جست‌وجوی استاد و پژوهشگر

Henry Kangمشاهده پروفایل

دانشیار

• Computer Graphics
• Data Visualization
• Computer Animation
• +۳ مورد دیگر

Henry Kang is an Associate Professor in the Department of Computer Science at the University of Missouri–St. Louis, College of Arts and Sciences. His expertise spans computer graphics, data visualization, and computational art, with extensive experience in full-stack web development and programming frameworks. Education: Ph.D. in Computer Science, Korea Advanced Institute of Science and Technology (2002) Research Interests: Kang's work focuses on computer graphics, non-photorealistic rendering, and data visualization. Key projects include coherence-enhancing filtering, stereoscopic 3D line drawing, and emotion-driven image recoloring. He integrates machine learning and GPU computing for real-time scene navigation and artistic effects. Publication Trends: His research emphasizes texture filtering, computational art, and perceptual modeling. Recent work includes Gaussian image binarization (2021) and coherence-enhancing GPU filtering (2018), while earlier contributions explore stereoscopic depth perception (2013) and directional stippling (2011). Contact: Email: kangh@umsl.edu Phone: (314) 516-5841 Office: 318 ESH

• Electronic Design Automation
• VLSI Design
• Computer Systems
• +۷ مورد دیگر

Shinji Kimura is a Professor at Waseda University's Faculty of Science and Engineering, specializing in VLSI design and electronic systems. He holds a Doctor of Engineering from Kyoto University and has been with Waseda since 2002, previously serving as Associate Professor at Nara Institute of Science and Technology (1993–2002) and Assistant Professor at Kobe University (1985–1993). Kimura's research spans low-power circuit design , approximate computing , FPGA optimization , video coding (HEVC) , and hardware acceleration for AI . His work focuses on energy-efficient architectures for applications like neural networks, computer vision, and ultra-high-definition video processing. Recent publications emphasize hardware-efficient multipliers, neural network compression, and 3D-stacked memory systems. Awards include the LSI IP Design Award (2000, 1999) and the Information Processing Society of Japan Encouragement Award (1993). He leads projects on HEVC encoding/decoding, non-volatile memory optimization, and 3D integrated circuits, with VLSI implementations achieving real-time 8K video processing.

James Shacklefordمشاهده پروفایل

دانشیار

• Medical Image Processing
• High Performance Computing
• Embedded Systems
• +۲ مورد دیگر

James Shackleford serves as Associate Professor and Interim Associate Dean for Enrollment Management and Graduate Education in the Department of Electrical and Computer Engineering at Drexel University. His research bridges medical image processing, high performance computing, and emerging neuromorphic architectures with significant contributions to radiation therapy applications. Education: PhD in Electrical Engineering, Drexel University, 2011 MS in Electrical Engineering, Drexel University BS in Electrical Engineering, Drexel University Research Focus: Professor Shackleford's work centers on GPU-accelerated medical image registration (forming the core of the open-source Plastimatch software), real-time tumor motion management for radiation therapy, and digital spiking neuromorphic systems . His research integrates computer vision, machine learning, and embedded systems to solve clinical imaging challenges. Publication Trends: Recent work (2020-2024) reveals dual research trajectories: (1) advancing deformable image registration through CycleGAN-based domain adaptation for CT auto-segmentation in radiation oncology, and (2) pioneering neuromorphic computing with configurable hardware architectures, dataflow-based compilers, and resource-aware neural network mapping. These streams converge on high-performance solutions for medical imaging and efficient neural processing.

Li Leidaمشاهده پروفایل

استاد

• Computer Vision
• Image and Video Quality Assessment
• Computational Aesthetics
• +۳ مورد دیگر

Professor Li Leida is a National Young Talent and doctoral supervisor at Xidian University's School of Artificial Intelligence. His research focuses on computer vision, image/video quality assessment, computational aesthetics, and visual sentiment analysis. He leads the Brain-like Visual Perception and Evaluation Laboratory and collaborates with industry partners like OPPO, Huawei, and Tencent. He has published over 90 papers in top venues like TPAMI and CVPR, with 6 highly cited papers and 8,400+ citations. He serves as deputy editor-in-chief of IEEE Transactions on Image Processing and is a senior member of IEEE/CCF/CSIG. Education & Academic Roles: PhD Supervisor/Master Supervisor Member of OPPO Computational Imaging Expert Committee Area Chair for ACM Multimedia 2025 Research Interests: Dr. Li's work bridges theoretical computer vision with practical applications in imaging quality, aesthetic evaluation, and sentiment analysis. His team develops benchmarks like AesBench for large model evaluation. Recent advancements include scanned image enhancement algorithms and personalized aesthetic datasets like PARA. Industry Impact: OPPO ColorOS integration Live broadcast camera technologies DXOMARK Portrait Quality Assessment Challenge winner (2024) Awards & Recognition: First Prize in Shaanxi Natural Science (2021) 2022 OPPO Industry Collaboration Award Stanford's Top 2% Global Scientists (2023-2024) Grants & Labs: Secured 5 National Natural Science Foundation projects. Active in lab collaborations with companies like DJI and Huawei's imaging teams.

• Signal Processing
• Image Processing
• Smart Environments
• +۶ مورد دیگر

Manuel Reis is an Associate Professor with Agregação at the University of Trás-os-Montes e Alto Douro (UTAD), Portugal, in the Electrical Engineering Department. His research focuses on signal/image processing, smart environment systems, and multimedia education technologies. He is affiliated with the Institute of Electronics and Telematics Engineering of Aveiro (IEETA) and collaborates with Brazil's IFPA-Santarém in computing education research. Education: Bachelor's in Electrical Engineering (University of Trás-os-Montes e Alto Douro, 1991) Master's in Electronics and Telecommunications (University of Aveiro, 1996) PhD in Electrical Engineering (University of Aveiro, 2001) Research Interests: Signal & Image Processing Cybersecurity for IoT and Smart Environments Education Technology (e-learning, multimedia tools) 5G and Edge Computing Applications Artificial Intelligence in Agriculture and Healthcare Recent articles highlight his work on cybersecurity in connected vehicles, federated learning for IoT security, and IoT-based systems for drowsiness detection. He has contributed to projects involving smart city infrastructure, sustainable waste management, and low-cost biomedical devices. His educational research explores e-learning frameworks, student engagement metrics, and innovative teaching methodologies in engineering education. Lab/Affiliations: Institute of Electronics and Telematics Engineering of Aveiro (IEETA) Multidisciplinary Research Group IFPA-Santarém-Brazil (Computing in Education)

Gerard de Haanمشاهده پروفایل

استاد

• Video Signal Processing
• Remote Photoplethysmography (rPPG)
• Motion Robust Signal Processing
• +۴ مورد دیگر

Gerard de Haan is a Professor of Electronic Systems at the Department of Electrical Engineering , Eindhoven University of Technology (TU/e). His research focuses on video signal processing, particularly in multimedia systems and video health monitoring , aiming to enhance image quality and enable accurate sensing of vital signals amidst motion artifacts. He has documented his research in 4 books, 3 book chapters, approximately 200 papers, and over 200 patent applications, leading to commercially available ICs. De Haan has served on program committees of international conferences and as a guest editor for journals including Elsevier, IEEE, and Springer. Education : BSc, MSc, PhD in Electrical Engineering from Delft University of Technology (1977, 1979, 1992) Professional Roles : Lead researcher at Philips Research (1979–present), Full Professor at TU/e (2000–present) His research interests span noise and artifact reduction , video format conversion , display-specific processing , and enabling technologies like motion estimation and object detection . Articles highlight advancements in rPPG motion robustness , blood volume pulse signature analysis , and remote SpO2 monitoring . Scientific awards include his appointment as Fellow at Philips Research Eindhoven in 2000.

Martin Reissleinمشاهده پروفایل

استاد

• Communication Networks
• Engineering Education
• Multimedia Networking
• +۵ مورد دیگر

Dr. Martin Reisslein is a Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU), where he also serves as Program Chair of Computer Engineering. He earned his Ph.D. in Systems Engineering from the University of Pennsylvania (1998) and holds degrees from the University of Pennsylvania and Fachhochschule Dieburg, Germany. His research focuses on communication networks (e.g., 5G, optical networks, software-defined networking) and engineering education, with over 200 journal articles and 60 conference papers. He has led NSF-funded projects on network architecture optimization and K-12 engineering education. Education : Ph.D. (Systems Engineering, UPenn, 1998), M.S.E. (Electrical Engineering, UPenn, 1996), Dipl.-Ing. (FH) (Electrical Engineering, Fachhochschule Dieburg, 1994) Awards : NSF Career Award (2002), IEEE Fellow (2014), Bessel Research Award (2015), DRESDEN Fellowship (2016) Editorial Roles : Co-Editor-in-Chief of Optical Switching and Networking , Associate Editor for multiple IEEE journals His research spans communication networks (e.g., multimedia networking, optical systems) and engineering education (e.g., K-12 outreach, instructional design). Recent articles address cloud computing, 5G architectures, and cybersecurity in satellite systems. He teaches courses such as Communication Networks and oversees graduate research.

• Video Coding
• Machine Learning in Video Compression
• Energy-Efficient Encoding
• +۳ مورد دیگر

Alexandre Mercat is an Assistant Professor in the Department of Computer Engineering at Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on video coding, energy-efficient encoding, and real-time multimedia systems. He leads projects on open-source video encoders and standards, including contributions to HEVC, VVC, and V-PCC technologies. His work emphasizes machine learning integration, low-power hardware optimizations, and scalable distributed encoding frameworks. Key technical interests include improving video compression efficiency through algorithmic innovations, developing open-source tools like the UVG dataset and Kvazaar encoder, and addressing challenges in volumetric video communication and 3D point cloud encoding. His research spans theoretical algorithm design to practical implementations, with applications in virtual reality, live streaming, and edge computing. Recent projects include real-time saliency-guided video coding frameworks, energy reduction techniques for HDR streaming, and multi-layer VVC coding schemes for hybrid machine-human consumption. He also explores FPGA acceleration and parallelization strategies for distributed video encoding systems. No scientific awards are explicitly mentioned in the provided texts. While no formal advisees are listed, his research group likely involves students through open-source development and collaborative projects. His work integrates closely with industry standards bodies and open-source communities, emphasizing reproducible evaluation frameworks and end-to-end software tools.

• Optoelectronics
• Liquid Crystal Technologies
• Visible Light Communication (VLC)
• +۴ مورد دیگر

Juan Carlos Torres Zafra is a Visiting Professor at Universidad Carlos III de Madrid. His research focuses on optoelectronics, liquid crystal technologies, visible light communication (VLC), and sensor systems. His work spans interdisciplinary areas including energy-harvesting IoT nodes, indoor positioning systems, and optical communication interfaces for high-definition media transmission. Torres Zafra has contributed to advancements in semiconductor materials (e.g., perovskites), low-cost sensor networks, and hybrid RF-VLC positioning systems. His research also extends to educational technology, exploring tools like Telegram and Google Workspace for improving student engagement in cybersecurity engineering programs. Notable projects include the GUTI group's work on optical vortices using liquid crystal devices and the development of tunable resonators based on liquid crystal capacitance. His publications from 2020–2025 highlight trends in VLC system optimization, AI-driven disinformation detection (SmartVote-AI initiative), and medical studies on amyloidosis therapy outcomes. Torres Zafra’s work often emphasizes practical applications in robotics, automotive systems, and sustainable energy solutions. While no formal awards are listed, his extensive publication record (over 80 entries from 2004–2025) reflects sustained contributions to photonics, sensor engineering, and liquid crystal device innovation. His research integrates hardware design, algorithm development, and material science to address challenges in modern communication systems and assistive technologies for visually impaired patients.

• Optoelectronics
• Visible Light Communication (VLC)
• Internet of Things (IoT)
• +۳ مورد دیگر

Carlos Iván Del Valle Morales is an Adjunct Professor at Carlos III University of Madrid. His research focuses on visible light communication (VLC), Li-Fi systems, and energy-harvesting technologies. He leads projects in the Grupo Universitario de Tecnologías de Identificación (GUTI), developing optical communication interfaces for IoT, high-power LED characterization, and indoor positioning systems using trilateration and angular diversity. Recent work emphasizes perovskite photovoltaic applications for self-powered IoT nodes and hybrid RF-VLC positioning systems. His publications span 2020–2024, addressing energy efficiency, bandwidth optimization, and hardware design for VLC systems in automotive and multimedia contexts.

Nabil J. Sarhanمشاهده پروفایل

دانشیار

• Computer Systems
• Deep Learning
• Video Streaming
• +۵ مورد دیگر

Dr. Nabil J. Sarhan is an Associate Professor of Electrical and Computer Engineering at Wayne State University , where he directs the Computer Systems and Deep Learning Research Laboratory and the interdisciplinary M.S. Program in Artificial Intelligence (Systems and Hardware Track) . He previously served as Graduate Program Director and College of Engineering Faculty Assembly Chair. Education : Ph.D. and M.S. in Computer Science and Engineering from Pennsylvania State University (GPA 4.0), B.S. in Electrical Engineering from Jordan University of Science and Technology Internationally recognized for expertise in computer systems , deep learning , video streaming , and AI hardware accelerators , Dr. Sarhan's research bridges theoretical and applied domains in automated video surveillance and medical AI systems . His work includes novel approaches to epileptic seizure prediction , energy-efficient neural network accelerators , and mixed-signal circuit design for edge computing. Recent grant highlights include a $418,907 National Science Foundation award for reconfigurable AI accelerator development. His patent (US Patent 9,313,463) on automated video surveillance systems demonstrates practical technology impact. Scientific Awards IEEE SEM Outstanding Professional of the Year Wayne State President's Excellence in Teaching Award College of Engineering Excellence in Teaching Award WSU Academy of Teachers (2022) IEEE Senior Member (2016) As an educational reform leader , he has chaired over 20 ABET accreditation panels in Saudi Arabia and Palestine, evaluating programs in electrical engineering, computer science, and software engineering. His intellectual property expertise spans significant patent infringement cases including ACQIS v. ASUSTek and VideoShare v. Google .

Alexis Kirkeمشاهده پروفایل

عضو هیئت علمی

• Music Technology
• Computational Music
• Human-Computer Interaction
• +۳ مورد دیگر

Dr Alexis Kirke is a Senior Research Fellow in Computer Music at the School of Art, Design and Architecture (Faculty of Arts, Humanities and Business) at the University of Plymouth. As a composer-in-residence, he specializes in interdisciplinary research at the intersection of music, computing, and healthcare. His work includes developing adaptive music systems like RadioMe for dementia care, applying quantum computing to music composition, and exploring affective computing through brain-computer interfaces. Teaching roles include associate lecturer positions in modules such as Collaborative Practice (BA Sound and Music Production), Psychology (BA Music), and MRes Computer Music. He has supervised five PhD students as a second supervisor and served as an internal PhD examiner. His research focuses on algorithmic composition, music technology for healthcare, quantum computing applications in music, and multi-agent systems inspired by natural phenomena like humpback whale song evolution. Recent projects include the Plymouth Marine Institute collaboration and the Cloud Chamber performance involving real-time interaction with subatomic particles. Key contributions include innovative systems like RadioMe, which combines adaptive radio with reminder systems for dementia patients, and Q-Muse, a quantum computer music system. His work bridges computational creativity with human-centric applications, emphasizing ethical and accessible technology. Grants & Awards: No specific awards listed, but active in collaborative research projects Lab/Teams: Plymouth Marine Institute, Cloud Chamber Project

• Energy-efficient circuits and systems
• Low-power wireless sensors
• Multimedia terminals
• +۳ مورد دیگر

Anantha P. Chandrakasan is MIT's Provost and Vannevar Bush Professor of Electrical Engineering and Computer Science. He leads strategic initiatives such as the MIT Climate and Sustainability Consortium, MIT AI Hardware Program, and MIT-IBM Watson AI Lab. As Chief Innovation and Strategy Officer, he oversees MIT HEALS, MGAIC, and MITHIC. Previously, he served as MIT School of Engineering Dean (2011-2025) and director of MIT Microsystems Technology Laboratories (2006-2011). He earned all degrees (B.S., M.S., Ph.D.) in EECS from UC Berkeley (1989-1994). His research focuses on energy-efficient circuits, low-power wireless sensors, and emerging technologies. Key projects include implantable medical devices, secure AI hardware, and THz communication systems. He pioneered the Schwarzman College of Computing, reshaping MIT’s academic structure. Recipient of the 2022 IEEE Mildred Dresselhaus Medal, he holds leadership roles in multiple MIT-industry partnerships. Notable collaborations include Ericsson (5G networks), Takeda (healthcare), and Accenture (industry-technology convergence). His academic advising includes fostering interdisciplinary programs like the MIT Quest for Intelligence and postdoctoral fellowships. He champions diversity through initiatives like the Faculty Gender Equity Committee and Daniel J. Riccio Graduate Engineering Leadership Program. Labs/Teams: Leads MIT's Office of Innovation & Strategy, oversees Microsystems Technology Laboratories, and co-chairs the MIT-GE Vernova Energy & Climate Alliance. Key hardware projects include conformable ultrasound patches and battery-free IoT devices.

Marco Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Nasser Kehtarnavazمشاهده پروفایل

استاد

• Signal and Image Processing
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor and Director of the Embedded Machine Learning (EML) Laboratory at the University of Texas at Dallas's Erik Jonsson School of Engineering and Computer Science. He holds a PhD from Rice University (1987) and has over 40 years of academic and industrial experience. His research focuses on signal/image processing, machine learning, biomedical applications, and real-time embedded systems. He has authored/co-authored 11 books and over 450 publications, and supervised 37 PhD and 35 MS students. Leadership roles: Editor-in-Chief of Journal of Real-Time Image Processing , SPIE Conference Chair, IEEE Fellow (2013), AAIA Fellow Industrial collaborations: Texas Instruments, AT&T Bell Labs, US Army TACOM Research Lab Key contributions: Smartphone-based signal processing labs, hearing aid amplification algorithms, and FPGA-based embedded systems Research interests include real-time implementation on embedded processors, biomedical signal analysis, and smartphone-based laboratories. He has pioneered educational tools like anywhere-anytime labs using smartphones for signal processing education. Recipient of numerous awards including IEEE Region 5 Professional Leadership Award (2015), Outstanding Engineering Educator Award (2013), and community recognition from SPIE (2020-2021). His funded projects (62 as PI/Co-PI) span healthcare tech, energy systems, and semiconductor manufacturing. Director of the EML Lab and previously the Signal and Image Processing Lab. Teaches graduate courses like Machine Learning and Pattern Recognition and undergraduate courses in signals and systems.