جست‌وجوی استاد و پژوهشگر

Eric Andersonمشاهده پروفایل

دانشیار

• Hydrosphere-Cryosphere-Atmosphere Interactions
• Numerical Modeling of Lakes/Rivers
• Extreme Storms (Meteotsunamis)
• +۳ مورد دیگر

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 Chenمشاهده پروفایل

استادیار

• Statistical Methodology
• Computational Algorithms
• Bayesian Modeling
• +۷ مورد دیگر

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.

Randal Barnesمشاهده پروفایل

دانشیار

• Geostatistical Site Characterization
• Uncertainty Quantification
• Analytic Element Method
• +۵ مورد دیگر

Randal Barnes serves as an Associate Professor and Director of Undergraduate Studies for Civil Engineering and Geoengineering at the University of Minnesota's Department of Civil, Environmental, and Geo-Engineering. He holds the distinguished title of Distinguished University Teaching Professor, recognizing his exceptional contributions to education. His research traverses mathematical modeling in geological and civil engineering with three primary foci: geostatistical site characterization (optimal sample design and engineering decision-making under spatial variability and parameter uncertainty), incorporation of uncertainty into quantitative modeling for geoengineering, and computational aspects of the Analytic Element Method. His work spans civil infrastructure, geotechnical applications, and environmental engineering, with particular emphasis on uncertainty quantification in engineering systems. Barnes' recent publications demonstrate a strong trend toward integrating machine learning techniques with traditional engineering modeling, particularly in uncertainty quantification for both civil infrastructure and environmental applications. His work bridges civil engineering with data science approaches, showing increasing focus on neural network applications for engineering problems. Distinguished University Teaching Professor Barnes has served as Principal Investigator and Co-Investigator on significant transportation research projects funded by the Minnesota Department of Transportation, including the MnROAD Data Mining project and PCC Pavement Thickness Variation study. His research has garnered substantial academic attention with multiple publications receiving double-digit Scopus citations. His work contributes to UN Sustainable Development Goals related to sustainable infrastructure development and environmental protection through improved engineering modeling and decision-making under uncertainty.

• Probabilistic Forecasting
• Statistical Evaluation
• Climate Science
• +۳ مورد دیگر

Prof. Tilmann Gneiting is a Professor of Computational Statistics at Karlsruhe Institute of Technology (KIT) and Head of the Computational Statistics Working Group at the Heidelberg Institute for Theoretical Studies (HITS). His research focuses on probabilistic forecasting, statistical evaluation of predictive models, and applications in environmental science, epidemiology, and renewable energy. He has held academic roles in mathematics and statistics departments across multiple institutions. His work emphasizes rigorous statistical methodology for forecasting systems, including ensemble models, uncertainty quantification, and validation frameworks. Notable contributions include developing scoring rules for probabilistic predictions, Bayesian model averaging techniques, and geostatistical methods for weather and climate modeling. Courses taught include Forecasting: Theory and Practice and Time Series Analysis . Prof. Gneiting’s research spans diverse areas such as earthquake forecasting, solar energy prediction, and pandemic modeling. He has contributed to R packages like ensembleMOS and ProbForecastGOP , advancing computational tools for statistical analysis. His work bridges theoretical statistics with practical applications in environmental and public health domains. Professional awards and recognitions are not explicitly listed in the provided texts. His advising and grants involve collaborations in interdisciplinary projects, though specific student names or grant details are not mentioned. He leads computational statistics teams at HITS and actively publishes in top-tier statistical and applied journals.

William Kleiberمشاهده پروفایل

دانشیار

• Spatial Statistics
• Geostatistics
• Uncertainty Quantification
• +۵ مورد دیگر

William Kleiber is an Associate Professor in the Department of Applied Mathematics at the University of Colorado Boulder, specializing in spatial statistics for geophysical applications and renewable energy systems. His work bridges advanced statistical theory with practical environmental challenges, particularly in climate modeling and uncertainty quantification. Education: Ph.D. in Statistics, University of Washington (2010) Dr. Kleiber's research centers on spatial statistics and geostatistics for large-scale nonstationary datasets, with significant contributions to statistical climatology , stochastic weather generators , and uncertainty quantification in geophysical models. His methodologies address critical energy science challenges, including solar irradiance forecasting and renewable resource assessment. Recent work integrates machine learning with spatial statistics to handle massive multivariate environmental data, advancing predictive capabilities for climate extremes and renewable energy systems. His 2023-2025 publications reveal a strong trend toward hybrid statistical-machine learning approaches for environmental data, emphasizing uncertainty-aware predictions in snow hydrology, sea surface temperature reconstruction, and extreme weather events. This work consistently tackles the computational and theoretical challenges of high-dimensional spatial processes. Scientific Awards: American Statistical Association's Section on Statistics and the Environment 2016 Young Investigator Award Dr. Kleiber has served on the Board of Directors for The International Environmetrics Society and the American Statistical Association's Section on Statistics and the Environment, and contributed to editorial boards of leading statistical journals. His research has been supported by collaborative grants including 'Collaborative Research: Theory and Methods for Highly Multivariate Spatial Processes with Applications to Climate Data Science' (2018) and 'Scalable Statistical Validation for Large Spatio-Temporal Datasets' (2014). Current projects focus on integrating deep learning with spatial statistics for renewable energy forecasting and climate risk assessment. He maintains active international collaborations, evidenced by his 2016 Lebesgue Chair visiting professorship at University of Rennes 1, France, and partnerships with the National Center for Atmospheric Research where he previously conducted postdoctoral research.

Daniel Göhringمشاهده پروفایل

استاد

• Autonomous Vehicles
• Mobile Robotics
• Computer Vision
• +۶ مورد دیگر

Prof. Daniel Göhring is a professor in the Department of Computer Science at the Free University of Berlin, leading the Autonomous Cars Lab and part of the Dahlem Center for Machine Learning and Robotics. His research emphasizes robotic perception, object tracking, and real-time planning under computational constraints, with a focus on autonomous vehicles and cooperative systems. Education: Bachelor's/Master's in Robotics (exact program unspecified) PhD in Computer Science at Humboldt University Berlin Postdoctoral Research at International Computer Science Institute (ICSI), Berkeley, CA Research Interests: Daniel's work integrates machine learning and sensor technologies like LiDAR and cameras to address challenges in autonomous driving. Key areas include SLAM algorithms, trajectory prediction, cooperative perception, and real-time systems. He explores how limited sensor data and computational resources can be optimized for dynamic traffic environments. Grants and Projects: Leader of the Autonomous Cars Lab Involved in EU-funded projects such as H2020 HIVEOPOLIS and KIS-M (AI-based mobility systems) Past projects include CRTX (recycling optimization), Open.Make (open hardware), RoboFish (biological swarm analysis), and SAFARI Awards: Best Poster Award at IAAS Workshop 2024 Best Paper Award at ICAIR-CACRE 2019 Teaching: He has taught courses such as Image Processing, Robotics, and Advanced Robotics. Recent semesters include modules on self-supervised learning, autonomous vehicle research, and continuous learning software projects. Labs and Teams: Daniel heads the Autonomous Cars Lab and collaborates with the BioRobotics Lab, focusing on interdisciplinary projects like 'Robots Communicating with Fish' and 'Open Hardware for FAIR Robotics.'

Naresh Devineniمشاهده پروفایل

استاد

• Hydrology
• Machine Learning
• Probability and Statistics
• +۴ مورد دیگر

Naresh Devineni is a Professor in the Department of Civil Engineering at the City College of New York (CUNY), with affiliations to the CUNY-CREST Institute, Earth System Science and Environmental Engineering, and NOAA-CESSRST. His research focuses on hydrology, machine learning, and risk management, particularly in water resource management and climate change impacts. He advises PhD students like Arun Ravindranath (crop water stress modeling) and Nasser Najibi (global flood trends analysis). His work integrates statistical modeling, environmental data analysis, and interdisciplinary collaboration to address challenges in flood risk, water scarcity, and climate adaptation. Recent studies include forecasting monsoon-induced agricultural risks in India and analyzing global flood frequency trends using satellite data. Research highlights include developing the Cumulative Deficit Index (CDI) for crop water stress and analyzing flood duration changes via Earth System Dynamics. He collaborates with institutions like the Dartmouth Flood Observatory and Harvard Dataverse for data-driven insights. His research emphasizes probabilistic forecasting, uncertainty quantification, and actionable policy frameworks for sustainable water management. Key areas of contribution include: Climate-informed seasonal forecasts for agriculture Urban flood resilience in NYC post-Hurricane Ida Global flood pattern analysis using historical datasets Machine learning applications for risk assessment Publications span journals like Hydrology and Earth System Sciences and Earth System Dynamics, with a focus on bridging theoretical models and real-world decision-making.

Michele Albanoمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Energy Engineering
• Quality of Service (QoS)
• +۵ مورد دیگر

Affiliations & Roles Michele Albano is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design, focusing on research in IoT, Cyber-Physical Systems, and Edge Computing. He leads the Productive4.0 project (2017–2020), funded by Horizon Europe, addressing Industry 4.0 challenges in product lifecycle management. His work integrates formal verification tools like Uppaal with real-world applications in robotics, energy systems, and blockchain-based platforms. Research Interests Albano's research spans IoT architecture optimization , energy-efficient systems , and model-driven engineering . He develops tools for autonomous exploration algorithms (MAES), edge-cloud resource orchestration, and fault-tolerant computation offloading. His work bridges theoretical models (e.g., Uppaal SMC) with practical implementations in smart grids and robotic systems. Recent projects include blockchain-based crowdsourcing for machine learning and energy-aware thermal dynamics estimation in buildings. Collaborations & Impact He collaborates with the European Industry 6.0 community, contributing to the Arrowhead Framework for interoperable IoT systems. His research outputs include 112 publications, with 2025 highlights in human-inspired robotics and cognitive cloud frameworks. Media coverage in 2024–2025 highlights his work on green IT and secure API generation. Albano advises students on system modeling (e.g., ACSmt plugin development) and edge computing optimization. Labs & Teams His research group focuses on Cyber-Physical Systems and Smart Grids , with contributions to tools like RoutesMobilityModel and FlexHousing. He actively participates in workshops on New Trends in Software Architecture (SATrends '24) and IEEE conferences on Industrial Informatics.

Shaun Bloomfieldمشاهده پروفایل

دانشیار

• Astrophysics
• Solar Physics
• Space Weather
• +۵ مورد دیگر

Shaun Bloomfield is an Associate Professor at Northumbria University's Mathematics, Physics and Electrical Engineering Department. His research focuses on solar physics, particularly solar flare forecasting, space weather prediction, and the application of machine learning in heliophysics. He holds a PhD and MSc in Astrophysics from Queen's University Belfast (2005 and 2001, respectively). Education: PhD in Astrophysics, Queen's University Belfast (2001-2005) MSc in Astrophysics, Queen's University Belfast (1998-2001) Research Interests: Space weather and solar flare forecasting Machine learning applications in solar physics Coronal mass ejections and solar eruptions EUV imaging and instrumentation Magnetic field dynamics in active regions Recent Articles: Bloomfield's work emphasizes forecasting solar energetic events, validating EUV wave simulations, and developing ensemble forecasting techniques. His contributions include advancements in flare prediction algorithms and EUV filter design for the SWAP telescope. Professional Activities: Member of the International Astronomical Union (IAU) since 2021 Editor of the Journal of Space Weather and Space Climate since 2017 Past member of the European Solar Physics Division (2014-2021) Labs/Teams: Collaborates on projects like the FLARECAST initiative, focusing on big data and machine learning for flare forecasting.

Benjamin Trabingمشاهده پروفایل

پژوهشگر

• tropical meteorology
• tropical cyclones
• lightning applications
• +۴ مورد دیگر

Benjamin Trabing serves as an Associate Scientist at the University Corporation for Atmospheric Research (UCAR) working remotely with the National Hurricane Center (NHC), focusing on advancing tropical cyclone forecasting capabilities and hazard communication strategies. His research directly impacts operational forecasting practices at one of the world's leading hurricane prediction centers. His academic foundation includes: B.S. in Meteorology from the University of Oklahoma M.S. and Ph.D. in Atmospheric Science from Colorado State University Dr. Trabing's research program centers on critical challenges in tropical cyclone prediction with particular emphasis on: Improving intensity and structure forecasting through advanced modeling techniques Developing and validating probabilistic forecast products for public communication Applying lightning observations to tropical cyclone monitoring Investigating radiation effects on tropical cyclone dynamics Enhancing understanding of diurnal cycles in tropical systems Translating research findings into operational forecasting improvements Analysis of his publication record reveals a strategic research trajectory that increasingly bridges meteorological science with social science considerations in risk communication. His recent work demonstrates growing emphasis on probabilistic forecasting methods and effective communication of forecast uncertainty to diverse stakeholders. Trabing's research consistently addresses operational forecasting challenges identified by the National Hurricane Center, ensuring practical applications for improving public safety during tropical cyclone events. Through his GitLab contributions and collaborative projects like the Landfall-Distribution-Product (LDP) and Intensity Change Bias and Uncertainty Scheme (IBUS), Dr. Trabing actively develops tools that directly inform operational forecasting practices at the National Hurricane Center, strengthening the research-to-operations pipeline in tropical meteorology.

Suelynn Choyمشاهده پروفایل

استاد

• Satellite Navigation
• Precise Positioning
• Geodesy
• +۵ مورد دیگر

Professor Suelynn Choy is a Professor of Satellite Navigation and Director of the RMIT SPACE Research Centre at RMIT University. Her roles include Deputy Dean, Industry, International & Engagement and Interim Head of Department in the School of Science. She holds a PhD in precise Global Navigation Satellite Systems (GNSS) from RMIT and specializes in satellite positioning, atmospheric remote sensing, and disaster management. Her research focuses on GNSS applications, satellite remote sensing for climate studies, and geospatial technologies for disaster resilience. Education: PhD in precise GNSS (RMIT University). Research Interests: Precise satellite positioning, GNSS remote sensing of the atmosphere, disaster risk management, and climate studies. Key areas include GNSS-based weather monitoring, multi-hazard early warning systems, and blending satellite and ground-based data for improved environmental analysis. Awards: Recipient of the 2016 Australian National Measurement Institute Prize, 2018 RMIT School of Science Leadership Award, and others recognizing her contributions to satellite navigation and climate research. Advising & Grants: Leads projects on drought monitoring, tropical cyclone risk assessment, and smartphone positioning. Collaborates with national/international committees on satellite navigation and disaster resilience. Supervises PhD and Master's students in geospatial sciences and related fields. Labs & Teams: Director of RMIT SPACE Research Centre, affiliated with the RMIT Sir Lawrence Wackett Centre and Urban Futures initiatives.

• Machine Learning
• Computational Data Analysis
• Neural Networks
• +۳ مورد دیگر

Yogesh Verma is a doctoral researcher at Aalto University's Department of Computer Science, specializing in machine learning and computational data analysis. His work bridges theoretical advancements with practical applications in molecular generation, climate forecasting, and topological modeling. Areas of expertise: Computer and information sciences, Computational data analysis Research interests: Focus on physics-informed neural ODEs for climate modeling, graph generation with diffusion models, topological neural networks, and ab initio antibody design. His interdisciplinary approach connects machine learning with computational biology and scientific computing. Scientific awards: Nokia Scholarship for doctoral studies in ICT-related fields Key publications appear in leading venues like ICLR and NeurIPS, spanning topics from molecular design to climate forecasting. Collaborators include Vikas Garg, Markus Heinonen, and Giangiacomo Mercatali.

• Coastal Engineering
• Coastal Hazards
• Numerical Modeling
• +۳ مورد دیگر

Matthew Janssen is a Research Assistant Professor at the Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology. His research focuses on coastal hazards, littoral processes, and developing computationally efficient models to assess risks to coastal infrastructure using field observations, numerical modeling, and data-driven techniques. He holds a PhD (2022), MS (2016), and BS (2011) in Ocean Engineering from Stevens Institute of Technology and the University of Rhode Island, respectively. His work emphasizes understanding storm erosion potential, dune performance under climate change scenarios, and the impact of coastal structures. Notable contributions include methodologies for quantifying storm erosion considering sea level rise and probabilistic forecasting of coastal storm impacts. He currently serves as Assistant Director of the NJ Coastal Protection Technical Assistance Service and has prior industry experience with firms like Rising Tide Waterfront Solutions and McLaren Engineering Group. Key Research Areas: Coastal resilience, numerical modeling, climate adaptation, dune dynamics, sediment transport. Recent Focus: Long-term dune performance under extreme and nuisance erosion events; integration of machine learning (CART models) for erosion prediction. Publications highlight his work on hurricane impacts, breakwater effectiveness, and navigation channel management. He received the John P. Breslin Award (2022) and is active in professional societies like ASBPA and COPRI. His technical reports include analyses of New Jersey beach sediment characteristics and shoreline impacts at North Wildwood. He collaborates on projects balancing engineering solutions with ecological and economic considerations.

• Atmospheric Physics
• Atmospheric Chemistry
• Extreme Weather Events Prediction
• +۵ مورد دیگر

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.

• Multivariate time series analysis
• Statistical analysis and modelling of energy commodities
• Time series forecasting
• +۳ مورد دیگر

Professor Dr. Stephan Schlüter leads the Research Group for Applied Mathematics for Energy Markets at THU (Technische Hochschule Ulm). His expertise spans multivariate time series analysis, statistical modelling of energy commodities, and forecasting renewable energy systems. Co-Editor, Spatial Information Research Teaching: Advanced Mathematics, Operations Research, Stochastics, Applied Time Series Analysis Research focuses on: Electricity and gas price forecasting using neural networks and Bayesian models Climate anomaly detection via satellite data and AI Energy market optimization with battery storage and hydrogen integration Environmental statistics linking CO2 fluxes with economic indicators Recent publications emphasize regime-aware forecasting, uncertainty quantification, and interdisciplinary climate-energy policy analysis. Supervising PhD candidates Abhinav Das and Erick Michel Lara Pinal, plus Master's students Martin Neumann and Andreas Lebedev.