جست‌وجوی استاد و پژوهشگر

Michael Shayمشاهده پروفایل

استاد

• Plasma Physics
• Space Physics
• Magnetic Reconnection
• +۴ مورد دیگر

Michael Shay is a Professor of Physics and Astronomy at the University of Delaware, affiliated with the College of Arts & Sciences. He holds a B.A. in Physics from Grinnell College (1992) and a Ph.D. in Physics from the University of Maryland, College Park. His research focuses on plasma physics, particularly magnetic reconnection and turbulence, with applications to solar flares, space weather, and astrophysical phenomena. He has pioneered studies on multiscale interactions between reconnection and turbulence, contributing to NASA’s Magnetospheric Multiscale (MMS) mission and Parker Solar Probe data analysis. Shay’s work emphasizes collisionless plasma dynamics, leveraging analytical theory and computational simulations. He was named an American Geophysical Union Fellow (2023) and a Fellow of the American Physical Society (2015) for breakthroughs in magnetic reconnection. His collaborations include investigations of the Earth’s magnetosphere, solar wind turbulence, and plasma heating mechanisms. He also serves as Associate Director of the Delaware NASA Space Grant Consortium, actively mentoring students and promoting outreach. Scientific awards include the NSF Early Career Award (2007), Francis Alison Society Young Scholar Award (2008), and Excellence in Scholarship Award (2011). His research bridges theoretical, computational, and observational approaches, addressing critical questions in space weather prediction and fundamental plasma physics.

Hermann F. Faselمشاهده پروفایل

استاد

• Aerospace Engineering
• Fluid Dynamics
• Hypersonic Flow
• +۷ مورد دیگر

Hermann F. Fasel is a Professor in the Department of Aerospace and Mechanical Engineering at the University of Arizona and a member of the Graduate Faculty. His research is centered on high-speed aerodynamics, boundary-layer transition, and computational fluid dynamics, utilizing advanced simulation techniques such as direct numerical simulation (DNS) and linear stability analysis. Research Interests: Fasel's work spans fundamental and applied aspects of fluid dynamics, particularly in hypersonic and transonic regimes. His expertise includes boundary-layer instability, shock-wave interactions, active flow control, and transition prediction using machine learning. He investigates complex flow phenomena over various geometries such as flat plates, cones, swept wings, and hollow cylinders. The recent publications highlight a consistent focus on high-speed boundary-layer transition, with increasing integration of data-driven methods like the Mori-Zwanzig formalism for modal decomposition. His research bridges theoretical analysis, numerical simulation, and experimental validation, often in collaboration with teams from other institutions. Scientific Awards: AIAA Fellow (2021) AIAA Fluid Dynamics Award (2019) Ludwig Prandtl Ring (2018) Advising and Grants: Fasel has advised numerous graduate students and researchers, including C. Hader, A. Haas, and S. Hosseinverdi. His research is supported by major funding agencies such as the Office of Naval Research (ONR), Air Force Office of Scientific Research (AFOSR), and Army Research Office (ARO), as evidenced by his regular participation in their program reviews and workshops. Labs and Teams: He leads a research group focused on high-fidelity simulations of compressible flows, collaborating with experimentalists and theorists. His team develops and applies advanced CFD solvers and participates in national and international forums such as AIAA, IUTAM, and ERCOFTAC.

Ignasi Josep Soler Poquetمشاهده پروفایل

پژوهشگر ارشد

• Solar Physics
• Machine Learning/Deep Learning
• Observational Astronomy
• +۲ مورد دیگر

Ignasi Josep Soler Poquet is a Doctoral Research Fellow at the Rosseland Centre for Solar Physics, University of Oslo. His research focuses on solar physics and computational science, with a strong emphasis on applying machine learning techniques to analyze solar observations. **Education**: Bachelor's in Physics from University of Valencia (specializing in astrophysics/theoretical physics) Master's in Advanced Physics (University of Valencia, focusing on supernovae and simulations) **Research Interests**: Development of deep learning models for detecting solar phenomena like Ellerman bombs Integration of Python-based tools for observational astronomy Computational analysis of solar dynamics using instruments like SST and SDO **Publications Trends**: His recent work demonstrates a clear trajectory in leveraging neural networks and deep learning to interpret solar observational data, with a focus on automated detection systems for transient solar events. **Advising & Grants**: No specific grants or advisees listed, though his PhD work is institutionally supported through the Rosseland Centre.

• Space Physics
• Space Science
• Space Weather
• +۳ مورد دیگر

Evangelos Paouris is a Postdoctoral Research Fellow at the Physics & Astronomy Department of George Mason University. His research focuses on space physics, space science, and space weather, particularly analyzing coronal mass ejections (CMEs), their interplanetary counterparts (ICMEs), and their effects on geomagnetic storms and solar radiation storms. He specializes in forecasting space weather conditions using CME kinematics and solar wind dynamics, and investigates cosmic ray modulation using solar and interplanetary parameters. Education: PhD in Space Weather from the National and Kapodistrian University of Athens (2017), MSc in Astrophysics (2013). Research interests include CME propagation modeling, SEP forecasting, heliospheric imaging via Parker Solar Probe and Solar Orbiter data, and integrating machine learning for solar event prediction. His work addresses challenges in space weather prediction accuracy, CME kinematics, and the impact of solar eruptions on Earth's magnetosphere. Recent contributions focus on refining CME arrival time predictions using heliospheric imaging, comparing forecasting models like ENLIL and DBEM, and developing ML-driven frameworks for SEP occurrence probability. He also collaborates on ESA's SEP Advanced Warning System (ASPECS) and the Cosmic Ray Modulation studies via neutron monitor data.

• Solar Physics
• Space Weather
• Solar Flares
• +۳ مورد دیگر

Gelu Nita is a Research Professor at NJIT's Center for Solar-Terrestrial Research with extensive expertise in solar physics and space weather. He maintains an h-index of 24 with over 1974 citations and leads multiple NSF-funded projects including SUNCAST and ASCENT. Research develops data-constrained 3D solar flare models using microwave imaging spectroscopy through the Expanded Owens Valley Solar Array. Recent work includes energy budget analysis of flares, solar energetic particle prediction using machine learning, and automated active region modeling. Publications demonstrate innovations in solar radio astronomy instrumentation, flare dynamics modeling, and space weather forecasting. Current projects focus on mapping coronal magnetic fields and developing multiscale digital twins for resilient power grids.

• Solar Physics
• Space Weather
• Solar Eruptions
• +۳ مورد دیگر

Ju Jing is a Research Professor at the Center for Solar-Terrestrial Research, New Jersey Institute of Technology (NJIT). Their research focuses on solar dynamics, space weather, and machine learning applications in astrophysics. Jing leads multiple NSF-funded projects aimed at predicting solar eruptions, geomagnetic indices, and interplanetary magnetic fields using advanced AI techniques. Recent work emphasizes high-resolution observations of solar flares and coronal mass ejections (CMEs) using instruments like the 1.6-meter Big Bear Telescope and SDO/HMI. Research Interests : Solar active regions, magnetic reconnection processes, CME initiation mechanisms, and AI-driven space weather forecasting. Collaborations span institutions like NJIT, BBSO, and international observatories. Grants & Projects : Active NSF grants include SHINE (2023–2026), ANSWERS (2022–2026), and high-resolution solar eruption studies (2023–2028). Projects address magneto-thermal coupling, geomagnetic storm prediction, and non-force-free magnetic fields in active regions. Labs/Teams : Co-leads the Solar Eruptions and Machine Learning (SEML) group at NJIT’s Center for Solar-Terrestrial Research, focusing on interdisciplinary approaches to heliophysics.

• Solar Physics
• Space Weather
• Magnetic Fields
• +۶ مورد دیگر

Haimin Wang is a Professor in the Department of Physics at New Jersey Institute of Technology (NJIT). He holds dual roles as Director of the Institute of Space Weather Sciences and Distinguished Professor at the Ctr for Solar-Terrestrial Research. His research focuses on solar dynamics, space weather, and solar magnetic field studies. He leads multiple NSF-funded projects exploring solar eruptions, magnetic flux ropes, and AI-driven space weather forecasting. Roles: Director (Institute of Space Weather Sciences), Distinguished Professor (Physics) Key Affiliations: NJIT, Big Bear Solar Observatory (BBSO), Daniel K. Inouye Solar Telescope (DKIST) Notable Grants: 33 federal grants since 2001, including NSF-funded projects on AI for space weather and high-resolution solar observations Research Interests: Solar flares, coronal mass ejections, magnetic flux ropes, solar wind dynamics, and machine learning applications in heliophysics. His work combines observational data from instruments like SDO/HMI and GST with advanced computational models to understand explosive solar phenomena. Recent studies include predicting halo CMEs using transformer models and analyzing small-scale magnetic structures in the solar wind. Collaborations: International partnerships in solar physics, including joint projects with researchers in magnetic field analysis, solar eruption dynamics, and space environment forecasting. Key Projects: CyberTraining AI initiative, DKIST Critical Science Program, SHINE solar flare studies Labs/Teams: Institute of Space Weather Sciences, NJIT Space Weather Lab

• Machine Learning
• Space Weather
• Computer Science
• +۳ مورد دیگر

Jason Wang is a Professor in the Department of Computer Science at the New Jersey Institute of Technology (NJIT). His research focuses on applying machine learning and AI to space weather phenomena, including solar eruptions, coronal mass ejections, and magnetic flux ropes in the solar wind. He leads federal grants funded by the National Science Foundation (NSF), such as the AI-driven solar eruption forecasting project (CAIG) and the CyberTraining initiative for machine learning in space weather forecasting. Research Interests: Machine Learning for Astrophysics, Space Weather Prediction, Solar Physics, Data Mining, and AI-driven Modeling. Key projects include developing interpretable transformer models for flare forecasting and improving solar magnetogram analysis using deep generative models. Collaborations include work with international teams on solar telescope data and space weather applications. His recent work emphasizes operational forecasting systems and automated detection methods for solar phenomena.

• Machine Learning
• Optimization Metaheuristics
• Cloud Computing
• +۹ مورد دیگر

Ivana Štrumberger is a Researcher at the Faculty of Informatics and Computing at Singidunum University in Belgrade, Serbia. Currently active in academia, she focuses on applying optimization metaheuristics to machine learning problems across diverse domains. Academic affiliation: Faculty of Informatics and Computing, Singidunum University Email: istrumberger@singidunum.ac.rs Research Focus Her work spans machine learning optimization, neural network tuning, and cloud computing applications. Key research areas include: Metaheuristic algorithms for deep learning (LSTM, GRU, CNNs) Feature selection and dimensionality reduction Cybersecurity applications (phishing detection, network intrusion) Financial forecasting (cryptocurrencies, oil prices) Environmental modeling (PAHs pollution, solar power prediction) Publication Trends Recent publications analyze: Hybrid models combining metaheuristics with XGBoost and ELM Applications of sine-cosine algorithm and Harris hawks optimization Time series prediction in energy and financial domains Deep learning for pandemic-related tasks (Covid-19 detection) Cloud computing resource scheduling solutions

Huw Morganمشاهده پروفایل

استاد

• Solar Physics
• Space Weather
• Solar Wind
• +۷ مورد دیگر

Huw Morgan is a Professor in the Department of Physics at Aberystwyth University, actively engaged in solar and space physics research. He leads multiple externally funded projects, including CorMag, MESOM, and SWEEP, and collaborates with institutions such as the UK Space Agency, STFC, and international partners. His work spans observational, theoretical, and instrumental aspects of solar physics. PhD in Physics (2005), thesis on solar wind acceleration using UV spectroscopy Principal Investigator on 11 externally funded research projects from 2015–2028 Active contributor to space missions and instrumentation development His research focuses on understanding the solar corona, solar wind origins, coronal mass ejections, and space weather prediction. He develops empirical and tomographic models to analyze solar wind properties and uses data from missions like Parker Solar Probe and IRIS. His work also extends to solar instrumentation, including high-resolution imaging spectrometers and lunar-based solar occultation missions (MESOM). He investigates magnetic field structures in active regions and quiet Sun phenomena, contributing to fundamental understanding of solar dynamics. The recent publications (2023–2025) reflect a strong trend in combining multi-instrument data with modeling to improve space weather forecasting. Topics include CME projection corrections, empirical solar wind models, coronal tomography, and high-resolution chromospheric studies. His work increasingly integrates automated analysis techniques and large statistical datasets. He supervises multiple PhD students and has mentored researchers in solar physics. His collaborative network includes experts in solar physics, space instrumentation, and data science. He is frequently cited in media for expert commentary on auroras and solar storms. Principal Investigator: CorMag (2025–2028), MESOM (2024–2025), SWEEP (2020–2023) Projects funded by STFC, Leverhulme Trust, UK Space Agency, Welsh Government Active grants support solar wind modeling, coronal imaging, and space weather forecasting He leads or co-leads several research groups and labs focused on solar system physics at Aberystwyth, including teams developing the EMPSOL model and the MESOM mission. His work bridges observational solar physics with practical space weather applications.

• Geomatic Engineering
• Physical Geography
• Environmental Management
• +۶ مورد دیگر

Philip Dennison is a Professor at the School of Environment, Society & Sustainability , University of Utah, with a career spanning two decades in Geography . He holds degrees from Penn State University (BS) and University of California Santa Barbara (MA, PhD). Research focus on remote sensing, ecological modeling, and wildfire management Teaching includes courses like Intro to Remote Sensing and thesis supervision His work bridges geoinformatics and atmospheric sciences , addressing challenges in non-photosynthetic vegetation monitoring , firefighter safety zone design , and global greenhouse gas quantification . Recent studies explore airborne and spaceborne lidar for biomass estimation in piñon-juniper woodlands and crowdsourced GPS data for pedestrian mobility modeling. Collaborative efforts include NASA's EMIT mission for methane attribution and FIREX-AQ thunderstorm measurements. His machine learning frameworks (random forests, XGBoost) enhance visibility mapping and wildfire risk prediction.

• Machine Learning
• Optimization Algorithms
• Cloud Computing
• +۴ مورد دیگر

Ivana Štrumberger is a researcher at Singidunum University's Faculty of Informatics and Computing, with a focus on machine learning optimization, cloud computing, and wireless sensor networks. Her work bridges computational intelligence with practical applications in healthcare, cybersecurity, and financial forecasting. Her research emphasizes hybrid metaheuristic algorithms for tuning neural networks and ensemble models. Notably, she has developed methods for Bitcoin and Ethereum price prediction using LSTM networks Phishing website detection via XGBoost and feature selection Solar flare forecasting with gradient boosting Optimization of wireless sensor network localization She has co-authored over 30 peer-reviewed publications in journals like Artificial Intelligence Review and Applied Sciences , and presented at IEEE conferences including IJCNN and CEC. Her methodological contributions include hybridized variants of Firefly Algorithm Harris Hawks Optimization Sine Cosine Algorithm Whale Optimization for constrained and multi-objective problems.

• Helioseismology
• Solar Flares
• Magnetic Fields
• +۶ مورد دیگر

Alina Donea is a Senior Lecturer in Astrophysics and Applied Mathematics at Monash University's School of Mathematics, specializing in helioseismology and solar physics. She holds a PhD in Mathematics and Astronomy and has held academic and research positions globally, including postdoctoral roles at the Max Planck Institute and visiting appointments at institutions like the High Altitude Observatory (USA). She is a Partner-Investigator on major NASA projects and an Associate Editor for Frontiers Astronomy and Space Sciences . Her research focuses on seismic emissions from solar flares, magnetic field dynamics, and space weather forecasting using computational methods. Education: BSc and Diploma in Physics (University of Bucharest), PhD in Mathematics and Astronomy (Romanian Academy/Max Planck), Graduate Certificate in Higher Education (Monash). Research Interests: Solar quakes, acoustic haloes, magnetic solar flares, helioseismic holography, and machine learning applications in solar imaging. Her work includes over 60 refereed publications, with highly cited papers on seismic emission from solar flares and sunquakes. She actively collaborates with international teams and has secured research grants totaling over $250,000. Current projects involve forecasting active region emergence and developing algorithms for far-side solar region detection using seismic data. Leadership roles include Honours Astrophysics Coordinator at Monash and membership in the International Astronomical Union. Her lab focuses on advancing helioseismic techniques and understanding solar dynamics' impact on space weather.

Kyle E. Fitchمشاهده پروفایل

استادیار

• Arctic Cloud Microphysics
• Riming Processes
• Space Weather
• +۳ مورد دیگر

Kyle E. Fitch is an Assistant Professor of Atmospheric Science at the Air Force Institute of Technology (AFIT) within the Department of Engineering Physics. He serves as the Atmospheric Science M.S. Curriculum Chair, directing coursework for 10 students and teaching 3-4 courses annually, including programming and physics refresher modules for new students. PhD in Atmospheric Sciences, University of Utah (2020) MS in Applied Physics, AFIT (2016) BS in Atmospheric Sciences, University of Washington (2006) His research focuses on Arctic cloud microphysics , including riming processes, graupel formation in thin clouds, and aerosol impacts on precipitation. He integrates space weather studies (e.g., ionospheric electron density, solar flares) and machine learning for predictive modeling. His work addresses Department of Defense weather priorities through funded research. Recent publications highlight collaborations on attention-based neural networks for lightning prediction, GNSS radio occultation for ionospheric modeling, and WRF-LES simulations of Arctic clouds. He presented at the 105th AMS Annual Meeting and QuIESCENT Arctic Workshop . Graduate School Team of the Year (2024) Dean’s Distinguished Teaching Professor (2022/2023) Advanced Technical Intelligence Award (2016) Distinguished Graduate, AFIT (2016) As a Weather and Environmental Sciences Officer in the Air Force, he has deployed to Afghanistan and worked with the National Air and Space Intelligence Center. His technical expertise includes VIIRS cloud base height validation , Multi-Angle Snowflake Camera analysis, and atmospheric model development .

Matthew Cottonمشاهده پروفایل

استاد

• Environmental Justice
• Sustainable Development
• Climate Change Policy
• +۷ مورد دیگر

Matthew Cotton is Professor of Environmental Justice and Public Policy at Teesside University, serving as Programme Director for Sustainable and Resilient Places within the Institute for Collective Place Leadership. Previously, he held academic positions at the Universities of York, Sheffield, Leeds, Exeter, and Manchester, focusing on socio-political dimensions of sustainable development across 15+ countries including the UK, Bangladesh, Mozambique, and Iran. His educational background includes: PhD in Environmental Science (University of East Anglia, awarded 2009) MSc in Social Research Methods (Teesside University, awarded 2004) BA (Hons) International Studies with Political Science (University of Birmingham, awarded 2003) Cotton's research centers on environmental justice frameworks applied to energy transitions, climate adaptation, and food security. He employs mixed methods including Q-methodology, participatory workshops, and critical policy analysis to examine stakeholder engagement in nuclear waste management, shale gas governance, and hydrogen economies. His work emphasizes ethical dimensions of technology assessment and place-based just transitions. Recent publications (2024-2025) reveal three dominant trends: (1) sociotechnical analysis of hydrogen economies in post-industrial communities, (2) climate adaptation strategies in vulnerable regions (Bangladesh/Mozambique) focusing on non-migration and place attachment, and (3) policy coherence for gas flaring reduction and nuclear waste governance. Methodologically, Q-methodology features prominently in stakeholder perspective analysis across energy and environmental domains. Professor Cotton supervises four active PhD students at Teesside University and has guided eight completions at prior institutions. His current research portfolio includes: National Institute of Health Research grants on climate adaptation (2023-2025) and food insecurity (2023-2024) Research England Development Fund project Growing Teesside's Hydrogen Economy (2022-2027) ESRC-funded solar energy project in East Africa (2020-2023) Nuclear Waste Services fellowship on intergenerational justice (2022-2024) He collaborates with the Institute for Collective Place Leadership and external partners including Nuclear Waste Services, X-Energy, Stockton Borough Council, and Tees Valley Combined Authority on community-centered sustainability initiatives.