element Method (AEM) for complex aquifer simulations Comparison of transport models (CTRW, MRMT) in heterogeneous environments Research trends in his publications reveal a focus on: Three-dimensional heterogeneous aquifer modeling Non-Fickian and anomalous transport behavior Impact of spatial variability on contaminant migration Development of numerical algorithms for large-scale groundwater simulations Validation of stochastic transport theories against field experiments (e.g., MADE and Borden aquifers) Interaction between physical and chemical heterogeneity in reactive transport

• Forest Inventory and Monitoring
• LiDAR Technology Applications
• Forest Regeneration Quantification
• +3 more

Tobias Ofner-Graff is a researcher at the Institute of Forest Growth within the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). Based at Peter-Jordan-Straße 82, 1190 Wien, his work focuses on advanced forest monitoring technologies. His research interests include: LiDAR and remote sensing applications in forestry Automated forest inventory systems Forest regeneration quantification Airborne Laser Scanning (ALS) data analysis Sustainable forest harvesting planning Recent project contributions include: Leading lidar-based forest monitoring systems development Developing spatial forest growth models Implementing digital inventory workflows His publications demonstrate expertise in: Quantifying forest resources through 3D point clouds Advanced timber stack measurement techniques ALS data integration for forest modeling Mobile laser scanning applications Forest climate adaptation strategies

Kobus BarnardView profile

Professor

• Computer Vision
• Machine Learning
• Scientific Applications
• +3 more

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.