Marina Astitha is an Associate Professor at the University of Connecticut's College of Engineering , specifically within the School of Civil and Environmental Engineering . Her research bridges atmospheric science with practical applications in energy systems and environmental management. Ph.D. in Physics from the University of Athens (2007) Specializes in high-resolution weather modeling and machine learning integration Research Interests include: Atmospheric Physics, Dynamics, and Chemistry Extreme Weather Event Prediction Multi-Media Modeling Systems Uncertainty Quantification in Atmospheric Models Climate Change Impacts on Wind Energy Resources Real-Time Weather and Air Quality Forecasting Scientific Awards : No awards explicitly mentioned in the provided data, but her publications and research activities indicate significant contributions to meteorology and environmental engineering fields. Advising and Grants : Specific details about grants and students are not provided in the available information, though her research focus suggests involvement in funded projects related to climate change, renewable energy, and environmental modeling. Labs and Teams : Leads the Atmospheric Modeling Group at UConn, integrating numerical weather prediction with machine learning techniques for environmental and energy applications.
Dr. Yang Du is a Senior Lecturer at James Cook University (JCU) in Cairns, Australia, specializing in Electronic Systems and IoT Engineering. He holds a Ph.D. in Electrical Engineering from The University of Sydney (2013) and has held academic positions at Xi'an Jiaotong-Liverpool University (2014–2018) and a visiting scientist role at MIT (2018). His research focuses on renewable energy systems, solar forecasting, and smart grid technologies. He has been recognized in the World’s Top 2% Scientists List by Stanford University and has authored numerous publications in top-tier journals. Education: Ph.D. in Electrical Engineering, The University of Sydney, Australia (2013) Postdoctoral Research Fellow at Masdar Institute of Science and Technology, UAE (2013–2014) Dr. Du’s research interests revolve around optimizing renewable energy integration, particularly in photovoltaic systems, energy storage, and advanced control strategies using machine learning. His work emphasizes predictive modeling for solar power ramp-rate control, federated learning frameworks for distributed energy systems, and the application of AI in IoT-driven energy management. Recent projects include ADMM-LSTM frameworks for load forecasting and thermal analysis of power devices. His publications highlight contributions to energy forecasting, grid stability, and adaptive control mechanisms. Notable achievements include developing solar forecasting models using GANs and sky images, and exploring peer-to-peer energy trading mechanisms with penalty adaptations. He actively collaborates with institutions like MIT and maintains an honorary position at Xi’an Jiaotong-Liverpool University. Dr. Du’s work addresses challenges in energy systems, such as mitigating PV power fluctuations and enhancing grid resilience through predictive analytics. His research has practical applications in microgrids, energy storage optimization, and sustainable power distribution. He has been awarded 33 academic accolades, including recognition for his impactful contributions to renewable energy research.
Asgeir Sorteberg is a Professor at the University of Bergen's Geophysical Institute and affiliated with the Bjerknes Centre for Climate Research. His research focuses on climate dynamics, extreme weather events, and their societal impacts. Key areas include projections of windstorm damages under climate change, offshore wind energy potential, and climate adaptation strategies. He has contributed to projects like StormRisk, examining damage modeling and future wind climates. His work integrates meteorological data, machine learning, and climate modeling to address challenges in environmental science and renewable energy. Notable collaborations include the Bergen Offshore Wind Centre (BOW) and the Norwegian Meteorological Institute. His presentations at workshops and conferences highlight contributions to climate policy, infrastructure resilience, and Arctic climate studies. Sorteberg's research spans regional climate analysis, hydrological responses to precipitation extremes, and validation of high-resolution climate datasets like NORA3. He frequently addresses interdisciplinary topics such as the impact of atmospheric rivers on Norwegian precipitation and the mitigation of energy intermittency through interconnected offshore wind systems. His findings inform climate adaptation policies and sustainable energy planning in Norway and beyond.
Andrew Clelland is a postdoctoral researcher in Machine Learning and AI for Wildfire Modelling at Imperial College London's Department of Computing, Faculty of Engineering. He works at the Data Science Institute under Dr. Rossella Arcucci, collaborating with the Leverhulme Centre for Wildfires, Environment and Society. PhD in Climate Change and Fire Activity (Boreal Forests), Durham University & British Antarctic Survey MSc (Hons), Novosibirsk State University BSc (Hons), University of Manchester His research focuses on 3D wildfire nowcasting using multi-source data fusion (social media, satellite imagery, climate data). He contributes to the DataLearning Group's AI model development for real-time threat prediction to regional authorities and communities. Andrew's academic journey includes collaborations with Woodwell Climate Research Center and University of Alaska, Fairbanks on the Permafrost Pathways project. His work spans interdisciplinary applications of AI in environmental science and climate-fire dynamics.
Dr. Sara Mirbagheri Golroodbari serves as an Assistant Professor in the Energy & Resources department at Utrecht University's Copernicus Institute of Sustainable Development, Faculty of Geosciences. She specializes in the integration of photovoltaic solar energy systems, with particular expertise in offshore and floating solar technologies. Her research interests span multiple dimensions of solar energy systems, including solar PV integration feasibility studies, local agrivoltaic systems in the Netherlands, responsible deployment of floating PV systems (both onshore and offshore), land restoration potential through solar installations, offshore floating PV data analysis, offshore solar energy transmission methods, solar energy forecasting using advanced techniques like All Sky Imager and machine learning algorithms, and big data analysis of large-scale PV systems. Dr. Golroodbari's publication record shows a consistent focus on solar energy technologies, particularly floating photovoltaic systems, with numerous high-impact publications in leading energy journals. Her recent work has examined global offshore floating photovoltaics system assessment, enhanced water cooling models for offshore PV, and advanced solar forecasting techniques incorporating artificial intelligence. Graduated with distinction (2013) Grand Cru 2019 award (November 13, 2019) Scholarship awarded (October 2019) Scholarship awarded (October 2018) She actively contributes to international collaborations as a member of the ETIP PV solar integration working group and IEA_PVPS task 13. Dr. Golroodbari teaches courses including 'Introduction to Energy and Material Analysis' and 'Photovoltaics: basics and integration' within the Energy Science and Environmental Sciences programs.
Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.
Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.
Craig Robson is a Lecturer in Data Centric Engineering at the School of Engineering, Newcastle University. He holds a PhD in Geospatial Engineering (2017) and has been involved in research and teaching focused on geospatial data applications in civil engineering. His work emphasizes climate change impacts on infrastructure, network resilience, and integrated assessment frameworks. Robson is part of the Geospatial Engineering group and leads projects like the MACC Hub and OpenCLIM initiative. Education: PhD in Geospatial Engineering (2017, Newcastle University). Research expertise includes geospatial data management, infrastructure network analysis, and digital twins. He serves as GISRUK national Chair and DAFNI Champion, promoting geospatial standards and national infrastructure analytics. Teaching roles include modules like CEG3005 (Data-centric urban environment) and CEG8006 (Digital Engineering and Analytics). Key research projects include the UK Climate Resilience Programme (PI), FloodPrepared real-time flood modeling, and contributions to NERC's Digital Solutions Hub. His work spans climate adaptation frameworks, urban resilience, and data-driven decision tools for infrastructure systems. Grant involvement includes £2M OpenCLIM project (Co-I) and MACC Hub leadership. He supervises PhD students researching topics like tailings dam failures and urban network resilience. Collaborations include EA, Arup, and the Tyndall Centre.
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.
Professor Ralf Brüggemann is a full-time faculty member at the University of Konstanz , holding the Chair of Statistics and Econometrics since October 2007. He completed his Habilitation in Time Series Econometrics at Humboldt-Universität zu Berlin in 2007 and received his Ph.D. in Economics in 2003 for work on VAR model reduction techniques. Education : Habilitation: "Topics in Time Series Econometrics", Humboldt University Berlin (2007) Ph.D.: Economics, Humboldt University Berlin (2003) Diplom: Economics, Humboldt University Berlin (1999) His research spans Time Series Econometrics with focus on Cointegrated VAR Models , Structural VAR/VECM , Forecasting Methods , and Empirical Macroeconomics . Key contributions include methodological work on structural identification, variable selection in high-dimensional VAR, and monetary policy analysis using microeconomic data. Recent publications address External instruments in SVAR identification (2022) Directed graphs for VAR variable selection (2022) Stochastic aggregation weights in forecasting (2023) Asymmetric impulse responses in European financial markets (2014) with methodological innovations in heteroskedasticity-robust inference and stochastic aggregation weights. Scientific Awards : Jean Monnet Fellow, European University Institute (2003-2004) He leads research on monetary policy transmission mechanisms and macroeconomic risk through collaborative projects with institutions like the German Research Foundation Collaborative Research Center 649 (2005-present) and serves as editor for the Journal of Economics and Statistics special issue on Economic Forecasts (2011).
Dr Ben Pickering is a Research Fellow at the University of Leeds and affiliated with the National Centre for Atmospheric Science. His work focuses on developing advanced rainfall retrieval systems using satellite data and machine learning techniques, with applications in near real-time nowcasting over Africa. His research integrates meteorology and engineering to improve forecasting tools and societal decision-making. He collaborates with institutions in the UK and Africa to develop practical web-based nowcasting solutions and promotes interdisciplinary collaboration through forecasting testbeds. Dr Pickering is actively involved in postgraduate supervision, scientific outreach, and leads a weekly weather discussion forum at the University of Leeds. He emphasizes the value of extracurricular engagement and interdisciplinary exploration in shaping a successful career in natural sciences. He advocates for diversity in the natural sciences, recognizing its broad societal and environmental impacts, and aims to bridge gaps between academia and industry through commercially viable research applications in meteorological measurement technologies.
Eugene Belilovsky is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on machine learning, computer vision, optimization algorithms, and their applications in medical imaging and federated learning. He explores topics such as continual learning, model merging, and bias mitigation in deep learning systems. His work bridges theoretical advancements with practical applications, including healthcare diagnostics and efficient training strategies for large models. Belilovsky's recent publications emphasize innovative methods like MuLoCo for inner optimizer design, FairDropout for enhancing minority group generalization, and techniques to scale multi-task learning with sparse masks. His studies on federated learning explore incentive mechanisms for decentralized systems and robust pre-training of large language models. His contributions to 3D reconstruction and motion prediction highlight interdisciplinary applications in computer graphics and generative models. His research trends reflect a strong emphasis on improving model efficiency, fairness, and adaptability across diverse domains. While no scientific awards are explicitly listed, his prolific publication record underscores his impactful contributions to AI and machine learning.
David James Delene is a Research Professor in the Department of Atmospheric Sciences at the University of North Dakota , with a secondary appointment as Aerospace Research Fellow at the John D. Odegard School of Aerospace Sciences. His expertise spans Cloud Physics , Atmospheric Aerosols , and Airborne Measurements , with a focus on Scientific Programming and Open Source Software development. He has taught advanced courses in Atmospheric Chemistry and Measurement Systems since 2006 and led significant research initiatives including the IMPACTS and FATIMA field campaigns. Education : Ph.D. in Atmospheric Science (University of Wyoming), MS in Geophysics (Michigan Tech), BS in Applied Physics (Michigan Tech) Research Interests center on airborne measurement systems, cloud microphysics, aerosol dynamics, and machine learning applications in meteorology. He develops open-source tools like ADTAE and adpaa_readplot_ccncdata for atmospheric data analysis. His work bridges Remote Sensing with Statistical Analysis to improve weather modification techniques. Scientific Awards include: UND's Spirit Faculty Achievement Award (2014) Golden Remer Awards (2007, 2013) Biggest Techie Award (2009) Delene advises both undergraduate capstone projects and graduate students in Atmospheric Sciences, with recent master's advisees including Kendra Sand (2024) and Joseph O'Brien (2023). He manages the Ballooning Laboratory and maintains the department's Atmospheric Sciences Wiki , contributing to UNIDATA and NASA EPSCoR programs.
Yufei Li is a Professor at Xi'an Jiaotong University's School of Computer Science and Technology, Department of Computer Science. With an extensive publication record spanning from 2007 to 2025, Dr. Li has established himself as a prominent researcher in database systems, software engineering, and machine learning applications. His work demonstrates strong interdisciplinary connections between computer science and electrical engineering, particularly in power systems applications. Dr. Li's research interests span multiple domains of computer science and engineering. His primary focus is on database systems, where he has pioneered work in LLM-based database tuning systems like GPTuner. He also has significant contributions in software configuration and performance optimization, as evidenced by his CSAT framework. His research extends to computer vision applications for security screening and medical diagnostics, as well as electrical engineering applications in power systems and UAV control. This diverse portfolio demonstrates his ability to bridge theoretical computer science with practical engineering applications across multiple domains. Analysis of Dr. Li's recent publications (2023-2025) reveals a strong trend toward integrating large language models with traditional computer science domains. His work on GPTuner represents a significant advancement in applying LLMs to database tuning, while his research on QUITE demonstrates innovative approaches to query rewriting using LLM agents. There's also a clear pattern of applying advanced machine learning techniques to solve domain-specific problems across electrical engineering, medical diagnostics, and industrial quality control. The interdisciplinary nature of his work positions him at the forefront of AI integration across multiple engineering disciplines. Dr. Li has established a productive research group with numerous doctoral students and collaborators, particularly Jiale Lao, Yibo Wang, and Jianguo Wang who frequently appear as co-authors on his recent publications. His research has been consistently funded, as evidenced by the steady stream of publications across multiple high-impact venues including IEEE Access, Journal of Systems and Software, and CVPR. His work demonstrates strong industry relevance with applications in database management, software configuration, security screening, and power systems. Dr. Li leads a research team focused on database systems and AI integration, with strong connections to both computer science and electrical engineering domains. His group appears to specialize in applying cutting-edge machine learning techniques, particularly large language models, to solve longstanding problems in database management and software engineering. The team maintains active collaborations with researchers across multiple institutions, as evidenced by the diverse author lists on his publications.
Dr. Huili Chen is a Senior Lecturer in Water Engineering at Loughborough University. Her research focuses on developing innovative methodologies using satellite observations and high-performance modelling tools to assess disaster impacts and risks, particularly in data-sparse regions. She specializes in flood risk reduction, climate change adaptation, and hydrodynamic modelling. Her work emphasizes safeguarding vulnerable populations in disaster-prone areas through advanced data analytics. Education: BEng, MSc, PhD Research Interests: Flood risk assessment, disaster impact modelling, satellite data integration, climate resilience strategies Dr. Chen leads and collaborates on projects such as the UNESCO Chair in Informatics and Multi-hazard Risk Reduction, the SPIRIT initiative for flood modelling standardization, and the FLASH project for hazard assessment in Bhutan. She has pioneered techniques like the High-Performance Integrated Hydrodynamic Modelling System (HiPIMS) and GIS-based DEM correction methods to enhance flood simulation accuracy. Her research also addresses agricultural flood damage, pluvial flood nowcasting, and glacial lake outburst flood impacts using open-source data. Her advisory and grant activities include roles as Principal Investigator (PI) and Co-Investigator (CoI) on multiple grants from NERC, EPSRC, and the Royal Society. She has managed projects totaling over £multi-million, demonstrating leadership in interdisciplinary environmental engineering solutions. Dr. Chen’s work contributes to global initiatives like the UNESCO Chair program and the SHEAR-funded WeACT project, advancing flood hazard assessment and forecasting systems for vulnerable regions. Her research bridges computational modelling, environmental science, and policy to promote sustainable resilience strategies.
