جستجوی استاد راهنمای دانشگاهی

Markus Hehnمشاهده پروفایل

پژوهشگر

• Indoor Positioning Systems
• Radar Systems
• Signal Processing
• +۳ مورد دیگر

Markus Hehn is a Researcher at the Chair of High Frequency Engineering within the Department of Electrical Engineering, Electronics and Information Technology (EEI) at Friedrich-Alexander-University Erlangen-Nuremberg. He holds a Dr.-Ing. (PhD) in Electrical Engineering, awarded with 'sehr gut' in November 2021. His work focuses on hardware design and signal processing for indoor positioning systems, including radar systems, low-frequency magnetic field localization, inertial navigation, and RFID technologies. Education: 2004-2007: Training as an Electronics Technician for Automation Technology 2008-2010: State-Certified Electrical Engineering Degree from Erlangen Technical College 2011-2014: B.Sc. in Electrical Engineering, Electronics, and Information Technology (FAU) 2014-2016: M.Sc. (with distinction) in the same field (FAU) Research interests span hardware development, radar systems, and signal processing for indoor navigation. Recent work emphasizes sequential sampling impulse radar, synthetic aperture imaging, and Kalman filter-based synchronization in networked systems. Collaborations include projects on UHF-RFID localization for mobile robots and 5G/6G antenna arrays. Labs/Teams: Active in the Institute of Microwaves and Photonics (LHFT) and the Chair of High Frequency Engineering. Seeks students for theses in hardware/circuit design, signal processing, and system design.

Fauzia Ahmadمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Array Processing
• Computational Imaging
• +۱۶ مورد دیگر

Fauzia Ahmad is an Associate Professor in the Department of Electrical and Computer Engineering at the College of Engineering, Temple University. She has previously held the position of Research Professor and Director of the Radar Imaging Lab at Villanova University. Her research is supported by major U.S. federal agencies, with over $7M in awarded research funding as Principal or Co-Principal Investigator. Ph.D. in Electrical Engineering, University of Pennsylvania, 1997 Dr. Ahmad's research focuses on statistical signal and array processing , computational imaging , and multi-modal sensing . Her work spans applications in through-the-wall radar , ground-penetrating radar , remote patient monitoring , and structural health monitoring . She employs advanced techniques in compressive sensing , sparse reconstruction , and machine learning to solve real-world sensing challenges. The recent publications highlight a strong trend in radar micro-Doppler signature analysis for human activity recognition, coprime array signal processing for super-resolution, and tensor decompositions in MIMO radar and communications. Her work increasingly integrates machine learning with traditional signal processing for robust detection and classification in noisy, real-world environments. Dr. Ahmad has received several prestigious honors: Fellow of the IEEE Fellow of the SPIE Chair of the IEEE Dennis J. Picard Medal Committee (2018-2020) She has served as an Associate Editor for multiple top-tier journals including IEEE Transactions on Signal Processing , IEEE Transactions on Aerospace and Electronic Systems , and IEEE Transactions on Computational Imaging , where she is currently a Senior Area Editor. She has led major conference series such as the SPIE Compressive Sensing and SPIE Big Data conferences. Her research is conducted through the Multi-modal Sensing and Imaging Lab , where she mentors students and collaborates on interdisciplinary projects involving radar, communications, and biomedical applications.

Victor Churchillمشاهده پروفایل

استادیار

• Computational Mathematics
• Scientific Machine Learning
• Image Reconstruction
• +۵ مورد دیگر

Victor Churchill is an Assistant Professor of Mathematics at Trinity College since 2023. He holds a Ph.D. and A.M. from Dartmouth College, an M.S. from New York University's Courant Institute, and a B.A. from Boston College. His research focuses on computational mathematics, scientific machine learning, and image reconstruction, particularly in Bayesian uncertainty quantification for synthetic aperture radar imaging and learning unknown dynamical systems using neural networks. He has held a postdoctoral position at The Ohio State University under Dr. Dongbin Xiu and previously worked at Dartmouth under Dr. Anne Gelb. Research Highlights: His work includes deep learning of PDEs, ensemble prediction for robust neural network training, and chaotic system learning from partial observations. Recent contributions address coarse time-scale observations and uncertainty quantification in SAR imaging. He was awarded the SIAM Science Policy Fellowship (2023-2024) to engage with federal science policy advocacy. Teaching: He teaches computational science courses at both undergraduate and graduate levels, integrating his research into lectures through case studies and data-driven examples. His pedagogical approach emphasizes applied computational mathematics and real-world problem-solving. Affiliations: Previously affiliated with The Ohio State University as a Visiting Assistant Professor of Scientific Computation. Active in computational math communities, including SIAM policy engagement. Personal Interests: An avid runner with marathon personal bests, he also enjoys bonsai cultivation, architectural design, and animal care. His unconventional hobbies include experimenting with hair color transformations.

Shaoqing Huمشاهده پروفایل

مدرس

• Millimeter Wave/THz Imaging
• Sparse Imaging
• Antennas and Arrays
• +۳ مورد دیگر

Dr. Shaoqing Hu is a Lecturer in Electronic and Electrical Engineering at Brunel University of London's College of Engineering, Design and Physical Sciences, and serves as an Adjunct Professor at Hangzhou Dianzi University. He holds a PhD from Queen Mary University of London (2020), with prior degrees from University of Electronic Science and Technology of China. His academic roles include Departmental Level 4 Coordinator, Brunel University London Pathway College liaison tutor, and external reviewer. His educational background includes: B.Eng in Vacuum Electronics (UESTC, 2013) M.Eng in Physical Electronics (UESTC, 2016) PhD in Electronic Engineering (QMUL, 2020) Dr. Hu's research centers on millimeter wave/THz security detection, sparse imaging, antennas, and wireless communication. His work bridges theoretical signal processing with practical security applications, particularly in personnel screening and target detection systems. Current projects focus on MIMO mmWave 3D imaging for future screening systems and advanced millimeter-wave imaging with sparse arrays. His group develops specialized hardware including 220 GHz imaging systems and multi-band antennas. Analysis of his recent publications reveals strong emphasis on sparse array configurations (MIMO planar/sparse arrays), algorithmic innovations (back-projection, low-rank matrix recovery), and emerging applications in security screening (2025 Signal Processing Magazine overview). The research spans THz to microwave frequencies with increasing integration of deep learning techniques since 2022. His scientific recognition includes: Fellow of the Higher Education Academy (2024) First Prize Best Student Paper Award (UCMMT 2020) Student Paper Award (IEEE AP CAP 2015) VinFuture Prize Official Nominator (2023) Dr. Hu actively supports early-career researchers through supervision of PhD students (including Shiwei Hu and Wenyi Yan) and MSc candidates. He has secured multiple research grants including Brunel Research Initiative and Enterprise Fund (BRIEF 2022-23), Brunel Research Development Fund, and NSFC collaboration grants. He serves as referee for major awards like UK Doctoral Researcher Award and supports fellowship applications including RAEng and Marie Curie schemes. His laboratory specializes in mm-wave/THz sparse imaging systems, featuring a 1.5m x 1.5m planar scanning setup and 220 GHz MIMO imaging capabilities. The team develops specialized antennas including GNSS arrays, dual-polarized horns, and quasi-Yagi structures for applications ranging from security screening to health sensing.

• Medical Image Processing
• Bioengineering
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Kristen Mariko Meiburger is an Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. She is a member of the PolitoBIOMed Lab , focusing on biomedical engineering research. Research Interests: Medical image processing, photoacoustic imaging, ultrasound technologies, and AI-driven diagnostic systems. Her work bridges industrial and information engineering with clinical applications. Recent Publications highlight advancements in photoacoustic uncertainty quantification, high-frequency vascular imaging, machine learning for muscular dystrophy analysis, and optoacoustic/ultrasound reconstruction techniques. Scientific Awards: Premio di Laurea GNB (2011) Premio di Qualità (2014) Key Projects: ImPACT-AI (2023-2025) AI-VASCUES (2023-2025) REAP (2021-2024) H2020-ICT (EU-funded) PhD Students: Bruna Cotrufo, Alen Shahini, Federico Sinnona, Matteo Salvi, Giulia Rotunno, Francesco Branciforti, Francesco Marzola.

MEHMET DEMİRمشاهده پروفایل

استادیار

• Electrical Engineering
• Signal Processing
• Image Processing
• +۳ مورد دیگر

Mehmet Demir is an Assistant Professor at Gaziantep University, Faculty of Engineering, specializing in Electrical and Electronics Engineering. His research focuses on signal processing, image processing, and machine learning with applications in synthetic aperture radar (SAR) imaging and compressed sensing. Education: PhD (2013-2019), MSc (2009-2012), and BSc (2004-2009) in Electrical and Electronics Engineering at Gaziantep University Academic Positions: Assistant Professor (2021-present), Research Assistant (2013-2021), and Research Assistant (2009-2013) at Gaziantep University Demir's research explores advanced algorithm development for SAR imaging systems, particularly in handling quantized data and signal-translation errors. His work leverages computational methods like ADMM, FISTA, and spectral conjugate gradient techniques, with applications in remote sensing and signal reconstruction. Recent publications focus on 1-bit compressive sensing in SAR imaging (2018, 2019, 2020, 2021), demonstrating his ongoing contributions to data-efficient image reconstruction algorithms. His research has received recognition through TÜBİTAK Publication Incentive Awards in 2019 and 2014. Demir actively engages in scientific communities as a member of TMMOB-EMO (Chamber of Electrical Engineers) since 2009, and contributes to engineering education with courses like EEE439 Introduction to Remote Sensing, emphasizing practical algorithm development in MATLAB/Python.

Kristen M. Meiburgerمشاهده پروفایل

استادیار

• Biomedical Image Processing
• Ultrasound Imaging
• Photoacoustic Imaging
• +۴ مورد دیگر

Kristen M. Meiburger is a Tenure-track Assistant Professor at the Department of Electronics and Telecommunications, Politecnico di Torino. She holds a Master’s in Biomedical Engineering (2010) and a Ph.D. in Biomedical Engineering (2015) from Politecnico di Torino. She has conducted research at the University of Texas at Austin (2013–2014) and the University of Toronto (2015). Her work focuses on biomedical image processing, including ultrasound and photoacoustic imaging, radiomics, deep learning, and signal reconstruction methods. Her research interests emphasize innovative imaging techniques, non-invasive vascular analysis, and dermatological applications. She leads ongoing projects like REAP (cancer imaging with optical coherence/photoacoustic tomography), AI-VASCUES (vascular dysregulation analysis), and ImPACT-AI (ethical AI in photoacoustic imaging). She teaches and proposes thesis topics at Politecnico di Torino’s DET (Department of Electronics and Telecommunications). Recent publications highlight advancements in AI-driven imaging, such as texture analysis in OCT, hybrid deep learning frameworks for microscopy, and GANs for color normalization. Her work bridges medical imaging innovation with clinical applications, focusing on disease diagnosis and treatment monitoring. Ongoing efforts integrate ethical AI practices and multi-center imaging harmonization to improve medical decision-making.

• Synthetic Aperture Radar (SAR)
• Interferometric SAR (InSAR)
• Coherent Change Detection (CCD)
• +۱۱ مورد دیگر

Dr Daniel Andre is a Lecturer in Radar at Cranfield University, affiliated with the Centre for Electronic Warfare, Information and Cyber within the Cranfield Defence and Security school. He leads the Cranfield Ground-Based SAR Laboratory (GBSAR Lab), conducting cutting-edge research in Synthetic Aperture Radar (SAR), interferometry, and multistatic imaging. Education: PhD in Applied Mathematics Joint Honours BSc in Mathematics and Physics His research interests span Synthetic Aperture Radar (SAR) , Interferometric SAR (InSAR) , Coherent Change Detection (CCD) , Bistatic and Multistatic Radar , Through-Wall Imaging , Polarimetry , and Remote Sensing . His work integrates applied mathematics with radar phenomenology to solve complex defense and security challenges. The recent publications reveal a strong trend in experimental and computational SAR research, particularly in multistatic 3D imaging , polarimetric coherence analysis , near-field effects , and through-wall transmission modeling . His team leverages laboratory-based SAR systems to validate novel algorithms under controlled conditions. Scientific Awards: John Benjamin Memorial Prize (UK Civil Service Scientist Innovation Award, 2011) Simon Fellowship, Isaac Newton Institute for Mathematical Sciences (2023) Dr Andre actively supervises PhD students and secures funding for advanced radar research. He collaborates with key defense and research institutions including Dstl, ESA, BAE Systems, QinetiQ, ATI, and the Sir Bobby Charlton Foundation. His projects often involve experimental validation, algorithm development, and real-world applications in intelligence and surveillance. He heads the GBSAR Lab, a unique facility enabling precise control over SAR geometries for fundamental research in radar coherence, imaging, and change detection. The lab supports both academic inquiry and defense technology development.

Thomas Blanfordمشاهده پروفایل

استادیار

• Underwater Acoustics
• Acoustic Sensing Devices
• Transducers
• +۵ مورد دیگر

Thomas Blanford serves as a Research Assistant Professor at the University of New Hampshire (UNH), based at the UNH Center for Acoustics Research & Education in Morse Hall, Durham, NH. His work bridges physics, electrical engineering, mechanical engineering, and computer science to advance underwater acoustic sensing technologies. Contact information includes email Thomas.Blanford@unh.edu and phone (603) 862-3202. His academic credentials include: Ph.D. in Acoustics from Pennsylvania State University Dr. Blanford's research centers on the complex interplay between acoustic stimuli, sensor systems, and environmental factors in underwater domains. His primary interests span: Underwater Acoustics Acoustic Sensing Devices and Transducers Signal Processing Navigation & Guidance Systems Sonar Systems Electromechanical Sensor Design He develops predictive models and designs novel sensor systems including transducers, electronics, and signal processing frameworks to address challenges in underwater detection and environmental monitoring. Analysis of his 10 publications from 2023-2025 reveals concentrated advancement in synthetic aperture sonar (SAS) technologies. Key trends include coherence-based motion estimation, late-time return processing, and neural network applications for volumetric reconstruction and object detection. His work consistently addresses environmental complexity in underwater sensing while integrating physics-based modeling with machine learning approaches. No scientific awards or major fellowships are documented in the available information. Dr. Blanford is actively engaged with the UNH Center for Acoustics Research & Education, where he leads experimental work in sensor development and signal processing. Current projects focus on multi-static SAS imaging, transducer design optimization, and AI-driven interpretation of sonar data for defense and environmental applications.

Randolf Ebeltمشاهده پروفایل

استاد

• Radar Systems
• Wireless Localization
• Automotive Radar
• +۳ مورد دیگر

Randolf Ebelt serves as Professor and Chair of High Frequency Engineering within the Department of Electrical Engineering, Electronics and Information Technology (EEI) at FAU's Faculty of Engineering. His research focuses on radar systems and wireless localization technologies with applications in automotive and indoor environments. His primary research interests include automotive radar signal processing, deep learning applications for radar enhancement, and wireless sensor networks for precise localization. He has pioneered work in ghost detection identification, MIMO radar arrays, and 24 GHz transceiver design for real-time 3D positioning systems. Analysis of his publication trends shows increasing integration of artificial intelligence with traditional radar engineering since 2019, particularly using deep learning for automotive radar enhancement and ghost detection. His work consistently addresses practical implementation challenges including phase noise effects, antenna array optimization, and multipath mitigation in complex environments. No scientific awards were documented in the provided materials. Information regarding student advising or research grants was not specified in the source content. Professor Ebelt leads the Chair of High Frequency Engineering (LHFT) research group at FAU, which maintains active collaborations with industry partners on radar technology development and participates in international research initiatives focused on automotive and wireless positioning systems.

Carlos Augusto Soares Ferreiraمشاهده پروفایل

پژوهشگر ارشد

• Carbon storage
• Poromechanics
• Fractured porous media
• +۴ مورد دیگر

Carlos Augusto Soares Ferreira is a Research Fellow at the Danish Offshore Technology Centre (DOTC) within GeoEnergy Engineering at the Technical University of Denmark (DTU) in Kgs. Lyngby, Denmark. His research focuses on subsurface energy systems with emphasis on carbon storage and geomechanics, contributing to UN Sustainable Development Goals for climate action. Contact: casfe@dtu.dk. His core research areas include Carbon storage, Poromechanics, Fractured porous media, and Machine learning applications. Work integrates computational modeling (Finite volume method, THM simulations) with experimental data to address challenges in CO2 sequestration and reservoir integrity. Key methodologies involve fracture flow analysis and machine learning for sparse-data reservoir characterization. Recent publications demonstrate strong focus on CO2 storage in chalk reservoirs, hydraulic permeability of rough fractures, and deep learning applications. Dominant themes include thermo-hydro-mechanical coupling, caprock integrity assessment, and matrix-fracture interactions using high-fidelity simulations validated against field data. Ferreira completed his PhD project "Role of micro cracks in flow and deformation" (2020-2025) under H. M. Nick's supervision at DTU. He has contributed four significant datasets on fracture flow patterns and hydraulic aperture to DTU's repository, supporting open science in geomechanics research.

Stacy Tantumمشاهده پروفایل

دانشیار

• Physics-based statistical signal processing
• Context-aware machine learning
• Domain-informed data science
• +۷ مورد دیگر

Stacy Tantum is an Associate Professor in the Department of Electrical and Computer Engineering at Duke University , where she also serves as the Faculty Director of the 360 Coaching Program for first-year advising in the Pratt School of Engineering. Her research bridges domain-informed data science and context-aware machine learning , emphasizing privacy-aware , explainable AI , and bias mitigation in machine learning systems. She integrates neuroscience-informed teaching , metacognition , and cognitive load theory into pedagogical practices to enhance student learning experiences. B.S.E.E., Tufts University (1994) M.S., Duke University (1996) Ph.D., Duke University (1998) Her work spans landmine detection , sensor data fusion , and statistical signal processing , with recent contributions to interpretable machine learning in medical imaging and educational coaching frameworks. Publications highlight innovations in UXO discrimination , Kalman filtering , and independent component analysis , alongside pedagogical research on mindset-aware learning . She teaches courses including Introduction to Machine Learning and Applied Probability for Statistical Learning .

David Downesمشاهده پروفایل

مدرس ارشد

• X-ray systems
• image processing
• interaction design
• +۴ مورد دیگر

David Downes is a Senior Lecturer at the Nottingham School of Art & Design, Nottingham Trent University, where he teaches scripting for animators, procedural animation, and motion capture applications while supporting students across multiple courses. He actively supervises postgraduate research in his specialized domains. His research centers on X-ray systems and image processing for security applications, interaction design, interactive environments, computational creativity, procedural animation, and motion capture. A sustained focus involves developing X-ray imaging systems for security screening, conducted within the Imaging Science Group led by Prof. Paul Evans. This work bridges engineering, computer science, and creative design to solve real-world security challenges. Downes' recent publications (2023-2024) reveal two dominant research trajectories: advanced X-ray imaging techniques (conical shell beam tomography/diffraction for medical/security applications) and machine learning-driven virtual reality exposure therapy for mental health. His work demonstrates interdisciplinary innovation spanning physics, computer vision, and clinical psychology. Professional recognition includes: Fellow of the Higher Education Academy Downes supervises PhD students including Hogg (2024) on focal construct geometry for X-ray diffraction. His research is funded by Defence and Security Accelerator (DASA), EPSRC, US Department of Homeland Security (DHS), and Cranfield University, with collaborations extending to Roma Patel of The Society of British Theatre Design. Key collaborators: Roma Patel (The Society of British Theatre Design) Primary sponsors: DASA, EPSRC, DHS, Cranfield University As a core member of the Imaging Science Group, Downes contributes to internationally recognized research in security-focused X-ray systems. The group maintains active GitHub repositories and ResearchGate presence, with projects emphasizing human-centered design validated through ergonomics studies and real-world security implementations.

Mete Ahishaliمشاهده پروفایل

پژوهشگر

• Machine Learning
• Remote Sensing
• Medical Imaging
• +۴ مورد دیگر

Mete Ahishali is a Postdoctoral Researcher affiliated with the School of Forest Sciences at the University of Eastern Finland's Faculty of Science, Forestry and Technology. His research focuses on advanced machine learning techniques applied to remote sensing, medical imaging, and environmental monitoring. He is a core member of the Global Ecosystem Health Observatory (GEHO) research group since January 2024. His technical expertise spans hyperspectral image analysis, SAR image classification, domain adaptation networks, and medical image restoration. Key contributions include developing neural network architectures for tree mortality mapping, anomaly detection in industrial systems, and COVID-19 classification using X-ray images. Recent work demonstrates innovations in autoencoder-based band selection for hyperspectral imagery, self-attention fusion networks for cardiac diagnostics, and operational U-nets for wildfire detection. His research bridges theoretical machine learning advancements with practical applications in environmental science and healthcare. Notable technical skills include deep learning frameworks, remote sensing data processing, and medical imaging modalities. Current projects focus on scalable solutions for large-scale Earth observation data analysis and cross-modal medical imaging systems.

Julie A. Jacksonمشاهده پروفایل

استاد

• Radar Systems
• Synthetic Aperture Radar (SAR)
• Polarimetric SAR
• +۷ مورد دیگر

Dr. Julie A. Jackson is a Professor of Electrical Engineering in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), part of the Graduate School of Engineering and Management. She holds a Ph.D. in Electrical Engineering from The Ohio State University (2009), an M.S. (2004), and a B.S. (2002) from Wright State University. Her work bridges theoretical and applied radar systems, with emphasis on synthetic aperture radar, signal processing, and electromagnetics. Ph.D., Electrical Engineering, The Ohio State University, 2009 M.S., Electrical Engineering, The Ohio State University, 2004 B.S., Electrical Engineering, Wright State University, 2002 Dr. Jackson's research focuses on radar systems , particularly synthetic aperture radar (SAR) , compressive sensing , polarimetric imaging , and passive radar using commercial signals . She develops advanced signal processing algorithms for feature extraction, autofocus, and clutter modeling. Her work enables high-resolution imaging in challenging environments and has been applied to both military and civilian remote sensing. Her recent publications show a strong trend in exploiting sparsity and crosstalk in polarimetric SAR, autofocus algorithms using convex optimization, and passive radar using LTE and WiMAX signals. These efforts reflect a deep integration of electromagnetic theory, statistical signal processing, and computational imaging. Dr. Jackson has received numerous accolades, including: 2023 EN Distinguished Teaching Professor Award 2022 John L. McLucas Basic Research Award 2020 Air Force Outstanding Scientist/Engineer Award (Mid-Career) 2019 IEEE Fred Nathanson Memorial Radar Award 2019 Gage H. Crocker Outstanding Professor Award 2016 SOCHE Faculty Excellence Award She has advised multiple graduate students and led collaborative research with faculty and laboratory technicians at AFIT. Her work has been supported by internal Air Force research funding and has resulted in peer-reviewed publications, patents, and real-world radar applications. Dr. Jackson also holds a U.S. patent on polarization recovery in SAR systems, highlighting the translational impact of her research. She is actively involved in the IEEE Aerospace and Electronic Systems Society and contributes to the advancement of radar technology through publications, conferences, and mentorship. Her laboratory work emphasizes experimental validation of theoretical models, particularly in heterogeneous clutter environments and multistatic configurations.