Find Academic Supervisors

Kristin TimmView profile

Academic

• Climate change
• Science communication
• Social science
• +2 more

Kristin Timm serves as a Research Assistant Professor at the International Arctic Research Center (IARC), University of Alaska Fairbanks, and is actively engaged with the Alaska Climate Adaptation Science Center. Her work bridges scientific research and community adaptation efforts in Arctic contexts through specialized focus on environmental communication and stakeholder collaboration. Her research expertise spans climate change communication, social science methodologies, and stakeholder engagement frameworks. She employs mixed-methods approaches including surveys and interviews to analyze how messengers, messages, and communication processes shape public understanding and behavioral responses to environmental challenges. Central to her work is the principle of equitable co-production of knowledge between researchers and communities, particularly in climate adaptation contexts where trust and cultural relevance are critical. Analysis of her 15 most recent publications (2020-2025) reveals dominant themes in climate change communication across media platforms, with significant attention to broadcast meteorology perspectives, journalism practices, and equitable knowledge co-production. Key trends include the examination of racial dynamics in climate reporting, probabilistic forecasting of glacial impacts, and the effectiveness of professional training programs for climate communicators. Her interdisciplinary work consistently intersects environmental science, social psychology, and policy implementation. Dr. Timm contributes to the Alaska Climate Adaptation Science Center through projects that translate climate research into actionable community strategies, emphasizing participatory approaches that incorporate local and Indigenous knowledge systems in Arctic resource management.

• Space Physics
• Magnetospheric Physics
• Radiation Belt Dynamics
• +5 more

Dr. Clare E. J. Watt is a prominent researcher in space physics at the University of Reading, with a distinguished publication record spanning over 15 years. Her work primarily focuses on magnetospheric physics, radiation belt dynamics, and space weather phenomena, contributing significantly to our understanding of Earth's space environment. Dr. Watt's research interests center on wave-particle interactions in Earth's magnetosphere, particularly examining ultra-low frequency (ULF) waves, plasmaspheric hiss, and their effects on radiation belt electrons. Her work bridges theoretical modeling with observational data, investigating how these waves influence electron acceleration and loss processes during geomagnetic storms. She has made significant contributions to understanding the temporal and spatial variability of wave-particle interactions, developing probabilistic mapping tools, and creating advanced models for radiation belt dynamics. Analysis of her recent publications reveals a strong emphasis on data-driven approaches to space physics, including machine learning applications for solar wind classification and ULF wave modeling. Her research demonstrates a consistent focus on quantifying uncertainties in space weather models and understanding the complex interplay between different wave modes in the magnetosphere. Dr. Watt frequently collaborates with leading space physics researchers across multiple institutions, contributing to major studies on space climate and radiation belt dynamics. Dr. Watt has co-authored numerous high-impact publications in leading journals including Journal of Geophysical Research: Space Physics, Geophysical Research Letters, and Space Weather. Her work has been instrumental in advancing our understanding of radiation belt dynamics and space weather prediction capabilities. She has also contributed to book chapters on ULF waves and Alfvén wave acceleration of auroral electrons, demonstrating her expertise in both observational and theoretical aspects of space physics.

Henrik MadsenView profile

Professor

• Energy Systems
• Stochastic Processes
• Demand Response
• +7 more

Henrik Madsen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His work focuses on energy systems, stochastic processes, and mathematical modeling. He leads research in areas such as demand response in district heating, probabilistic forecasting, and integration of renewable energy sources. Madsen has supervised multiple PhD students and contributes to projects like the IEA DHC Annex TS9 and SEEDS initiative. His expertise includes statistical analysis, time series modeling, and data-driven approaches for energy systems. He actively engages in collaborative research on smart grids, thermal energy storage, and sustainable energy solutions. Education: Academic qualifications from DTU, though specific details are not provided in the text. Research Interests: Mathematical modeling of energy systems Probabilistic forecasting methodologies Integration of wind and solar power Dynamic modeling for district heating Data assimilation in hydrological systems Optimization of energy flexibility Publications & Trends: Recent works emphasize demand response strategies, district heating optimization, and machine learning tools like the nabqr Python package and evalprob4cast R package. His research bridges theoretical stochastic methods with practical applications in energy infrastructure. Grants & Projects: IEA DHC Annex TS9: Digitalization of district heating SEEDS: RES-integrated electrified heating systems Data-Driven Methods for Demand-Side Flexibility Labs/Teams: Collaborates with DTU's Dynamical Systems group and participates in interdisciplinary initiatives like the Frigg 2.0 energy system analysis framework.

• Machine Learning
• Climate Science
• Energy Systems
• +3 more

Sancho Salcedo Sanz is a Full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department and the GHEODE Research Group. His work focuses on applying machine learning and optimization techniques to energy systems, climate science, and environmental modeling. He holds PhDs from Universidad Complutense de Madrid (2019) and Universidad Carlos III de Madrid (2002). Key research interests include deep learning for energy price prediction, spatio-temporal climate analysis, and hybrid models for renewable energy forecasting. His GHEODE group develops optimization algorithms for network design and distributed systems. Recent publications highlight advancements in extreme weather prediction, smart grid optimization, and explainable AI for environmental monitoring. He has pioneered methodologies like Autoencoder-based flow analogues for heatwave reconstruction and multi-method ensembles for energy demand modeling. Labs/Teams: Leader of the GHEODE Group, specializing in modern heuristics and network design. Collaborates extensively on interdisciplinary projects combining AI with environmental and engineering applications.

• Atmospheric Science
• Renewable Energy
• Wind Power Forecasting
• +5 more

Dr. Cannon Dirk is a researcher affiliated with the University of Reading, as evidenced by his publications in the university's institutional repository, CentAUR. His work lies at the intersection of atmospheric science and renewable energy systems, with a focus on wind power forecasting and meteorological modeling. His research interests span Atmospheric Science , Renewable Energy , Wind Power Forecasting , Orographic Precipitation , Climate Modeling , and Energy Meteorology . These are reflected in his publications, which combine meteorological dynamics with practical energy system challenges. The recent articles show a strong trend toward probabilistic forecasting of wind power generation, quantification of extreme events using long-term reanalysis data, and the interaction between atmospheric processes and renewable energy infrastructure. His work often involves modeling frameworks applied to Great Britain's energy landscape. Dr. Cannon has collaborated with prominent researchers such as David Brayshaw, Joanne Methven, and Suzanne Gray, indicating integration within a well-established meteorological and energy research group at Reading. There are no listed scientific awards or honors in the provided data. Dr. Cannon has contributed to both journal articles and technical reports, including a MATLAB-based wind power model. While no formal advising or grant information is available, his repeated work on energy-meteorology integration suggests involvement in research projects related to sustainable energy systems. No lab or team name is explicitly mentioned, but his research aligns with atmospheric and renewable energy modeling groups, possibly within the Department of Meteorology at the University of Reading.

Anaïs CouasnonView profile

Researcher

• Flood Risk Management
• Climate Change
• Environmental Science
• +3 more

Anaïs Couasnon is a PhD researcher at the Department of Water and Climate Risk, Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, part of the Faculty of Science. Her research focuses on compound flood risk modeling, probabilistic methods, and global hazard assessment under climate change. She is supervised by Dr. Philip Ward and Dr. Hessel Winsemius as part of a VIDI project. Education: MSc in Hydraulic Engineering (TU Delft, 2017); BSc in Civil Engineering (McGill University, 2010). Research interests include probabilistic modeling, multivariate dependence analysis, flood risk management, and climate impacts on coastal-riverine interactions. She contributes to global datasets like COAST-RP and socio-hydrological benchmarking. Key activities include: Developing frameworks for compound flood risk assessment Modeling extreme sea-level events and storm surges Collaborating on global hazard frameworks and disaster risk reduction strategies Contributions to datasets include: COAST-RP dataset Panta Rhei socio-hydrological benchmark dataset

• Landscape Ecology
• Biodiversity
• Hydrology
• +3 more

Jess Neumann is an active researcher at the University of Reading, with a focus on interdisciplinary environmental science. Her work spans Landscape Ecology , Hydrology , and Biodiversity studies, often addressing climate change impacts and conservation strategies. ORCID: 0000-0003-3244-2578 Collaborator on 21+ publications (2014–2025) Key journals: Landscape Ecology , Natural Hazards and Earth System Sciences , Diversity and Distributions Research Themes: Flood preparedness under uncertainty, nature-based solutions for flood risk, climate-driven species distribution changes, and landscape heterogeneity effects on biodiversity. Her recent work on meteotsunami intensity indexes and social contracts in flood governance highlights cross-sectoral relevance. Scientific Contributions: Developed frameworks for probabilistic flood forecasting Advocates for citizen science in ecological monitoring Explores urban wildlife conservation (e.g., hedgehogs, pheasants) Pioneers ecological data integration across spatial scales

Martina KauzlaricView profile

Researcher

• Hydrology
• Flood Risk Management
• Climate Change Impacts
• +3 more

Dr. Martina Kauzlaric is a researcher at the University of Bern's Institute of Geography, specializing in hydrological modeling and flood risk management. She develops national-scale frameworks to enhance flood forecasting systems in Switzerland, focusing on transparent, open-source models for operational use. Her work integrates meteorological forecasts with impact assessment to improve emergency response, emphasizing high-resolution flood risk cascades and probabilistic impact visualization tools. Her research addresses antecedent conditions' role in extreme flood generation, stochastic weather generator applications, and glacier-hydrology coupling to improve predictive accuracy. Collaborative efforts include participatory storymap development for disaster preparedness and database creation (e.g., CAMELS-CH) to standardize hydrological studies. Key contributions span flood impact modeling frameworks, surrogate models for efficient simulations, and interdisciplinary analyses of hydrological model diversity. Her projects often emphasize climate change impacts in alpine regions and the societal benefits of integrated forecasting systems. Notable projects include: National hydrological modeling framework for Switzerland Impact-based flood warning tools Storyline tools for civil protection training Participatory visualization for flood risk communication Her work aligns with enhancing decision support systems through model efficiency, uncertainty quantification, and stakeholder engagement.

Adrián García GutiérrezView profile

Professor

• Aerospace Engineering
• Uncertainty Quantification
• Computational Fluid Dynamics
• +3 more

Adrián García Gutiérrez is a Professor in the Department of Aerospace Engineering at the University of León's College of Engineering. His research focuses on aerospace systems, uncertainty quantification in CFD, atmospheric boundary layer modeling, and airship technology. Recent publications highlight his work in parallel orbital propagation algorithms, stochastic optimization of high-altitude platforms, and neural network applications for wind profiling. Key trends include aerodynamic modeling under uncertainty and interdisciplinary approaches combining turbulence analysis with LiDAR measurements. He contributes to educational innovation in aerospace engineering through simulation-based learning tools and leads the GITA Tecnología Aeroespacial research group.

• Arctic climate science
• Ecohydrology
• Land-atmosphere interactions
• +3 more

Norbert Pirk is an Associate Professor at the University of Oslo, affiliated with the Department of Physical Geography and Hydrology. His research focuses on ecohydrology in cold environments, land-atmosphere energy and trace gas exchange, and snow-vegetation interactions. He teaches courses including Geophysical Data Science and Fluvial Hydrology, and contributes to arctic climate studies through field campaigns and modeling. His research spans tundra carbon cycling, permafrost dynamics, and machine learning applications in climate modeling. Recent publications highlight advancements in snow data assimilation techniques, greenhouse gas flux monitoring using drones, and deep learning methods for Arctic methane analysis. He actively collaborates within international research networks. Current projects include SvalGaSess (methane monitoring in Svalbard), SnowSub (snow sublimation impacts on hydropower), and EMERALD (terrestrial climate interactions). He contributes to initiatives like FLUXNET2015 and the Land Sites Platform, advancing Arctic observation systems and climate modeling frameworks.

Lance ManuelView profile

Professor

• Uncertainty Quantification
• Wind Energy
• Offshore Structures
• +5 more

Lance Manuel is a Professor of Engineering at The University of Texas at Austin. His research focuses on uncertainty quantification in engineered systems, particularly wind energy and offshore structures. He has led projects related to wind turbine fatigue analysis, hurricane risk assessment, and climate change adaptation. Ph.D., Civil Engineering (Stanford University) M.S., Civil Engineering and Applied Mechanics (University of Virginia) B.Tech., Civil Engineering (Indian Institute of Technology, Bombay) His work bridges civil infrastructure with climate resilience, emphasizing probabilistic methods for structural reliability. Key areas include extreme climate modeling , floating offshore wind turbines , and fatigue damage prediction under non-stationary conditions. Recent publications highlight interdisciplinary trends in renewable energy systems and climate hazard quantification . He advises graduate students like Taemin Heo and Ding Peng Liu, whose work spans stochastic processes and offshore structural reuse.

Federico DivinaView profile

Professor

• Bioinformatics
• Evolutionary Computation
• Machine Learning
• +5 more

Federico Divina is a Professor in the Department of Sports and Computer Science at Universidad Pablo de Olavide, Spain. His research focuses on evolutionary computation, machine learning, and bioinformatics. PhD in Artificial Intelligence from Vrije Universiteit Amsterdam Former postdoc at University of Tilburg (NEWTIES EU project) Research Interests : Bioinformatics, Evolutionary Computation, Machine Learning, Big Data, and Soft Computing. He specializes in knowledge extraction from massive datasets through genetic algorithms and heuristic optimization. Recent Publications span time series forecasting, feature selection in high-dimensional data, and biomedical image analysis. His work combines evolutionary algorithms with practical applications in energy consumption prediction, ocular disease diagnosis, and genomic data analysis. Labs & Collaborations : Leads the DS&BD Data Science & Big Data Lab and has collaborated with groups like DATAi and DASE. His projects include Differential (multi-university energy analysis), GALICIAME (SMA genetic data analysis), and NEWTIES (artificial society modeling).

Aneesh SubramanianView profile

Assistant Professor

• Weather and Climate Predictability
• Climate Dynamics
• Atmospheric River Dynamics
• +6 more

Dr. Aneesh Subramanian is an Assistant Professor in the Department of Atmospheric and Oceanic Sciences (ATOC) at the University of Colorado Boulder. He also holds visiting positions at the Center for Western Weather and Water Extremes at Scripps Institution of Oceanography, UC San Diego, and as a visiting scholar in the Predictability of Weather and Climate group at the University of Oxford. Additionally, he serves as an international collaborator with the Geophysical Flows Lab at the Indian Institute of Technology Madras. His educational background includes: Ph.D. in Climate Research from Scripps Institution of Oceanography, UC San Diego (2012) M.Sc. (Engineering) from Indian Institute of Science (2006) B.Tech from Indian Institute of Technology (IIT) Madras (2004) Dr. Subramanian's research focuses on weather and climate predictability, with particular emphasis on subseasonal-to-seasonal forecasting. His work spans tropical climate dynamics, atmospheric river prediction, data assimilation techniques, and the application of machine learning to improve earth system models. He investigates coupled ocean-atmosphere processes, particularly related to the Madden-Julian Oscillation and its teleconnections, and develops stochastic parameterization schemes for climate models. Dr. Subramanian's recent publications demonstrate a strong focus on advancing subseasonal-to-seasonal prediction capabilities, particularly for extreme weather events like atmospheric rivers and marine heatwaves. His work increasingly integrates machine learning techniques with traditional physics-based approaches, reflecting a growing trend in the field toward hybrid modeling frameworks. Much of his recent research examines regional climate phenomena in the Indian Ocean, Pacific, and Arabian Sea regions, with applications to monsoon prediction and understanding climate change impacts. Dr. Subramanian has received several notable awards and honors throughout his career: Best Team in visualization of weather forecasts Award, ECMWF Users Meeting (2017) Best Student Presentation Award, WCRP Open Science Conference (2011) Best Teaching Assistant Award, Scripps Institution of Oceanography (2011) SUNNY Scripps-NCAR Graduate Student Fellowship (2009-2011) NCAR ASP Summer Fellowship (2008, 2012) Dr. Subramanian has secured multiple research grants totaling over $3 million from agencies including NOAA, ONR, NASA, and KAUST. Current projects focus on improving understanding of air-sea interaction processes, marine ecosystem drivers in the California Current System, monsoon intra-seasonal oscillations, and marine heatwaves. He actively mentors undergraduate research assistants, graduate students, and postdoctoral scholars through his Climate Processes and Predictability Group at CU Boulder. Dr. Subramanian leads the Climate Processes and Predictability Group at CU Boulder, which focuses on subseasonal predictability, data assimilation, Atmospheric River dynamics, and tropical-extratropical teleconnections. He is also an active participant in several collaborative research initiatives including the Geophysical Flows Lab at IIT Madras and the Center for Western Weather and Water Extremes at Scripps Institution of Oceanography. His work frequently involves international collaborations with researchers from institutions in the UK, Saudi Arabia, and India.

Eric AndersonView profile

Associate Professor

• Hydrosphere-Cryosphere-Atmosphere Interactions
• Numerical Modeling of Lakes/Rivers
• Extreme Storms (Meteotsunamis)
• +3 more

Eric Anderson is an Associate Professor in Civil & Environmental Engineering and the Hydrologic Science and Engineering Program at Colorado School of Mines. His expertise centers on hydrosphere-cryosphere-atmosphere interactions, numerical modeling of large water bodies, and extreme weather phenomena like meteotsunamis. Previously at NOAA GLERL, he developed operational forecast systems for lakes/rivers supporting navigation and environmental management. Education: PhD, Mechanical and Aerospace Engineering, Case Western Reserve University (2007) BS, Mechanical Engineering, Case Western Reserve University (2004) Research Interests: Anderson focuses on: Lake ice forecasting and climate impacts Meteotsunami dynamics and coastal flooding Water quality modeling (e.g., cyanotoxins in Lake Erie) Operational hydrologic forecast systems Ecosystem responses to hydrologic extremes Recent work emphasizes cross-scale predictions, probabilistic risk assessment, and stakeholder engagement in forecast system development. Awards: Presidential Early Career Award for Scientists and Engineers (2019) National Academy of Sciences Kavli Fellow (2020) Multiple U.S. Department of Commerce Bronze Medals for forecast system innovations Advising & Labs: Leads the Anderson Research Group, focusing on collaborative projects with NOAA and other agencies. Active in developing forecast systems for coastal and freshwater environments. No explicitly listed advisees/students are noted in available materials. Labs/Teams: Primary affiliation with Anderson Research Group at Colorado School of Mines, collaborating with Great Lakes Environmental Research Laboratory (NOAA) and other operational partners.

Yang ChenView profile

Assistant Professor

• Statistical Methodology
• Computational Algorithms
• Bayesian Modeling
• +7 more

Yang Chen is an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute for Data Science (MIDAS), University of Michigan. Her research focuses on statistical methodology and computational algorithms for scientific applications, including Bayesian modeling, hidden Markov models, and applications in astronomy, space science, and epidemiology. She holds a Ph.D. in Statistics from Harvard University and a B.Sc. in Mathematics from the University of Science and Technology of China. Education: Ph.D. in Statistics, Harvard University, 2017 B.Sc. in Mathematics, University of Science and Technology of China, 2011 Research Interests: Her work emphasizes computational algorithms for statistical inference, with applications in astronomy (e.g., solar flare prediction, instrument calibration) and epidemiology. Key areas include spatial-temporal data analysis, uncertainty quantification, and machine learning for large-scale imaging data. Grants & Funding: Principal Investigator/Co-Investigator on multiple NSF, NASA, and industry grants totaling over $13M, including space weather modeling, solar physics, and data science initiatives. Teaching: Teaches advanced courses in Bayesian data analysis, statistical modeling, and computational methods at the University of Michigan. Professional Activities: Associate Editor for The American Statistician , Statistical Science , and Journal of the American Statistical Association . Active in organizing conferences and seminars on astrostatistics, machine learning, and space weather.