جستجوی استاد راهنمای دانشگاهی

Hefin Williamsمشاهده پروفایل

پژوهشگر

• Parasitology
• Environmental DNA
• Sustainable Livestock Farming
• +۹ مورد دیگر

Hefin Williams is a researcher in the Department of Life Sciences at Aberystwyth University , with a focus on parasitology, environmental DNA (eDNA), and sustainable livestock systems. His work contributes to UN Sustainable Development Goals through innovations in veterinary science and agricultural sustainability. He has served as Principal Investigator on externally funded projects related to fluke control and digital agriculture and is actively engaged in interdisciplinary research with collaborators across the UK and internationally. Research Interests: Dr. Williams specializes in the detection and management of parasitic infections in ruminants, particularly Fasciola hepatica and Calicophoron daubneyi , using molecular tools such as eDNA and PCR-based assays. His research extends into digital agriculture, where he explores AI and sensor technologies for animal health monitoring. He also investigates the ecological and genomic aspects of plant relatives like Brachypodium distachyon . The recent publications highlight a strong trend in environmental DNA applications for parasite surveillance, statistical modeling of disease spread , and precision livestock farming . His work bridges molecular biology, veterinary epidemiology, and sustainable agricultural policy. Scientific Awards: Queen’s Anniversary Prize 2023 Grants and Advising: Dr. Williams has led multiple externally funded research projects, including those from Riverlane (UK) and Connect LNG (Norway). He has supervised PhD students and early-career researchers, as evidenced by co-authored works and collaborative outputs. His role in the BBSRC-funded Precision solutions for controlling fasciolosis in sheep project underscores his leadership in grant acquisition and team coordination. Labs and Teams: He is part of a multidisciplinary team at Aberystwyth University involving parasitologists, molecular biologists, and agricultural scientists. His collaborations with researchers like R. A. Jones and P. M. Brophy reflect strong institutional and cross-institutional networks focused on livestock health and environmental sustainability.

• Geographic Information Science
• GIS
• Watershed Modeling
• +۵ مورد دیگر

Steve Graham is a Researcher at the University of Minnesota's Research Computing U-Spatial unit. He holds a PhD in Civil Engineering from The University of Texas at Austin and is a certified GISP (Geographic Information Systems Professional). His work primarily focuses on environmental and climate-related research, with contributions to hydrology, watershed modeling, and geographic information science. PhD in Civil Engineering from The University of Texas at Austin GISP certification from GIS Certification Institute since 2009 Graham's research spans climate change impacts, flood modeling, and GIS applications. He has been involved in projects related to wind resource development, soil moisture variability analysis, and hazard mitigation planning. His work contributes to UN Sustainable Development Goals, particularly in education and environmental sustainability. Recent projects include developing machine learning models for crop evaluation, creating transparency tools for lead service line data, and conducting floodplain area analysis using environmental GIS data. He has collaborated on grants from agencies like USDA, US Forest Service, and Minnesota Department of Health.

• Hydrology
• Remote Sensing
• Artificial Intelligence
• +۳ مورد دیگر

Mathew Herrnegger is a Senior Scientist at the Institute of Hydrology and Water Resources Management (HyWa) at the University of Natural Resources and Life Sciences, Vienna (BOKU). He completed his doctorate in hydrology after studying Environmental Engineering and Water Resources Management. His research focuses on hydrological modeling, integration of remote sensing data, artificial intelligence, and machine learning in water management applications, climate change impacts on water resources, and big data analytics. Primary research areas: Hydrology, Water Resources Management, Climate Change, Artificial Intelligence, Remote Sensing, Machine Learning Dr. Herrnegger leads research groups since 2013 and has published extensively on hydrology and water management, particularly in alpine and East African contexts. His work includes flood risk assessment, groundwater recharge modeling, and soil erosion studies using advanced statistical and machine learning techniques. He actively participates in international conferences and collaborates with institutions in Kenya, Uganda, and Austria. His recent article analyses span from 2023 to 2025, covering glacier melt contributions to runoff under climate change, water quality degradation mapping, and extreme flood estimation using synthetic weather data. Key subtopics include snow-hydrology coupling, transboundary river basin management, and AI applications in stream temperature prediction. Dr. Herrnegger contributes to university teaching and supervises student theses while engaging in knowledge transfer through GIS training workshops in Uganda and Kenya. He maintains active roles in international hydrological conferences and local Austrian climate change initiatives.

• Hydrology
• Groundwater Recharge
• Water Resource Management
• +۴ مورد دیگر

Dipl.-Ing. Hanna Zeitfogel is a researcher at the Institute of Hydrology and Water Management, part of the Department of Landscape, Water and Infrastructure at the University of Natural Resources and Life Sciences, Vienna (BOKU). Her work focuses on hydrological systems, groundwater recharge, and soil-water interactions in the Austrian context. Her primary research interests include hydrology, groundwater recharge assessment, water resource management, soil physics, and the application of machine learning techniques to environmental data analysis. Her work demonstrates a strong emphasis on regional water assessment and hydrological modeling at various spatial scales. Her publication record shows a consistent focus on Austrian water systems, with particular attention to groundwater recharge modeling, water balance components, and soil hydraulic properties. Her recent work increasingly incorporates machine learning approaches for predicting soil properties and understanding regional hydrological patterns. She has been actively involved in significant research projects including 'Austrian wide infiltration capacities' (2020-2021) and 'Variability of Groundwater Recharge and its Implication for Sustainable Land Use in Austria' (2019-2023), collaborating with Professor Karsten Schulz and other researchers. Dr. Zeitfogel regularly presents her research at major international conferences, particularly the European Geosciences Union General Assembly, demonstrating her active participation in the global hydrology research community.

Apostolos Voulgarakisمشاهده پروفایل

استاد

• Climate change
• Atmospheric environment
• Wildfires and climate interactions
• +۳ مورد دیگر

Apostolos Voulgarakis is a Professor at the Technical University of Crete, specializing in Climate Change and the Atmospheric Environment. He holds the AXA Chair on Fires and Climate and leads the Department of Environmental and Energy Management, Sustainable Development and Climate Change. Education: 2008: PhD in Atmospheric Science, Department of Chemistry, University of Cambridge 2004: Postgraduate Diploma, School of Environmental Engineering, Technical University of Crete 2002: Degree in Physics, School of Sciences, Department of Physics, Aristotle University of Thessaloniki Research Interests: His work focuses on climate change and its impacts on the atmospheric environment , particularly the thermal and hydrological impacts of air pollution . He investigates the relationship between fires and climate , conducts climate and Earth system simulations , and explores the correlation between climate, air pollution, and public health . His expertise also extends to global and regional environmental policy . Grants and Projects: He coordinates several major projects, including the AXA Chair on Fires and Climate (2022-2026), FirePC (2022-2024), and is the founding director of the Leverhulme Centre for Wildfires, Environment and Society (2019-2029). Scientific Awards: Best lecturer at Imperial College London (2016) Best Young Scientist award from the Hellenic Meteorological Society (2008) Student Advising: He has supervised students including Anastasios Rovithakis, Dimitra Tarasi, and Rafaela Mourgela.

Andrew McDonaldمشاهده پروفایل

دانشیار

• Cropping Systems Ecology
• Climate Change Adaptation
• Agricultural Sustainability
• +۴ مورد دیگر

Andrew McDonald is an Associate Professor at Cornell University, holding dual appointments in the School of Integrative Plant Science (Soil and Crop Sciences Section) and the Department of Global Development. His work focuses on cropping systems ecology, climate change adaptation, and agricultural sustainability with a strong emphasis on South Asia. He previously led CIMMYT's sustainable intensification program and currently develops decision frameworks for technology scaling in agri-food systems. He earned his B.Sc., M.Sc., and Ph.D. from Cornell University between 1994 and 2003. His research integrates agronomy, hydrology, and social sciences to address food security challenges, particularly in water resources management and policy. Recent work includes applying remote sensing for crop yield estimation and machine learning for precision agriculture in resource-constrained environments. Key research themes include climate-smart agriculture practices, residue management, and irrigation strategies. His interdisciplinary approach addresses both biophysical and socio-economic dimensions of agricultural systems. He has contributed to frameworks like the PAiCE tool for climate adaptation and the LCAS-APT protocol for prioritizing agronomic interventions. His lab's work has been supported by grants such as the Cornell Atkinson Academic Venture Fund (2024). Education history includes a Doctorate (2003), Master of Science (1998), and Bachelor of Science (1994) all from Cornell University. He advises on courses like PLSCI 4140 (Global Cropping Systems) and PLSCI 6017 (Cropping Systems Ecology). His lab's website highlights ongoing projects in systems agronomy and global development contexts.

Henry Addisonمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Climate Science
• Climate Modeling
• +۳ مورد دیگر

Henry Addison is a Senior Research Associate at the School of Geographical Sciences, University of Bristol. His research focuses on advancing climate science through machine learning techniques, particularly in the emulation of high-resolution regional climate models. He holds a BA, MMath, and MSc, and his work addresses critical challenges in climate prediction, extreme weather analysis, and computational efficiency of climate simulations. His research interests include developing machine learning frameworks to enhance the scalability and accuracy of climate models, with applications in understanding local climate change impacts, precipitation patterns, and environmental risk assessment. His recent studies emphasize diffusion models and emulation techniques for improving the resolution and accessibility of climate data. Publications highlight trends in machine learning-driven climate emulation, downscaling methodologies, and predictive modeling of extreme weather events. While no explicit awards or grants are mentioned, his work aligns with cutting-edge climate science and computational innovation.

• Weed Science
• Precision Agriculture
• Soil Salinity
• +۴ مورد دیگر

Roberta Masin serves as an Associate Professor within the Department of Agri-Food, Environmental and Animal Sciences (DAFNAE) at the University of Padua, Italy, specializing in AGR/02 (Agronomy and Field Crop Production). Based at the Agripolis campus in Legnaro, Padova, she conducts research at the intersection of agricultural systems and environmental sustainability, with significant contributions to Mediterranean agricultural challenges. Her research program focuses on weed ecology under climate stressors, particularly soil salinization and drought impacts on coastal agriculture. She pioneers precision agriculture techniques using UAVs and remote sensing for weed mapping, seed germination modeling, and site-specific management. Key innovations include nature-based solutions for salinity mitigation, herbicide reduction strategies, and assessment of invasive species like Ambrosia artemisiifolia across European ecosystems. Her work integrates field experiments with advanced data analytics to develop sustainable crop protection frameworks. Analysis of her 2023-2025 publications reveals a dominant research trajectory in climate-resilient agriculture, with 60% of recent work addressing seawater intrusion impacts and weed adaptability in coastal zones. She demonstrates strong European collaboration networks, particularly with Croatian institutions, focusing on translating hydrothermal emergence models into practical precision farming tools. Her publications consistently emphasize actionable strategies for reducing chemical inputs while maintaining productivity in saline-stressed environments.

Mathieu Morlighemمشاهده پروفایل

استاد

• Polar Climate Change
• Ice Sheet Dynamics
• Sea Level Rise
• +۷ مورد دیگر

Mathieu Morlighem is a Professor in the Department of Earth Sciences at Dartmouth College, holding the Evans Family Distinguished Professorship. His research focuses on ice sheet physics, climate system interactions, and sea level rise projections. Expertise in polar climate change Specialization in ice sheet numerical modeling Leadership in data assimilation techniques Development of Physics-Informed Neural Networks for ice dynamics His work combines high-resolution numerical models with remote sensing data to understand glacier behavior in Greenland and Antarctica. Recent publications emphasize subglacial topography estimation, calving parameterizations, and ice-hydrology coupling mechanisms. Current projects involve the Ice-sheet and Sea-level System Model (ISSM) and collaborations with institutions across Europe and the US. He teaches courses in Earth system modeling and mathematical geosciences.

Jungang Chenمشاهده پروفایل

پژوهشگر ارشد

• Energy Research
• Geological Research
• Environmental Geology
• +۲ مورد دیگر

Dr. Jungang 'Gordon' Chen is a Postdoctoral Fellow at the Bureau of Economic Geology within The University of Texas at Austin. His primary research interests span energy systems, geological carbon sequestration, reservoir characterization, and environmental geology. Email: jungang.chen@beg.utexas.edu Research focuses: CO2 storage optimization, unconventional resource recovery, induced seismicity, and coastal environmental dynamics Technical expertise: Deep learning applications for subsurface modeling, enhanced oil recovery, and seismic hazard assessment Recent publications demonstrate strong contributions to: Carbon capture utilization and storage (CCUS) through innovative injection techniques Understanding fluid-rock interactions in mobile shale systems Coastal geomorphology and groundwater resource management Geophysical modeling of sediment transport and reservoir quality

Gabriel Pasquetمشاهده پروفایل

پژوهشگر ارشد

• Carbon Sequestration
• Enhanced Oil Recovery
• Seismicity
• +۳ مورد دیگر

Gabriel Pasquet is a Postdoctoral Fellow at the Bureau of Economic Geology, University of Texas at Austin. His work focuses on carbon sequestration, enhanced oil recovery, and seismicity, with a strong emphasis on Permian Basin studies and the energy-water nexus. Current affiliation: Bureau of Economic Geology, University of Texas at Austin Position: Research Fellow Pasquet's research spans multiple disciplines, including: Carbon storage security and CO2 injection methodologies Seismic risk assessment from fluid injection activities Hydrological modeling integrating satellite data and machine learning Basin tectonics and stress field characterization Permafrost dynamics in Arctic environments Coastal system modifications and sediment transport Recent publications highlight his expertise in: Geological CO2 sequestration optimization Seismotectonic analysis of Midland Basin Formate-based oil recovery techniques Environmental impacts of energy development His work frequently intersects with: Energy economics Geophysical monitoring Reservoir characterization Machine learning applications in hydrology

Juliane Maiمشاهده پروفایل

دانشگاهی

• Computational Hydrology
• Hydrological Modeling
• Sensitivity Analysis
• +۴ مورد دیگر

Juliane Mai is a Research Associate Professor at the University of Waterloo's Department of Earth and Environmental Science and a former Research Scientist at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig. Her work focuses on computational hydrology, model calibration, sensitivity analysis using high-performance computing, and data dissemination tools like HydroHub and CaSPAr. She develops blended hydrological models that combine multiple process implementations and leads large-scale intercomparison projects (GRIP-E, GRIP-GL) across North America's 3,000+ basins. Her research bridges environmental modeling with machine learning and data science, emphasizing open-access platforms for stakeholders. Key projects: GRIP-E/GRIP-GL model intercomparisons Methodological innovations: xSSA, EEE, MVA sensitivity techniques Created data portals: HydroHub for visualization, CaSPAr for climate data Her 2023 Journal of Hydrology paper outlines calibration strategies for environmental models, while 2022 Nature Communications work quantifies process sensitivities across North America. She received the 2023 ASCE-EWRI best case study award for GRIP-E research and serves on editorial boards like Journal of Hydrology .

Ries Plescherمشاهده پروفایل

پژوهشگر

• Geohazards
• Environmental Geology
• Machine Learning Applications in Earth Sciences
• +۳ مورد دیگر

Ries Plescher is a researcher in the Department of Earth Sciences at the University of Oregon, College of Arts and Sciences. His work focuses on geohazard assessment, environmental geology, and machine learning applications for natural disaster prediction. Institution: University of Oregon Department: Earth Sciences Research Focus: Landslide dynamics, rainfall modeling, structural geology His research integrates machine learning with hydrological modeling to improve landslide forecasting systems , particularly in steep mountainous regions. Recent publications examine air cavity ventilation effects on flood structures, rock mass characteristics in Puerto Rico, and downdrag impacts on foundation piles. Current work emphasizes extreme event analysis , combining storm characteristics with landslide inventories to quantify surface process dynamics. Key collaborations involve interdisciplinary teams in geoscience and engineering. Email: ripl@uoregon.edu

Hamed Moftakhariمشاهده پروفایل

دانشیار

• Coastal Hydrology
• Hydrologic Modeling
• Compound Flooding
• +۱۲ مورد دیگر

Dr. Hamed Moftakhari is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at The University of Alabama, where he contributes to the College of Engineering and conducts interdisciplinary research through the Center for Complex Hydrosystems Research and the Center for Sustainable Infrastructure. His research focuses on coastal hydrology, particularly the dynamics of estuarine systems, compound flooding, nuisance flooding, and the impacts of sea level rise on low-lying coastal communities. He investigates how interactions between inland hydrologic and coastal oceanic processes affect water, food, and energy resources, and how communities respond to these challenges. His work integrates artificial intelligence, geospatial analysis, data analytics, and climate modeling to improve flood resilience and risk assessment. The recent publications highlight a strong trend in developing advanced computational models for coastal hazard estimation, with emphasis on Bayesian methods, deep learning, data assimilation, and remote sensing. These works span applications across the U.S. Gulf Coast and focus on both extreme events and chronic flooding, reflecting a dual focus on acute disasters and long-term climate adaptation. NSF CAREER Award (2023) Early-Career Research Fellowship, National Academies (2020–2022) Cronin Award – Early Achievement, Coastal and Estuarine Research Federation (2021) Early Career Research Excellence and Innovation Award, College of Engineering, UA (2023) Faculty Excellence in Graduate Education Award, CCHE Department (2023) Early-Career Faculty Fellowship, Alabama Water Institute (2022–2025) Maseeh Graduate Fellowship (2012) Dr. Moftakhari advises graduate students and leads the Coastal Hydrology Lab (CHL), where he mentors researchers in hydrodynamic modeling and coastal resilience. His research is supported by grants from the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA), including the prestigious NSF CAREER award. He also serves as an Associate Editor for the Journal of Hydrologic Engineering (ASCE) and on the editorial board of Scientific Reports (Nature). The Coastal Hydrology Lab (CHL) at UA focuses on improving scientific understanding of freshwater-influenced coastal systems such as estuaries, wetlands, deltas, and harbors. The lab utilizes emerging technologies and diverse data sources to study sediment supply, salinity intrusion, ecosystem responses, morphologic evolution under sea level rise, and compounding hydroclimate extremes like hurricanes.

Sebastian Otteمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Recurrent Neural Networks
• +۱۲ مورد دیگر

Sebastian Otte is a Professor at the Institute for Robotics and Cognitive Systems at the University of Lübeck, where he leads the Adaptive AI research group. Prior to this, he was a postdoctoral researcher and substitute professor at the University of Tübingen, contributing significantly to the Cognitive Modeling and Distributed Intelligence groups. University of Lübeck, Professor (since 2023) University of Tübingen, Postdoc and Substitute Professor (2016–2023) Centrum Wiskunde & Informatica (CWI), Humboldt Fellow (2022–2023) His research focuses on recurrent and spiking neural networks, bio-inspired computing, efficient learning, and adaptive AI systems. He explores how neural models can perform online learning, handle multiple time scales, and solve complex cognitive tasks such as binding, prediction, and motor control. His work bridges machine learning with cognitive science and robotics. The recent publications show a strong trend toward physics-informed neural networks, finite volume methods for PDE modeling, and explainable AI via counterfactual reasoning. His work integrates deep learning with scientific computing, emphasizing robust, interpretable, and efficient models for real-world applications. Scientific awards include: Best Paper Award at ICANN 2019 Humboldt Research Fellowship Editor's Highlight in Water Resources Research He has supervised over 70 bachelor’s and master’s theses and actively mentors students in areas such as spiking neural networks, reservoir computing, and robotics. His teaching includes core computer science and advanced neural network courses. He has been involved in research projects with industry partners like Daimler AG and Mercedes-Benz AG. Otte leads the Adaptive AI research group, which focuses on developing next-generation AI systems that learn efficiently, adapt dynamically, and model complex cognitive and physical processes using biologically inspired architectures.