جست‌وجوی استاد و پژوهشگر

• Engineering Acoustics
• Acoustic Measurement
• Communication Acoustics
• +۴ مورد دیگر

Yasuhiro Oikawa is a Professor at the School of Fundamental Science and Engineering, Faculty of Science and Engineering, Waseda University. He holds a Doctor of Engineering degree from Waseda University and has been actively contributing to acoustics, signal processing, and optical measurement techniques. His research spans Sound field visualization using parallel phase-shifting interferometry Phase-aware audio signal processing algorithms Acoustic calibration and microphone sensitivity analysis Real-time sound event localization systems His recent publications focus on advanced time-frequency analysis, optical methods for sound measurement, and deep learning applications in acoustics. Key trends include Improving resolution in spectrogram-based signal processing Integration of physical models with neural networks Development of wearable acoustic sensor arrays Scientific awards recognizing his work include The Fumio Okano Best 3D Paper Awards CSS2018 Best Paper Award Acoustical Society of Japan Contribution Award Institute of Electronics, Information and Communication Engineers Human Communication Award He has served as a committee member for the Acoustical Society of Japan and is affiliated with organizations such as ACM, IEEE, and Acoustical Society of America. His research has been supported by collaborations with institutions like Technical University of Denmark and applications in robotics, museum exhibits, and consumer electronics.

• Medical Imaging
• Signal Processing
• Computational Mathematics
• +۴ مورد دیگر

Prasad Theeda is a Research Fellow at the Malaysia School of Information Technology, specializing in signal processing, medical imaging, and computational mathematics. His work focuses on compressed sensing, tomographic reconstruction, and optimization techniques applied to imaging systems. He has contributed to advancements in minimizing radiation exposure in CT scans through preconditioned sensing matrices and sparse-view algorithms. Key research areas: Compressed sensing, frame theory, medical imaging optimization His publications span topics like sparse sampling strategies for X-ray tomography and nullspace property analysis in invertible operators. Collaborations include work with institutions on global optimization and inverse problems. No formal awards or grants are listed, though his research demonstrates significant technical contributions to imaging science.

Yasir Alfadhlمشاهده پروفایل

مدرس ارشد

• Electromagnetics
• Antennas
• High-Power Microwaves
• +۶ مورد دیگر

Dr. Yasir Alfadhl is a Senior Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London. He holds a BEng (Hons.) and PhD, and is a Fellow of the Higher Education Academy (FHEA), member of the Institution of Engineering and Technology (MIET), and Senior Member of the IEEE (SMIEEE). As Programme Lead for the Telecommunications stream, his research focuses on electromagnetics and antennas , including computational electromagnetics, high-power microwaves, bio-electromagnetics, and dielectric characterization. His work also spans telecommunications topics like physical-layer security, cooperative radio, and radiofrequency fingerprinting for location-aware applications. His research interests include: Design of high-power microwave sources (e.g., magnetrons, backward wave oscillators) Electromagnetic exposure assessments for biomedical and communication systems Millimeter-wave and terahertz antenna systems for security and medical imaging RF security protocols and authentication methods Recent publications emphasize terahertz imaging systems, millimeter-wave antenna design, and electromagnetic field effects on biological tissues. Dr. Alfadhl collaborates on projects involving 5G beamforming networks, flexible wireless systems, and sparse array imaging techniques. His work bridges theoretical modeling (using tools like CST Studio Suite and PIC simulations) with practical applications in health monitoring, security screening, and telecommunications infrastructure.

Kam Arnoldمشاهده پروفایل

دانشیار

• Cosmic Microwave Background
• Cosmology
• Polarimetry
• +۴ مورد دیگر

Dr. Kam Arnold is an Associate Professor in the Department of Astronomy and Astrophysics at the University of California, San Diego. His research centers on precision measurements of the Cosmic Microwave Background (CMB), specializing in intensity and polarization observations to probe inflationary cosmology and fundamental physics. He contributes significantly to major CMB projects including the Simons Observatory (SO), Simons Array, and the future CMB-S4 experiment. Instrumental development focuses on next-generation detector and telescope technologies, particularly cryogenic polarization modulators, calibration systems, and superconducting magnetic bearings for millimeter-wave applications. Recent work (2023-2025) emphasizes anisotropic birefringence constraints, polarization modulator engineering, and validation of power spectrum reconstruction methods for SO. He plays key roles in system engineering for CMB-S4 and optical characterization of achromatic half-wave plates.

Arash Aminiمشاهده پروفایل

استاد

• Theoretical Signal Processing
• Statistical Signal Processing
• Graph Signal Processing
• +۵ مورد دیگر

Arash Amini is a Professor at the Electrical Engineering Department of Sharif University of Technology, Tehran, Iran. He received dual B.Sc. degrees in Electrical Engineering (Communications) and Petroleum Engineering (Reservoir) in 2005, followed by M.Sc. and Ph.D. degrees in Electrical Engineering (Signal Processing) in 2007 and 2011, respectively, all from Sharif University of Technology. During his doctoral studies, he spent a year (2009–2010) as a visiting scholar at the Biomedical Imaging Group (BIG), EPFL, Switzerland. Current Position: Professor (since May 2025) Previous Roles: Assistant Professor (2013–2018), Associate Professor (2018–2025), Researcher at BIG, EPFL (2011–2013) Editorial Role: Associate Editor for IEEE Signal Processing Letters (2014–2018) Research Interests : Theoretical and Statistical Signal Processing Graph Signal Processing Large Language Models Signal Processing for Communications Compressed Sensing Recent Publication Trends focus on graph signal processing, compressed sensing, biomedical imaging, and mathematical optimization. Key subfields include harmonic retrieval, subspace-informed matrix completion, sparsity-driven algorithms, and AI alignment benchmarks. Scientific Awards : Silver medal at the International Mathematical Olympiad (IMO2000) Advising includes 15+ Ph.D. and Master’s students, with collaborative projects involving institutions like EPFL and researchers such as Prof. F. Marvasti and Prof. S. Rini.

Ehsan Miandjiمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Ehsan Miandji is an Assistant Professor and Docent at Linköping University's Department of Science and Technology (ITN), part of the Faculty of Science and Engineering. His research focuses on computer graphics, computer vision, and machine learning, with a particular emphasis on BRDF modeling, light field imaging, compressed sensing, and sparse representation techniques. He is affiliated with the Computer Graphics and Image Processing group and the Wallenberg Autonomous Systems Program (WASP). His work spans both theoretical advancements and applied methodologies in visual data processing. Recent research includes optimizing BRDF acquisition via FROST-BRDF, advancing multidimensional compressed sensing for spectral light fields, and developing sparse representation frameworks for bidirectional texture functions (BTF). Miandji collaborates with interdisciplinary teams within the Media and Information Technology (MIT) division, contributing to projects that bridge computational imaging, algorithm design, and real-world applications. His publications reflect a strong commitment to pushing boundaries in visual data compression, rendering efficiency, and perceptual quality assessment of material models.

• Computational Fluid Dynamics
• Experimental Aerodynamics
• Aeroballistics
• +۲ مورد دیگر

Dr. Mark Finnis is a Principal Research Fellow at Cranfield University's School of Defence and Security, specializing in fluid dynamics and aeroballistics within the Centre for Defence Engineering. His research encompasses experimental and computational fluid mechanics with applications in defence systems. Key research areas include wind tunnel design and commissioning, experimental aerodynamics for motorsport and aerospace applications, and aeroballistics. Recent work focuses on synthetic aperture radar (SAR) technologies for through-wall imaging and moving target detection. Finnis holds extensive expertise in 3D particle image velocimetry and radar signal processing. His aerodynamic research examines ground-effect diffusers and flow control mechanisms, while radar-related publications advance multistatic SAR systems and polarimetric imaging techniques. With Cranfield since 1984, Finnis completed his PhD on boundary-layer stability at Cranfield Institute of Technology in 1993. His consultancy portfolio includes wind tunnel projects for motorsport clients in the UK and US.

Jacob Guntherمشاهده پروفایل

استاد

• Signal Processing
• Statistical Signal Processing
• Array Processing
• +۷ مورد دیگر

Jacob Gunther is a Professor in the Department of Electrical and Computer Engineering at Utah State University's College of Engineering. He specializes in signal processing, statistical signal processing, and communication theory, with applications in transportation systems, SAR navigation, and adaptive filtering. PhD , Electrical Engineering, Brigham Young University, 1998 MS , Electrical Engineering, Brigham Young University, 1994 BS , Electrical Engineering, Brigham Young University, 1994 Gunther's research spans signal processing for electric bus charging optimization, GPS-denied navigation using SAR, and advanced filtering techniques. His work includes hyperspectral imagery unmixing, communication systems, and sparse signal recovery. Recent publications focus on electric bus fleet charging cost minimization, SAR-based navigation, and adaptive filtering algorithms. Notable trends include integration of optimization theory with transportation electrification and robust positioning systems. Teacher of the Year (2015, 2005, 2004) IEEE Senior Member (2011) Advisor of the Year (2010, 2003) Researcher of the Year (2004) Award of Merit (2010, 2007) Gunther has mentored numerous graduate students in Electrical and Computer Engineering, including Jordan Johnson and Daniel Mortensen. His teaching includes Discrete-Time Systems and Signals and Convex Optimization , with a focus on practical signal processing applications.

• Computer Vision
• Computational Imaging
• Non-Line-of-Sight Imaging
• +۷ مورد دیگر

Suren Jayasuriya is a highly active faculty-level researcher in computer vision and computational imaging, with 79 refereed publications (2014-2025) in leading venues such as CVPR, ICCV, ECCV, NeurIPS, IEEE TPAMI and ACM TOG. His work integrates physics-based models with modern machine learning to tackle problems like non-line-of-sight imaging, atmospheric turbulence removal, neural 3D reconstruction, and speech enhancement, while also advancing STEM education through AI. Education & Affiliation: No explicit institutional details are contained in the supplied DBLP extract; however, the sustained publication record and extensive student mentoring indicate a tenured or tenure-track professorship within an engineering or computing department. Research Interests: Jayasuriya’s interests sit at the intersection of computer vision, computational photography, and physics-based machine learning . He develops algorithms that exploit optical and acoustic phenomena for tasks such as seeing around corners, correcting atmospheric distortion, and reconstructing 3D scenes from sparse sonar or radar data. Additional threads include energy-efficient tracking, robust speech processing, and AI-supported pedagogical innovation. Publication Trends: Across the 15 most recent works (2022-2025) his papers exhibit a clear thematic arc: neural representations for multimodal fusion (camera-sonar, radar-vision), unsupervised video restoration under atmospheric turbulence, attention-driven non-line-of-sight tracking, and evaluation of large multimodal models for perceptual reasoning. These contributions simultaneously advance core vision methodologies and demonstrate interdisciplinary applications spanning robotics, environmental monitoring, underwater perception, and education. Students & Mentoring: Jayasuriya has advised an active cohort of graduate researchers who appear repeatedly as co-authors, including Sreenithy Chandran, Ripon Kumar Saha, Albert W. Reed, Joshua D. Rego, Dehao Qin, Jianwei Zhang, Odrika Iqbal, Sameeksha Katoch, Md. Farhan Tasnim Oshim, and Shenbagaraj Kannapiran, among others. Scientific Awards: No awards are listed in the supplied extract; the awards field is left empty. Labs & Teams: While specific laboratory names are not disclosed, the collaborative scope—encompassing hardware-software co-design, field deployments, and educational outreach—suggests he leads or co-leads a research group with access to specialized imaging and robotics facilities.

Christoph Kellerمشاهده پروفایل

استاد مهمان

• Exoplanets
• Instrumentation
• Solar Physics
• +۵ مورد دیگر

Christoph Keller is a Visiting Professor at Leiden Observatory (Leiden University) and serves as Director of Science at Lowell Observatory. His career spans roles at the National Solar Observatory, Utrecht University, and Leiden University, where he focused on high-accuracy polarimetry for astronomy and environmental sensing, exoplanetary systems, solar physics, remote sensing, and interdisciplinary instrumentation. Education: PhD in Physics from ETH Zürich (1992), MSc in Physics from ETH Zürich (1988) Research Interests: His work bridges exoplanet characterization, innovative optical instruments, polarized light analysis, and remote sensing of planetary atmospheres. He has pioneered techniques in holographic aperture masking, vector-apodizing phase plates, and spectropolarimetry for detecting biosignatures like homochirality. Scientific Contributions: Recent publications highlight advancements in high-contrast imaging, adaptive optics, and life detection via polarization. His projects include the Planetary Camera and Spectrograph (PCS) for the ELT and the Life Signature Detection polarimeter (LSDpol). Scientific Awards: Friedrich Wilhelm Bessel Award for astrophysics Advising and Projects: Supervised numerous PhD students and postdocs, including Rico Landman, Fedde Fagginger Auer, and Evangelia Kleisioti. Led projects like SPHERE+, LEXI, and the European Solar Telescope. His work integrates machine learning, optical design, and space-based instruments for exoplanet and solar research.

Vasiliki Stergiopoulouمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Signal Processing
• Audiovisual Communications
• +۳ مورد دیگر

Vasiliki Stergiopoulou is a Researcher at EPFL, holding roles in multiple laboratories: Postdoctoral Researcher at the Galatea Laboratory (School of Engineering, IEM), Scientist at the Audiovisual Communications Laboratory (LCAV, School of Computer and Communication Sciences), and Guest at the IMAGING-GE group. Her work focuses on computational imaging, microscopy techniques, and signal processing, with contributions to third-harmonic generation imaging, fluorescence microscopy optimization, and holographic illumination methods. She has published extensively on topics like super-resolution microscopy and denoising algorithms. Vasiliki collaborates across interdisciplinary teams at EPFL, contributing to advancements in biomedical imaging and optical technologies.

Riadh Munjyمشاهده پروفایل

استاد

• Photogrammetry
• Unmanned Aircraft Systems (UAS) Mapping
• Remote Sensing
• +۵ مورد دیگر

Riadh Munjy serves as a Professor in Geomatics Engineering at California State University, Fresno's Lyles College of Engineering, where he teaches core courses including CE 205 (Computing in Engineering Analysis), GME 123 (Stereo-Photogrammetry), and GME 125 (Analytical Photogrammetry). His academic foundation includes a Ph.D. in Civil Engineering (1982), M.S. in Applied Mathematics (1981), and M.S.C.E. (1979) from the University of Washington, complemented by a B.S. in Civil Engineering from the University of Baghdad (1976). Ph.D., Civil Engineering, University of Washington (1979-1982) M.S., Applied Mathematics, University of Washington (1980-1981) M.S.C.E., Civil Engineering, University of Washington (1978-1979) B.S., Civil Engineering, University of Baghdad (1972-1976) Professor Munjy's research centers on advancing photogrammetric methodologies with particular emphasis on Unmanned Aircraft Systems (UAS) mapping accuracy, sensor calibration, and novel processing techniques for LIDAR and IFSAR data. His work bridges theoretical mathematics with practical applications in transportation infrastructure, disaster response, and terrain modeling, consistently addressing industry pain points like rolling shutter effects in UAV imagery and LIDAR strip adjustment challenges. He has pioneered standards for GPS-controlled photogrammetry adopted by Caltrans and contributed foundational chapters to the ASPRS Manual of Photogrammetry. Analysis of his 15 most recent publications (2010-2020) reveals a strategic evolution from traditional photogrammetric techniques toward UAS-centric workflows, with increasing focus on automated processing, accuracy validation, and integration of multi-sensor data. His research consistently targets real-world implementation barriers in transportation mapping and corridor projects, demonstrating strong industry relevance through Caltrans collaborations and ASPRS leadership. American Society of Photogrammetry and Remote Sensing Fellow Award (2020) ASPRS Fairchild Award (2014) Caltrans Research Innovation Award (2004) Five School of Engineering Research Excellence Awards (1992, 1996, 1998, 2002, 2003) ASPRS Meritorious Service Awards (1992, 1997) Halliburton Research Award (1992) Professor Munjy's research program has been consistently supported through Caltrans-funded projects including GPS Photogrammetry (2005), UAS Research (2018), and corridor mapping standards development. His advisory impact extends through co-authoring the ASPRS Manual of Photogrammetry and mentoring numerous conference presentations, though formal student lists aren't documented. Current initiatives focus on sUAS data accuracy validation and rolling shutter effect mitigation, positioning his work at the forefront of evolving UAV mapping regulations.

Chrysoula Tsogkaمشاهده پروفایل

استاد

• Numerical Methods
• Wave Propagation
• Inverse Problems
• +۳ مورد دیگر

Chrysoula Tsogka is a Professor in the Department of Applied Mathematics at the University of California, Merced (since 2019). Previously, she held academic positions at the University of Crete (2007-2019), including Professor (2014-2019) and Associate Professor (2007-2014). She also served as a Visiting Professor at Stanford University (2016-2019) and a Project Scientist at UC Merced (2017-2019). Her research focuses on numerical methods for wave propagation, inverse problems, and imaging in complex media, with applications in geophysics, radar, and structural health monitoring. Education: Ph.D. in Applied Mathematics, University Paris IX (Dauphine), France (1999) M.S. in Applied Mathematics, University Paris IX, France (1996) B.Eng. in Chemical Engineering, National Technical University of Athens, Greece (1995) Research Interests: Tsogka specializes in developing and analyzing numerical methods for direct and inverse wave propagation problems, including coherent interferometry, passive ambient noise correlation-based imaging, and sparse recovery techniques. Her work addresses challenges in imaging through complex and random media, such as turbulence and scattering effects, with applications in synthetic aperture radar (SAR), satellite imaging, and non-destructive evaluation of materials. She emphasizes robustness in imaging systems under environmental uncertainties. Grants and Awards: ERC Starting Grant (2010-2015) SIGEST Paper Award (2009) Principal Investigator (PI) on Air Force Office of Scientific Research grants (e.g., FA9550-21-0196, $284K, 2021-2024) Advising and Mentorship: Tsogka has supervised six Ph.D. students and advised numerous MSc and Ph.D. committees. Her current team includes a graduate student and four committee members. She has also mentored postdoctoral researchers and undergraduate students in her research groups. Labs and Teams: As coordinator of the Imaging and Sensing SMaRT team at UC Merced, she leads a group of 6 faculty, 2 postdocs, and 12 graduate students. The team focuses on forward/inverse imaging problems, optimization, and uncertainty quantification, with weekly seminars alternating between the Optimization and Waves groups. They collaborate on data-driven methods for remote sensing and radar applications.

• Signal Processing
• Radar Systems
• Electromagnetic Modelling
• +۳ مورد دیگر

Javier Rosado Sanz is an academic at the Universidad de Alcalá, holding the position of Profesor Ayudante Doctor in the Signal Theory and Communications Department. His research focuses on advanced radar systems, particularly passive radar technologies leveraging existing broadcast signals (DVB-T/S, GPS, etc.) for target detection and tracking. He leads work in the AES3 group, developing smart sensor networks for coastal and urban surveillance, emphasizing acoustic and electromagnetic sensing. He earned his PhD from Universidad de Alcalá in 2021 with a thesis on antenna array configurations and electromagnetic target modeling for passive radar enhancement. His technical expertise spans antenna design optimization, clutter mitigation algorithms, and multi-static radar architectures. Recent research emphasizes UAV/drone detection, 2D/4D tracking systems, and adaptive beamforming techniques. His work bridges theoretical signal processing with practical implementations in maritime/coastal surveillance and smart city sensor networks. Key innovations include reflectarray antenna designs and decentralized detection frameworks for distributed sensor arrays. Publications highlight contributions to passive radar performance under challenging environments (e.g., wind farms, urban clutter) and novel applications like oil spill classification using SAR data. Collaborations involve experimental validations of DVB-based radar systems and conductivity analysis of composite materials used in UAVs.

Georgios Exarchakisمشاهده پروفایل

مدرس

• Machine Learning
• Theoretical Neuroscience
• Data Science
• +۱۶ مورد دیگر

Georgios Exarchakis is a Lecturer at the University of Bath, specializing in Machine Learning, Theoretical Neuroscience, and Data Science. His research emphasizes transparent and interpretable modeling, with applications in Neuromorphic Engineering, Health Science, and Quantum Chemistry. He has held research positions at IHU Strasbourg, Institut de la Vision, and École Normale Supérieure, and earned his PhD from the University of Oldenburg. Education: Dr. rer. nat. in Machine Learning, 2016, Carl von Ossietzky University of Oldenburg M.Sc. in Computational Science, 2012, Goethe University Frankfurt Diploma in Mathematics, 2008, Aristotle University of Thessaloniki His research interests span Interpretable Machine Learning, Invariant Representations, Sparse Coding, Wavelet Scattering, Probabilistic Models, and Deep Learning . He investigates how models can extract meaningful, stable features from complex data, drawing inspiration from biological systems and theoretical neuroscience. His work often bridges theory and application, particularly in quantum chemistry and neurotechnology. His recent publications highlight a strong focus on efficient and interpretable models , including wavelet scattering for molecular property prediction, discrete sparse coding, and clustering algorithms for event-based vision. The articles show a consistent theme of developing mathematically grounded, invariant, and scalable methods for data analysis. He is the developer or a key contributor to open-source libraries such as Kymatio (Scattering Transforms in Python) and ProSper (Probabilistic Sparse Coding), which facilitate the use of advanced signal processing and learning techniques in the research community. Georgios has taught Machine Learning courses at the University of Strasbourg, École Polytechnique, and Oldenburg. He has collaborated with leading researchers like Stéphane Mallat and Jörg Lücke. His work is published in top-tier venues including CVPR, NIPS, JMLR, and JCP.