جست‌وجوی استاد و پژوهشگر

Bruno Stuytsمشاهده پروفایل

استاد مهمان

• Offshore Wind Energy
• Geotechnical Engineering
• Soil-Structure Interaction
• +۳ مورد دیگر

Bruno Stuyts is a Visiting Professor at Ghent University and a postdoctoral researcher in the Applied Mechanics Acoustics & Vibration Research Group. His research focuses on offshore wind energy, geotechnical engineering, and soil-structure interaction, with emphasis on data-driven modeling and in-situ monitoring. He leads projects like WINDSOIL (2020–2024) and SOIL-TWIN (2020–2023), addressing soil-structure interaction and smart monitoring of offshore foundations. His work integrates geophysics, computational mechanics, and artificial intelligence to improve offshore wind foundation design and geohazard assessment. Key research interests include seabed response under wave loading, eigenfrequency analysis of wind turbines, and seismic inversion for subsurface characterization. He collaborates globally on projects involving pore pressure monitoring, data fusion, and real-time modeling of monopile foundations. His contributions span experimental studies (e.g., 3D seabed response experiments) and numerical simulations (e.g., finite element modeling of monitored monopiles). Stuyts has contributed to over 30 peer-reviewed articles, covering topics like CPT-based correlations for shear modulus, scour protection effects on turbine stiffness, and Bayesian estimation techniques. His work bridges academic research and industry applications, aiming to optimize offshore infrastructure reliability through advanced data science and geotechnical innovations. He actively participates in conferences, presenting on data science applications, seismic inversion, and offshore wind challenges. Collaborations include institutions like Zenodo (dataset sharing) and ASME. Current efforts focus on integrating geohazard mapping, AI-driven geotechnical analysis, and open-source modeling frameworks for sustainable offshore energy systems.

Qiang Yuمشاهده پروفایل

پژوهشگر ارشد

• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• Machine Learning
• +۵ مورد دیگر

Dr. Qiang Yu is a Postdoctoral Research Fellow at the Thompson Institute, University of the Sunshine Coast (UniSC), Australia. His research focuses on neuroimaging applications for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), integrating advanced MRI techniques and machine learning to advance precision medicine. He holds a PhD in Computational and Applied Mathematics from Queensland University of Technology (QUT, 2013) and completed postdoctoral fellowships at the University of Queensland (UQ) and QUT, specializing in anomalous diffusion modeling in MRI. Education: PhD (Computational and Applied Mathematics), Queensland University of Technology (2013) MSc (Computational Mathematics), Xiamen University, China (Year not specified) BSc (Applied Mathematics), Fujian Agriculture and Forestry University, China (Year not specified) Research Interests: Dr. Yu’s work bridges mathematical modeling and biomedical imaging, particularly in developing fractional calculus-based models for MRI tissue analysis. His key areas include: Anomalous diffusion modeling in biological tissues White matter microstructure characterization via diffusion MRI Fractional-order Bloch equations for MRI signal analysis ME/CFS neuroimaging patterns and clinical subtyping Publications Trends: His recent articles emphasize MRI-based diagnostic advancements, including diffusion metrics for ME/CFS subtypes, fractional models for tissue relaxation, and algorithmic innovations in medical image processing. These contributions highlight interdisciplinary synergy between computational mathematics and clinical neurology. Awards & Memberships: Executive Dean’s Commendation Award, QUT (2013) Outstanding Doctoral Thesis Award, QUT (2013) Editorial Board Member, Applied and Computational Mathematics (2022–2024) Grants & Projects: University of Queensland Postdoctoral Fellowship ($287,945, 2014–2016): Modeled anomalous diffusion in MRI for tissue microstructure characterization Collaborations on distributed-order fractional models and IVIM MRI methodologies Labs & Teams: Affiliated with the Thompson Institute’s ME/CFS neuroimaging program and the Centre for Advanced Imaging (UQ). His work intersects with clinical trials and advanced MRI hardware development for high-resolution brain imaging.

• Inverse Problems
• Numerical Analysis
• Wave Equations
• +۳ مورد دیگر

Thorsten Hohage is a Professor at the Institute of Numerical and Applied Mathematics, Georg-August-Universität Göttingen, and a Max Planck Fellow at the Max Planck Institute for Solar System Research. His work bridges inverse problems, numerical analysis, and wave equation modeling. Research interests focus on computational methods for inverse problems in helioseismology, aeroacoustics, and imaging. Recent projects include iterative holography for solar differential rotation, phase retrieval in X-ray and EUV imaging, and regularization techniques in Banach spaces. Collaborations span institutions like Zuse Institute Berlin and Johannes-Kepler University Linz. Key publications since 2022 address viscous-inertial solar waves, phaseless scattering, and learned boundary conditions. His work employs advanced numerical algorithms for heterogeneous materials and dispersive media, with applications in astrophysics and biomedical imaging.

• Information and Communication Technology (ICT)
• Radar Systems
• Signal Processing
• +۵ مورد دیگر

Karol Abratkiewicz is an Assistant Professor at the Institute of Electronic Systems within the Faculty of Electronics and Information Technology at Warsaw University of Technology. His primary research focuses on Radar Systems , Signal Processing , and Passive Radar technologies. Research Trends : His recent work emphasizes advancements in passive radar noise suppression, time-frequency analysis for vital sign detection, and super-resolution techniques in ISAR/SAR imaging using 5G and satellite signals. Scientific Metrics : He has published 64 articles, supervised 1 thesis, and participated in 10 projects. His Scopus h-index is 10, and his WoS h-index is 9. Contact : Email karol.abratkiewicz@pw.edu.pl | Phone +48 22 234 17 04 | Room 447

Lu Ganمشاهده پروفایل

مدرس ارشد

• Signal Processing
• Compressed Sensing
• Machine Learning
• +۱۰ مورد دیگر

Dr. Lu Gan is a Senior Lecturer in the Department of Electronic and Computer Engineering at Brunel University London, within the College of Engineering, Design, and Physical Sciences. She earned her B.Eng and M.Eng in Electronic and Information Engineering from Southeast University, China, in 1998 and 2000, followed by a Ph.D. in Information Engineering from Nanyang Technological University, Singapore, in 2004. Before joining Brunel in 2008, she held faculty positions at the University of Newcastle, Australia (2004-2006) and the University of Liverpool, UK (2006-2007). Her research spans fundamental signal processing theories, machine learning, and applications in image/video coding, non-destructive terahertz/ultrasound imaging, wireless communications, and sparse antenna arrays. Education: B.Eng (Electronic and Information Engineering), Southeast University, China (1998) M.Eng (Electronic and Information Engineering), Southeast University, China (2000) Ph.D (Information Engineering), Nanyang Technological University, Singapore (2004) Her research focuses on structured sparse signal processing for infrared/terahertz systems, super-resolution in non-destructive imaging, deep learning for terahertz data, non-orthogonal pilot design for 5G systems, and separation of singing voice from music. She has secured funding from EPSRC, Innovate UK, BBSRC, UK Atomic Energy Authority, and TWI. She actively contributes to academic service as an Associate Editor for IEEE Signal Processing Letters, IEEE Transactions on Circuits and Systems-I, and as a Meta Reviewer for ICASSP 2025. Her work has been recognized with a Best Paper Award from the Journal of The British Blockchain Association in 2022 and a Gold Medal at the IEEE Audio and Acoustic Signal Processing Challenge (DCASE) in 2022. Dr. Gan also serves as a reviewer for top journals like IEEE Transactions on Information Theory and IEEE Transactions on Signal Processing, and participates in grant panels for the Royal Society and EPSRC. Recent publications emphasize cross-domain speech enhancement architectures, terahertz data reconstruction via spatio-temporal dictionary learning, and coprime array designs using Chinese remaindering over quadratic fields. Her work bridges compressive sensing, lattice structures, and practical applications in healthcare and communication systems. She is a Senior Member of IEEE, Fellow of the Higher Education Academy (UK), and actively contributes to departmental leadership as Course Director for MSc Wireless Communication Systems, Level 3 Coordinator, and Social Media Administrator.

• Cosmology
• Astrophysics
• Cosmic Microwave Background
• +۳ مورد دیگر

Victor C Chan is a researcher in the Department of Astronomy & Astrophysics at the University of Toronto, Faculty of Arts & Science. His work focuses on developing computational tools for Cosmic Microwave Background analysis, particularly in the area of pixel-space methods for gravitational lensing estimation. Dr. Chan's research interests span cosmology, astrophysics, and computational physics, with specific expertise in Cosmic Microwave Background analysis, gravitational lensing, and high-performance computing for cosmological applications. His work bridges theoretical cosmology with practical computational methods, developing tools that enable more efficient and accurate analysis of cosmological data. The research output shows a consistent progression in developing sophisticated tools for CMB analysis, moving from foundational algorithm development to more advanced implementations incorporating parallel computing and specialized filtering techniques. The work demonstrates strong technical expertise in both cosmological theory and computational implementation. Dr. Chan has developed the cmbpix software package, which has become an important tool in the cosmology community for pixel-space CMB analysis. The package provides researchers with methods for lensing estimation, spherical harmonic transforms, and other critical operations needed for modern CMB data analysis. As a supervisor, Dr. Chan offers opportunities to work at the intersection of cosmology and computational science, with projects involving algorithm development, statistical analysis, and application to real cosmological data. The research program provides valuable experience with both theoretical concepts and practical implementation skills that are highly relevant in modern cosmological research.

Roni Senguptaمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• Computational Photography
• +۷ مورد دیگر

Roni Sengupta is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where she leads the Spatial & Physical Intelligence (SPIN) Lab. Her academic journey includes a Ph.D. from the University of Maryland (2019), a postdoctoral position at the University of Washington (2019-2022), and an undergraduate degree in Electronics and Tele-Communication Engineering from Jadavpur University in India. Her research lies at the intersection of Computer Vision and Computer Graphics, with four primary themes: Inverse Rendering : Recovering physical scene properties like geometry, material reflectance, and lighting 3D Perception from Endoscopy : Monocular depth estimation and SLAM for medical imaging applications Inverse Physics : Recovering 3D geometry and physical properties from sparse video inputs Generative Facial Editing : Developing personalized, training-free methods for facial attribute manipulation Her work has practical applications in visual content creation, telepresence, AR/VR, robotics, and healthcare, with several technologies adopted by companies including Microsoft. Dr. Sengupta's publication record shows a clear trajectory toward increasingly sophisticated inverse problem solving, with recent work (2023-2025) focusing on neural approaches to inverse rendering, medical imaging applications, and personalized generative models. Her research demonstrates strong interdisciplinary connections between computer vision, graphics, and medical applications. Her scientific recognition includes: NIH NIBIB Trailblazer Award for New and Early Stage Investigators (2024) UNC Junior Faculty Development Award (2024) UNC CS Student Association Excellence in Teaching Award (2023) CVPR Best Student Paper Honorable Mentions (2021) Dr. Sengupta actively mentors a diverse team of researchers, including 5 current PhD students, 3 MS students, and several undergraduates. Her former students have gone on to positions at Google, Kitware, Databricks, and Capitol One. She has secured significant research funding, with recent work supported by NIH and industry partners. Her teaching portfolio includes undergraduate and graduate courses in computer vision, 3D generative models, and neural rendering. The SPIN Lab maintains strong industry connections, with research collaborations and technology adoption by Microsoft, NVIDIA Research, and Snapchat Research. The lab's work on background matting has been particularly influential, receiving recognition at CVPR 2021 and being adopted by multiple companies.

Firat Y. Testikمشاهده پروفایل

استاد

• Environmental Fluid Mechanics
• Rainfall Microphysics
• Coastal Engineering
• +۳ مورد دیگر

Firat Y. Testik is a Professor in the Civil and Environmental Engineering and Construction Management department at the University of Texas at San Antonio's Klesse College of Engineering and Integrated Design. Contactable at firat.testik@utsa.edu, he leads the Flow Physics Lab and has published extensively on rainfall microphysics and coastal engineering challenges. Ph.D. in Aerospace Engineering from Arizona State University His research focuses on environmental fluid mechanics , particularly: Rainfall microphysics, including raindrop collision dynamics and shape-velocity distribution under wind shear Coastal bridge vulnerability to hurricane wave impacts Gravity current propagation through aquatic vegetation Atmospheric boundary layer turbulence interactions Instrument validation for precipitation measurement Recent publications show a trend toward advanced measurement techniques (high-speed imaging, sUAS) and hazard modeling for hurricanes. He actively contributes to operational rainfall estimation algorithms and infrastructure resilience studies.

Audun Theodorsenمشاهده پروفایل

دانشیار

• Plasma Physics
• Nuclear Fusion
• Turbulence
• +۳ مورد دیگر

Audun Theodorsen is an Associate Professor at the Department of Physics and Technology, UiT The Arctic University of Norway, specializing in Complex Systems Modeling within plasma physics and nuclear fusion. His research focuses on turbulence , stochastic modeling , and intermittent fluctuations in fusion plasmas, particularly in the scrape-off layer of tokamaks like Alcator C-Mod. He also contributes to understanding cosmic dust dynamics using data from Solar Orbiter and Parker Solar Probe. Key Collaborators: Odd Erik Garcia, Samuel Kociscak, Ingrid Brigitte Mann, Ralph Kube, Juan Manuel Losada Teaching: Courses in Statistical Physics , Stochastic Modeling , and Nonlinear Dynamics His recent publications include stochastic models for plasma filaments and Bayesian analyses of dust impact ionization , spanning journals like Physics of Plasmas , Astronomy and Astrophysics , and Journal of Geophysical Research: Atmospheres . His work bridges fusion energy research and space science , emphasizing universal statistical patterns across diverse physical systems. For details on his research projects, see ITER and Fusion Research Center Norway .

• Gravitational Lensing
• Astrophysics
• Cosmology
• +۵ مورد دیگر

Dr. Hector Vives Arias is a researcher in the Department of Astronomy at the University of Valencia, specializing in gravitational lensing applications to astrophysics and cosmology. His work focuses on high-resolution studies of quasars, active galactic nuclei (AGN), and cosmic structures using cutting-edge observational techniques. Education: PhD in Gravitational Lensing: the Structure of Quasars and Galaxies (2016), Universitat de València, supervised by Dr. José Antonio Muñoz Lozano. Research Focus: Vives Arias leverages gravitational lensing as a natural telescope to probe micro-arcsecond structures in distant objects. His research integrates multi-wavelength observations (radio to infrared) to investigate accretion disk physics, AGN outflows, and dark matter properties. Key methodologies include: Strong and microlensing analysis of lensed quasars ALMA/VLA submillimeter and radio imaging High-resolution spectroscopy of molecular gas Cosmological constraints from microlensing statistics Multi-epoch monitoring of variable sources Publication Trends: His recent work (2019-2024) demonstrates increasing use of ALMA for molecular structure mapping in AGN tori (e.g., NGC 1068) and primordial black hole constraints via quasar microlensing. A consistent theme is exploiting gravitational lensing to achieve resolutions unattainable by conventional telescopes, particularly for sub-microJy radio sources. Scientific Awards: No awards documented in provided materials Advising and Grants: No student supervision or grant information is mentioned in available records. His research appears institutionally supported through University of Valencia affiliations and telescope time allocations. Research Group: Active member of the "Lensing: Gravitational Lensing: A Tool for Cosmology and Astrophysics" group, collaborating on projects involving CanariCam, VLA, and ALMA observations of lensed systems.

• Optical Fiber Sensors
• Brillouin Scattering
• Distributed Sensing
• +۳ مورد دیگر

Kouhei Noda is a Researcher at Tokyo Institute of Technology , specializing in Optical Fiber Sensors , Brillouin Scattering , and Distributed Sensing . His work focuses on advancing Brillouin optical correlation-domain reflectometry (BOCDR) for high-resolution, real-time tactile and temperature sensing applications. Education: Doctor (Engineering), Tokyo Institute of Technology (2023). Research Interests: Noda's research explores Brillouin scattering for distributed strain/temperature sensing, tactile sensors using polymer optical fibers, and polarization-sensitive reflectometry to enhance measurement accuracy. He investigates low-coherence light sources and cost-effective fiber amplifier configurations to improve system performance. Recent Article Trends: His 15 most recent publications (2020-2025) emphasize high-speed distributed sensing , real-time touch/tactile detection , and systematic error compensation in BOCDR systems. Key subfields include noise floor distortion compensation , low-coherence Brillouin sensors , and polymer fiber Bragg grating applications . Technical Contributions: Noda has co-authored patents related to wheelchair motorization devices and multimode FBG temperature stabilization . His collaborations with researchers like Yosuke Mizuno and Kentaro Nakamura span experimental validation, simulation analysis, and hardware optimization for optical sensing systems.

• Geostatistics
• Environmental Modeling
• Risk Assessment
• +۷ مورد دیگر

Dr. Alexander Sun is a Researcher at the Bureau of Economic Geology, University of Texas at Austin, focusing on computational hydrology, geostatistics, and data-driven predictive analytics. His work addresses climate change impacts, energy-water nexus, and sustainable water management through multidisciplinary approaches. B.S., Civil and Environmental Engineering, University of California, Los Angeles (1995) M.S. and Ph.D., Civil and Environmental Engineering, University of California, Berkeley (1996, 2000) His research integrates physics-based modeling with machine learning to solve hydrogeoscience challenges, particularly in groundwater-surface water interactions, CO2 plume migration, and real-time data fusion. Recent projects include NASA-funded GRACE satellite data gap filling and industrial collaborations on sensor anomaly detection. Key trends in his publications include applying artificial intelligence to hydrological modeling, enhancing remote sensing data utility, and optimizing carbon sequestration. He emphasizes bridging process-level understanding with deep learning for predictive models. Dr. Sun actively seeks partnerships with government and industry stakeholders to translate computational tools into practical environmental decision support systems. He co-authored a seminal book on environmental model calibration with his father in 2015.

• Oceanography
• Hydrodynamic Modeling
• Satellite Altimetry
• +۳ مورد دیگر

M. Verlaan is a Professor at Delft University of Technology (TU Delft) within the Electrical Engineering, Mathematics and Computer Science faculty. His research focuses on Oceanography , Hydrodynamic Modeling , and Satellite Altimetry , with emphasis on storm surge prediction , tidal dynamics , and climate change impacts on coastal systems . Recent works highlight his expertise in nonlinear tide-surge interactions and altimetry-based river water level estimation . He has developed advanced Ensemble Kalman Filtering systems for ocean modeling and contributed to global storm surge projections using CMIP6 climate models. His 2025 studies include Arctic tidal analysis via GNSS buoys and improved shallow-water tidal modeling. Collaborations span Earth and Space Science , Ocean Dynamics , and Journal of Geophysical Research-Oceans . Key datasets include Arctic sea level measurements and Tide-surge simulations , reflecting his work in coastal flood hazard assessment and hydrodynamic leveling for geodetic applications. His 2024-2025 publications demonstrate a trend toward multi-sensor data integration (e.g., Sentinel-6 altimeters, GNSS buoys, AIS) for high-resolution coastal and riverine monitoring . Themes include nonlinear feedback mechanisms in tidal systems and climate-driven variability in coastal processes.

Richard Alligierمشاهده پروفایل

مدرس

• Machine Learning
• Air Traffic Management
• Optimization Algorithms
• +۵ مورد دیگر

Richard Alligier is a lecturer and researcher at Ecole Nationale de l'Aviation Civile (ENAC) specializing in artificial intelligence applications for air traffic management. His work bridges machine learning, optimization algorithms, and trajectory prediction to address critical challenges in conflict detection and resolution for both manned and unmanned aerial systems. Research Focus : Trajectory prediction, conflict resolution, UAV collision avoidance, mass/thrust estimation, and uncertainty modeling Key Collaborations : Nicolas Durand, David Gianazza, Xavier Olive, Kim Gaume, Sarah Degaugue His publications (2011–2024) analyze trajectory uncertainty quantification, 3D maneuver visualization, and human-aligned deconfliction strategies using ADS-B data. Notable contributions include: Dual-horizon collision avoidance algorithms integrating human factors High-confidence interval prediction frameworks Wind parameter extraction from flight paths Machine learning models for climb/descent phase optimization Awarded the 2019 best paper in trajectory prediction , his work emphasizes operational alignment between automated systems and air traffic controller decision-making. He employs GPU acceleration, metaheuristics, and deep learning architectures while maintaining a focus on practical implementation through partnerships with ONERA and ISAE-SUPAERO.

Luciana Fenoglioمشاهده پروفایل

دانشیار

• Satellite Geodesy
• Sea Level Change
• Satellite Altimetry
• +۴ مورد دیگر

Luciana Fenoglio is an Associate Professor at the Space Geodesy and Geodynamics Unit, Onsala Space Observatory (Chalmers University of Technology) and a Senior Academic Councillor at the Institute for Geodesy and Geoinformation, University of Bonn. She joined the University of Bonn in 2015 and holds a habilitation in Physical Geodesy from the Technical University of Darmstadt (2014). Her research focuses on satellite geodesy, altimetry, and monitoring water cycle dynamics. Research Interests: Dr. Fenoglio investigates small-scale ocean variability, sea level change, satellite altimetry techniques (conventional and delay Doppler), coastal data retracking methods, and height reference systems. Her work bridges geodesy, oceanography, and hydrology, emphasizing high-resolution satellite applications for coastal and inland water monitoring. Publication Focus: Her recent articles (2020-2025) predominantly analyze advanced satellite altimetry methods (SAR, SWOT, Sentinel-3) for coastal sea level monitoring, river discharge estimation, and geodetic model validation. Key themes include radar signal processing, hydrological applications, climate-related ocean dynamics, and international mission collaboration. Awards: Italian Ministry of Education Award for Physics/Astronomy (scholarship abroad) First Prize for Astronomy Research Report (University of Turin) Projects & Leadership: She leads/participates in major initiatives including ESA's Climate Change Initiative, DFG-EDDY, SWOT Science Team projects (CONWEST-DYCO, REFECCT), and chairs the IAG Sub-Commission on High-Resolution Altimetry. She also coordinates validation for Sentinel-3 and Copernicus missions. Affiliations: Dr. Fenoglio directs research at the Astronomical, Physical and Mathematical Geodesy Group (University of Bonn) and collaborates globally through the Ocean Sea Surface Topography Science Team and Coastal Altimetry Working Group.