جستجوی استاد راهنمای دانشگاهی

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.

Apostolos Giannakosمشاهده پروفایل

پژوهشگر

• Geospatial Artificial Intelligence
• Machine Learning
• Satellite Meteorology
• +۵ مورد دیگر

Dr. Apostolos Giannakos is a Researcher at the Climate and Atmosphere Research Center (CARE-C) of The Cyprus Institute, specializing in geospatial artificial intelligence for weather, climate, and environmental applications. His work involves developing machine learning algorithms and GPU-accelerated data science workflows for Earth observation analysis. He earned his PhD in Satellite Meteorology from the Department of Meteorology and Climatology, Faculty of Geology, Aristotle University of Thessaloniki, Greece. His research integrates computational techniques with environmental science through: Geospatial AI for environmental monitoring and prediction Satellite Meteorology and nowcasting systems development Remote Sensing model validation using complex EO datasets Geoinformatics infrastructure for climate data processing GPU-accelerated workflows in atmospheric and environmental modeling As a core member of CARE-C, Dr. Giannakos advances computational approaches to climate research within The Cyprus Institute's interdisciplinary framework.

Bjorn Stevensمشاهده پروفایل

استاد

• Climate Physics
• Atmospheric Science
• Cloud Physics
• +۳ مورد دیگر

Prof. Bjorn Stevens serves as the Managing Director of the Max Planck Institute for Meteorology in Germany and holds a professorship at the University of Hamburg. He leads the Climate Physics department at the Max Planck Institute, where his research focuses on understanding the role of atmospheric water vapor and clouds in shaping global and regional climate systems. His research interests include: Atmospheric water vapor and cloud physics Turbulent mixing and cloud microphysical processes Cloud organization mechanisms Precipitation, air-sea interaction, and radiative cooling effects Cloud-circulation system coupling Climate change pattern determination Stevens' work has led to the development of innovative observational techniques and expanded simulation science frontiers, particularly in applying AI to climate modeling as evidenced by his participation in sessions on Earth virtualization engines and machine learning approaches for storm-resolving Earth system models. His research bridges traditional climate physics with modern computational methods, contributing significantly to understanding cloud responses to warming and radiative forcing responses to aerosol perturbations.

Graham Rileyمشاهده پروفایل

مدرس

• High Performance Computing
• Scientific Computing
• Parallel Computing
• +۴ مورد دیگر

Graham Riley is a Lecturer in the School of Computer Science at the University of Manchester and holds a part-time position in the Scientific Computing Department (SCD) at STFC, Daresbury. His research focuses on high performance computing (HPC), software engineering for scientific computing, and performance modeling for parallel machines. Key areas include techniques for developing HPC applications, software architectures for coupled modeling, and performance control in distributed systems. His work emphasizes collaboration with computational scientists in domains such as Earth System Modelling (e.g., UK Met Office), computational chemistry, and biology. He has contributed to projects like the EuroExa architecture for exascale computing and the LFRic weather/climate model porting to FPGAs. Riley's research also explores energy efficiency in HPC systems and FPGA acceleration strategies for scientific workloads. Notable contributions include studies on parallelization strategies, FPGA-based acceleration of climate models, and optimizing OpenCL for heterogeneous architectures. His work aligns with UN Sustainable Development Goals, particularly through contributions to climate modeling and sustainable computing practices. Riley collaborates with institutions like the Met Office and the ESM community in Europe/US. His academic profile reflects a balance between theoretical research and practical application, with a strong focus on bridging computational science and engineering challenges.

Scot T. Martinمشاهده پروفایل

استاد

• Environmental Science
• Atmospheric Chemistry
• Climate Change
• +۴ مورد دیگر

Scot T. Martin is the Gordon McKay Professor of Environmental Science and Engineering and Professor of Earth and Planetary Sciences at Harvard University, with affiliations in the School of Engineering and Applied Sciences and the Department of Earth and Planetary Sciences. His research focuses on engineering solutions to global environmental challenges, particularly air/water pollution and their connections to regional/global change, with significant work in the Amazon basin. Key research interests include aerosol dynamics, climate impacts of pollution, and the interplay between human activities and natural ecosystems. His work integrates field observations, laboratory experiments, and computational modeling to address complex environmental systems. Recent studies emphasize Amazonian deforestation patterns, aerosol-cloud interactions in urban and pristine environments, and the development of novel sensing technologies for atmospheric chemistry. Notable methodologies include unmanned aerial vehicle (UAV) measurements, advanced spectroscopic techniques, and machine learning-driven models. Scientific contributions span over 15 years, with a focus on air quality, climate feedback mechanisms, and environmental policy implications. His interdisciplinary approach bridges engineering, earth sciences, and public health.

• Quantum Communications
• Quantum Sensing
• Wireless Communications
• +۷ مورد دیگر

Professor Robert Malaney is a faculty member in the School of Engineering, Department of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW), Sydney, Australia. He has held previous academic positions at the California Institute of Technology (USA), the University of California (USA), the University of Toronto (Canada), and the CSIRO (Australia). Professor Malaney specializes in quantum communications, quantum sensing, wireless communications, optical communications, satellite communications, quantum physics, and astrophysics. His research focuses on developing practical quantum communication systems with particular emphasis on satellite-based applications. He has made significant contributions to quantum key distribution (QKD), quantum networking, and the integration of quantum and classical communication systems. His work addresses critical challenges in quantum communications including atmospheric turbulence compensation, phase estimation, error mitigation, and spatial diversity exploitation. Analysis of Professor Malaney's recent publications (2023-2025) reveals an intensive focus on advancing satellite-to-earth quantum communications. His work demonstrates leadership in developing quantum routing protocols, machine learning applications for quantum systems, and hybrid quantum-classical communication techniques. These publications highlight his commitment to solving practical implementation challenges for global quantum networks, with particular attention to real-world constraints like atmospheric interference and signal degradation in space-to-ground links. Professor Malaney has maintained an extensive collaborative network across multiple institutions and research domains. His work bridges theoretical quantum information science with practical engineering applications, positioning him as a key contributor to the emerging field of quantum communications infrastructure.

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.

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 .

Yongjing Maoمشاهده پروفایل

دانشگاهی

• Remote Sensing
• Coastal Geomorphology
• Shoreline Monitoring
• +۴ مورد دیگر

Dr. Yongjing Mao serves as an Associate Lecturer at the Water Research Laboratory within the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). His academic journey includes a PhD in Remote Sensing and Coastal Geomorphology from The University of Queensland (2022), an MSc in Coastal and Marine Engineering and Management from Delft University of Technology (2018), and a BSc in Harbour, Coastal and Offshore Engineering from Hohai University (2016). Mao specializes in integrating coastal morphology with advanced remote sensing and machine learning techniques to address critical shoreline monitoring challenges. His research focuses on developing SAR-Optical satellite image fusion methods for cloud-contaminated imagery, implementing data assimilation for shoreline modeling, and creating deep learning spatio-temporal models (such as ConvLSTM and PredRNN) for predicting future shoreline changes at regional to global scales. His work significantly contributes to understanding coastal responses to sea-level rise and improving satellite-derived shoreline mapping accuracy. Mao's publication portfolio demonstrates consistent high-impact contributions to coastal remote sensing, with recent work benchmarking shoreline prediction models and developing innovative fusion techniques. His research bridges theoretical advancements with practical applications for coastal management, particularly in vulnerable regions like the Great Barrier Reef catchments. UQ Dean's Award for Outstanding HDR Theses (2022) Erasmus Mundus Partner Country Scholarship (€49,000) (2016) Mao actively contributes to coastal research through the Water Research Laboratory at UNSW, where he applies cutting-edge computational methods to real-world coastal challenges. His teaching responsibilities include CVEN9620 Rivers, Estuaries and Wetlands and ENGG2500 Fluid Mechanics for Engineers, where he integrates his research expertise into curriculum development. Current projects focus on spatio-temporal deep learning applications for shoreline change prediction and satellite-derived environmental monitoring systems.

Sandro Luigi Fioreمشاهده پروفایل

دانشیار

• Data Science
• Big Data
• Scientific Data Management
• +۱۳ مورد دیگر

Sandro Luigi Fiore is an Associate Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy. He has held academic positions at the University of Salento and visiting scientist roles at the University of Chicago and Lawrence Livermore National Laboratory. His research integrates data science, big data, and high-performance computing with climate informatics and open science. Education: Ph.D. in Innovative Materials and Technologies, University of Lecce, 2004 Master of Science in Computer Engineering (with honors), University of Lecce, 2001 His research interests span Data Science, Big Data, Scientific Data Management, Artificial Intelligence, and Distributed/Cloud/Parallel Computing , with a strong application focus on Climate Change and Open Science . He develops FAIR-enabled data analytics solutions and contributes to large-scale data infrastructures such as ESGF, EOSC-hub, and INDIGO-DataCloud. His work emphasizes provenance, reproducibility, and interoperability in scientific computing. The articles reflect a consistent trajectory in large-scale scientific data systems , evolving from Grid-DBMS and parallel computing to modern applications in climate informatics, AI, and FAIR data. Key themes include distributed data management, middleware, exascale software, and reproducible workflows in HPC environments. Scientific Awards: Earth System Grid Federation Team Award (2017) UNIDATA Community Equipment Award (2011) Best Student Paper Award, ITCC2003 Fiore has advised on and led multiple national and European projects including EOSC-hub, IS-ENES, and BARRACUDA (RDA-Europe3). He actively supports the adoption of FAIR data principles in scientific repositories, advising on data policies, architecture, provenance, and interoperability. He is a member of the FAIR Champions group and serves on the Advisory Board of FAIRsFAIR. He is involved in key research teams and projects such as the Earth System Grid Federation (ESGF) , Globus Lab (University of Chicago) , and PCMDI/LLNL . His work contributes to climate model intercomparison (CMIP5/6) and the development of next-generation data infrastructure for open science.

• Neural Machine Translation
• Computational Linguistics
• Natural Language Processing
• +۳ مورد دیگر

Yang Liu is a researcher at the Institute of Computing Technology (ICT), Tsinghua University , and affiliated with the Beijing Academy of Artificial Intelligence . His work spans computational linguistics, machine translation, and dialogue systems, with additional collaborations in medical NLP and cross-lingual knowledge representation. Doctorate from Purdue University Collaborations with Microsoft, Amazon, and Facebook researchers Research focuses on Neural Machine Translation (NMT) advancements through: Self-supervised alignment and regularization Document-level context integration Robust translation frameworks for low-resource scenarios Adapter-based pluggable models Recent works extend to conversational AI, including: Subjective knowledge integration in task-oriented dialogue Context representation analysis for open-domain dialogue Synthetic conversation dataset generation Key contributions include: THUMT: Open-source NMT toolkit Earth Mover's Distance for lexicon induction Context Gates for translation fluency Publications demonstrate continuous engagement with top-tier conferences including ACL, EMNLP, and COLING, collaborating with prominent figures like Maosong Sun and Qun Liu.

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.

• Numerical Methods for Continuum Physical Modelling
• Scientific Software Development
• Computational Geometry
• +۱ مورد دیگر

Alan Correa is a PhD candidate and Scientific Researcher at the Chair of Methods for Model-based Development in Computational Engineering, RWTH Aachen University, Germany. His research advances forward solvers for thin film free surface flows over complex geometry through numerical methods, computational geometry, and automatic differentiation, with strong advocacy for open source software principles. Education Master of Science (with distinction), Computer Aided Conception and Production in Mechanical Engineering, RWTH Aachen University, Germany (2023) Bachelor of Technology, Mechanical Engineering, Sardar Patel College of Engineering, University of Mumbai, India (2017) Research Interests Alan's work spans Numerical Methods for Continuum Physical Modelling , Scientific Software Development , Computational Geometry , and Scientific Machine Learning . His PhD project integrates depth-integrated Shallow Water Equations (Surface PDEs) with computational geometry and automatic differentiation to model complex fluid dynamics. He has developed computational material models for visco-elastic hydrogels, automated 3D mesh generation systems, and state estimation software for autonomous vehicles. Publication Trends His 2024 publications demonstrate a cohesive focus on computational reproducibility through Julia-based workflows and FAIR research practices. Both papers address critical challenges in sustainable research software engineering, emphasizing reusable datasets, containerized compute environments, and transparent simulation pipelines within computational engineering and earth sciences contexts. Scientific Awards Dean's List, RWTH Aachen University (2022) Best of Class Awards, RWTH Aachen International Academy (2021) J.N. Tata Scholarship for Higher Education of Indians (2019) Best Outgoing Student Award, Sardar Patel College of Engineering (2017) Advising and Funding As a PhD candidate, Alan does not supervise students. His research is funded through his Scientific Researcher position at RWTH Aachen and scholarship support including the prestigious J.N. Tata endowment. Professional experience includes structural analysis internships at Airbus and graduate research at RWTH's Material Applications Institute. Research Environment Alan operates within RWTH Aachen's Modeling & Simulation Sciences profile area, collaborating across engineering, mathematics, and computer science disciplines. The Chair develops performant simulation methods and sustainable software implementations for model-based decision support systems, with active participation in international conferences like JuliaCon and EGU General Assembly.

• Machine Learning
• Remote Sensing
• Artificial Intelligence
• +۹ مورد دیگر

Jordi Muñoz Mari is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. He is a key member of the Image and Signal Processing (ISP) research group within the Image Processing Laboratory (IPL), a recognized research institute (ERI) at the university. His research focuses on the intersection of artificial intelligence, remote sensing, and environmental science. Key areas include machine learning for Earth observation, biophysical parameter retrieval, crop yield forecasting, climate modeling, and causal inference in ecological systems. He also explores applications of AI in education, particularly generative models for assessment and digital learning tools. The most recent publications highlight a strong trend toward physics-aware machine learning, uncertainty quantification, and large-scale applications using platforms like Google Earth Engine. His work spans climate extremes (medicanes, hurricanes, droughts), agricultural monitoring, biodiversity, and educational technology, demonstrating interdisciplinary innovation. Jordi Muñoz Mari completed his PhD at the University of Valencia in 2004 on telemonitoring systems for cardiac patients. His ongoing research involves advanced signal processing, kernel methods, and AI-driven geoinformation systems.