جستجوی استاد راهنمای دانشگاهی

• Embedded Systems
• Multimedia Computing
• VLSI Design
• +۶ مورد دیگر

Professor Hua Harry Li serves in the Department of Computer Engineering at San José State University, teaching specialized courses including Embedded Hardware Systems (CMPE 242), Embedded Software (CMPE 244), and Embedded Wireless Systems (CMPE 245). His academic profile spans semiconductor physics, real-time control systems, and multimedia hardware implementation. Education Background: Doctor of Philosophy, University of Iowa, 1989 Master of Science in Electrical and Computer Engineering, University of Iowa, 1984 Bachelor of Science in Electronics Engineering, Tianjin University, 1981 Graduate Studies in Electronic/Electrical Engineering, Tsinghua University, 1982 Li's research integrates embedded systems with intelligent control methodologies , particularly focusing on fuzzy logic applications in semiconductor manufacturing and neural network implementations for real-time processing. His work bridges theoretical algorithms and hardware constraints, evidenced by publications on vision chips, wafer defect detection, and pH neutralization control systems. The Encyclopedia of Electrical and Electronics Engineering features his contributions to neural fuzzy control techniques for semiconductor equipment. Analysis of his 1993-2005 publications reveals three distinct phases: early neural network applications (1993-1994) in wafer manufacturing and radar systems, mid-career expansion into video compression and vision chips (1995-1997), and mature work on embedded video processing systems (2001-2005). This trajectory demonstrates consistent focus on hardware-constrained intelligent systems while adapting to emerging technologies in wireless communications and multimedia computing. Key Honors: Industrial Neural Network Award (1994 World Congress Neural Network Conference) Who’s Who in Science and Engineering (4th Edition, 1999) Who is Who in America (53rd Edition) Professor Li contributes significantly to academic discourse as Guest Editor for IEEE Transactions on CPMT Special Emerging Technology Section (1994, 1996) and encyclopedia contributor. While specific grant details aren't documented, his sustained publication record and industry-recognized awards indicate substantial research funding, particularly in semiconductor process control and embedded vision systems. His teaching portfolio reflects direct alignment with research expertise through advanced embedded systems courses. Current work appears focused on embedded multimedia hardware systems with practical applications in semiconductor manufacturing and real-time control, though explicit details about active research teams or laboratory facilities aren't provided in available materials.

• Functional Analysis (applied)
• Operator/Spectral Theory
• Compressed Sensing
• +۱۲ مورد دیگر

Professor Anders Christian Hansen is a leading mathematician at the University of Cambridge and University of Oslo , specializing in Functional Analysis , Artificial Intelligence , and Compressed Sensing . As head of the Applied Functional and Harmonic Analysis group and a Royal Society University Research Fellow , his work bridges foundational mathematics with cutting-edge data science. Current affiliations: University of Cambridge (since 2013), University of Oslo (Professor II since 2012) Previous roles: Von Karman Instructor (Caltech), Marie Curie Fellow (Vienna), Junior Research Fellow (Cambridge) Research Interests : Functional Analysis (applied), Operator/Spectral Theory, Compressed Sensing Artificial Intelligence, Computational Barriers, Foundations of Mathematics Medical Imaging, Inverse Problems, Numerical Analysis Article Trends : His recent work explores the limits of AI through the Solvability Complexity Index (SCI), revealing inherent instabilities in deep learning despite theoretical guarantees. Collaborations with B. Adcock and M. Colbrook address structured sampling and numerical algorithms for spectral problems in infinite dimensions. Scientific Awards : PROSE Award Finalist (2022) for Compressive Imaging Whitehead Prize (2019), IMA Prize (2018) Leverhulme Prize (2017) for contributions to applied analysis Advising & Grants : Supervised 17 PhD students and 8 postdocs. Active in grants related to medical imaging , AI safety , and computational mathematics .

Asim Smailagicمشاهده پروفایل

استاد پژوهش

• Wearable Computing
• Context-Aware Systems
• Healthcare Technology
• +۲ مورد دیگر

Asim Smailagic is Research Professor at Carnegie Mellon University directing the Laboratory for Intelligent Interactive Real-Time Computing Systems. His work develops wearable systems and virtual coaches for healthcare applications. Current research creates AI-assisted rehabilitation technologies, privacy-preserving medical imaging systems, and explainable AI for clinical decision support. His virtual coaches combine sensing, machine learning and human-computer interaction for personalized therapy. Recent publications address stroke rehabilitation assessment, algorithmic bias in medical imaging, and privacy-aware medical data compression. These innovations bridge AI with clinical applications for improved patient care. He received the Allen Newell Research Excellence Award, Carnegie Science Center Excellence Award, and leads projects funded by NSF, NIH and DoD.

Simon Heneinمشاهده پروفایل

دانشیار

• Micromechanical Design
• Precision Mechanisms
• Flexure Guidance Systems
• +۸ مورد دیگر

Simon Henein is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , leading the Micromechanical and Horological Design Laboratory (INSTANT-LAB) under the School of Engineering and Institute of Mechanical Engineering . Since 2024, he holds a Courtesy Appointment at the Digital Humanities Institute (DHI-GE) . He was Tenure Chairholder at Patek Philippe (2012–2020) and a Visiting Professor at the University of Lausanne's Centre d'études théâtrales (2020–2021). His research bridges micromechanical design and creative pedagogy , focusing on flexure mechanisms for aerospace, biomedical devices, and horology, alongside improvisational arts in education. He has authored over 25 peer-reviewed journal articles and 60+ conference papers , with 35 patents. Notable awards include the Best Teacher Award (2023) , OMEGA Scientific Prize (1996) , and Maillefer SA Award (1996) . His teaching involves 12 ECTS credits annually with over 200 students , including mentorship of 26 Master’s and 60 semester projects . He has directed 6 completed PhD theses and currently supervises 4 PhD students , co-supervising 1 more. His lab, INSTANT-LAB, comprises a dozen researchers focusing on centimeter-scale mechanisms with novel kinematics and technologies.

Ian Wassellمشاهده پروفایل

مدرس ارشد

• Wireless Communications
• Sensor Networks
• Cooperative and Relay Networks
• +۷ مورد دیگر

Dr. Ian Wassell is a University Senior Lecturer at the University of Cambridge's Department of Computer Science and Technology. He holds a PhD from the University of Southampton (1990) and BSc/BEng degrees from the University of Loughborough (1983). His research focuses on wireless communications, sensor networks, and electromagnetic propagation modeling. He has authored over 200 publications since 1999 and supervised 23 PhD and 6 MPhil students. Currently, he leads wireless communications research in the Digital Technology Group and is a Fellow of Churchill College. His research interests include broadband fixed wireless access (FWA), cooperative networks, MIMO systems, compressive sensing, and AI-driven radio propagation models. He teaches Digital Electronics and Hardware Practical Classes at the undergraduate level. Key contributions include developing data-driven propagation models, deep learning applications in wireless networks, and optimizing heterogeneous radio access networks. Dr. Wassell's work integrates theoretical advancements with practical systems, addressing challenges in high-speed rail communications, indoor/outdoor detection, and 6G IoT opportunities. His research bridges signal processing, machine learning, and wireless network design, emphasizing real-world deployment in infrastructure like tunnels and vehicles.

Zijian Wangمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Deep Learning
• Domain Adaptation
• +۵ مورد دیگر

Dr. Zijian Wang is a Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on improving vision model transferability, domain adaptation, and deep learning applications in computer vision and remote sensing. He holds a PhD in Computer Science from The University of Queensland (2023). Research interests include domain adaptation techniques, neural network generalization, unsupervised learning, and applications in satellite imagery analysis, vegetation segmentation, and disaster damage assessment. His work emphasizes practical solutions for open-world domain shifts and efficient data utilization. Recent publications explore transfer learning via feature space perturbation, neural network-based satellite image delivery (MIDNet), and benchmark datasets for model generalization (CIFAR-10-Warehouse). He collaborates on platforms like FluMA for influenza monitoring and RoadAtlas for infrastructure management. Currently advises PhD candidates in vegetation segmentation for Australian wheat data and crop health assessment. Active in top conferences (CVPR, ICCV, NeurIPS) and journals (IEEE TPAMI, TMM).

Ashley Stewartمشاهده پروفایل

پژوهشگر ارشد

• Neuroimaging
• Quantitative Susceptibility Mapping
• Magnetic Resonance Imaging
• +۳ مورد دیگر

Dr Ashley Stewart is a Research Fellow at the School of Electrical Engineering and Computer Science , University of Queensland , specializing in Quantitative Susceptibility Mapping (QSM) and Neuroimaging . Her work focuses on developing automated MRI analysis pipelines and improving clinical applications of neuroimaging techniques. Research Interests Development of automated neuroimaging pipelines (e.g., QSMxT, Neurodesk). Quantitative susceptibility mapping for traumatic brain injury and cortical iron quantification. MRI acceleration techniques (CAIPIRINHA, compressed sensing). Integration of deep learning in neuroimaging reconstruction. Clinical translation of QSM for oncology and neurodegenerative disorders. Current Funding 2025-2026: Precision Brain Susceptibility Mapping for Large-Scale Neurogenetic Research 2025-2026: Enabling the Clinical Translation of Neuroimaging Analyses through Automated Benchmarking

• Inverse Problems
• Applied and Computational Harmonic Analysis
• Statistical Signal Processing
• +۴ مورد دیگر

Dr Dimitrios Roxanas is a Teaching Professor in the Department of Mathematics and Statistics at the University of Sheffield, UK. He serves as the Undergraduate Research Experience Coordinator and Advisor for the Sheffield SIAM-IMA Student Chapter. His career includes roles as a University Teaching Associate (2019–2021) and University Teacher (2019 at Bath University) before joining Sheffield. He holds a PhD in Mathematics from the University of British Columbia (2017), an MSc in Applied Mathematics from the National Technical University of Athens (2011), and a BSc/MEng in Electrical and Computer Engineering (2009). His research focuses on applied mathematical analysis, with expertise in inverse problems, statistical signal processing, compressed sensing, and probability. He also investigates partial differential equations (PDEs), harmonic analysis, geometric analysis, and mathematical control theory. His work bridges pure and applied mathematics, addressing challenges in nonlinear PDEs and their applications to imaging and signal estimation. Publications highlight contributions to energy-critical heat equations, harmonic map dynamics, and global solution analysis. Teaching responsibilities include foundational courses in pure mathematics (e.g., analysis), metric spaces, and skills development in mathematics. He collaborates within the Analysis research group at Sheffield, advancing interdisciplinary research in mathematical sciences.

• Triboelectric Nanogenerators
• Energy Harvesting
• Textiles
• +۳ مورد دیگر

Akshaya Aliyana serves as a Research Associate at Heriot-Watt University's School of Textiles & Design in Edinburgh, United Kingdom, conducting cutting-edge research in triboelectric energy harvesting systems integrated with smart textiles. Her work directly supports UN Sustainable Development Goals for clean energy and sustainable industry innovation through practical textile-based energy solutions. Research Focus: Triboelectric Nanogenerators (TENGs) Textile-Based Energy Harvesting Wearable Electronics Integration Smart IoT Carpets and Sensors 3D-Printed Conductive Fabrics AI-Optimized Sensor Networks She pioneers material fabrication techniques including direct 3D printing of triboactive polymers and compression mechanics optimization to develop self-powered sensing systems for real-world textile applications. Her interdisciplinary approach bridges electrical engineering, materials science, and textile technology to create sustainable energy solutions. 2025 Publication Trends: Recent works demonstrate concentrated advancement in TENG standardization, flexible supercapacitor development, and AI-driven sensor optimization. Key themes include IoT-enabled energy harvesting carpets, twistable yarn supercapacitors, and performance evaluation frameworks—all targeting commercial implementation of textile-based energy systems. Scientific Recognition: No awards or honors documented in available records. Mentorship & Funding: No information available regarding graduate student supervision or research grant acquisition. Research Ecosystem: Collaborates with international teams across materials science and engineering disciplines; work aligns with Heriot-Watt's textile sustainability initiatives though specific laboratory names remain unstated in public profiles.

• Quantum Entanglement
• Photonics
• Quantum Information Science
• +۳ مورد دیگر

Natalia Herrera Valencia serves as a Research Associate at the Institute of Photonics and Quantum Sciences within Heriot-Watt University's School of Engineering & Physical Sciences. Her work centers on experimental quantum photonics, specifically manipulating high-dimensional quantum states for advanced information processing. Her research spans quantum entanglement engineering , photonic quantum computing , and noise-resilient quantum communication . Key specialties include structured light control, optical fiber networks for entanglement distribution, and tomographic reconstruction of complex quantum states. She investigates fundamental limits of quantum systems under losses and decoherence. Recent publications (2024-2025) reveal a concentrated effort in scalable quantum photonic architectures , with emphasis on multiplexed networks, inverse-designed optical circuits, and parallelized Bell tests. These works collectively advance high-dimensional quantum information protocols using integrated photonics platforms. Dr. Herrera Valencia actively collaborates with the quantum photonics group at Heriot-Watt, particularly within Mehul Malik's research team. Her experimental work leverages the university's state-of-the-art quantum optics facilities for generating and characterizing entangled photon states in complex media.

Pedro Porto Buarque de Gusmãoمشاهده پروفایل

مدرس

• Computer Vision
• Navigation under adverse conditions
• Sensor Fusion
• +۴ مورد دیگر

Pedro Porto Buarque de Gusmão is a Lecturer in Computer Science at the University of Surrey, affiliated with the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on Computer Vision, Navigation under adverse conditions, Sensor Fusion, and Distributed Machine Learning. He leads projects in federated learning, autonomous systems, and robotic navigation, contributing to frameworks like Flower, a widely used federated learning library. His work emphasizes advancing federated learning techniques for privacy-preserving distributed systems, with notable achievements including winning the First Prize in the NIST Privacy-Enhancing Technologies Prize Challenge (2023). His publications span conferences like ICCV, USENIX Security, and IEEE Transactions, addressing topics such as federated learning optimization, 3D lane detection, and thermal-inertial SLAM. Key contributions include developing methods like L-DAWA for federated visual learning and FedVal for federated model validation. His research also integrates multimodal sensor data for autonomous navigation systems, leveraging mmWave radar and thermal-inertial fusion. He actively contributes to open-source projects and collaborates with industry on privacy-aware machine learning solutions.

Fan Yangمشاهده پروفایل

استادیار مدعو

• Networking Protocols
• Internet Architecture
• Telecommunications
• +۳ مورد دیگر

Fan Yang is an Adjunct Assistant Professor at Boston University and the lead of Google’s networking protocol team. He specializes in internet architecture and protocol engineering, notably driving Google’s industry-first deployment of HTTP/3 over QUIC, which significantly improved latency for Google services. His research focuses on optimizing network protocols, satellite communications, and OFDM systems. Education: PhD in Computer Science from the University of Delaware (2015), where he worked in Professor Paul D. Amer’s Protocol Engineering Lab. Research Interests: Developing scalable internet protocols, enhancing TCP and multipath TCP performance, and applying advanced signal processing techniques to wireless communication challenges. His work bridges theoretical protocol design with real-world internet-scale deployment. Key technical contributions include QUIC protocol specifications, non-renegable selective acknowledgments (NR-SACKs), and channel estimation methods for OFDM systems. His publications span topics from HTTP/3 implementation to satellite-based TCP optimizations. Labs/Teams: Previously associated with the Protocol Engineering Lab at University of Delaware. Currently leads Google’s networking protocol team responsible for cutting-edge internet infrastructure projects.

• Deltas
• Geomorphology
• Climate Change
• +۷ مورد دیگر

Prof. Hans Middelkoop is a Professor of Physical Geography - Global Change Geomorphology at Utrecht University, affiliated with the Faculty of Geosciences. He leads the Water, Climate & Future Deltas Hub under the strategic theme Pathways to Sustainability, collaborating with interdisciplinary teams to design sustainable delta development pathways. His research focuses on delta dynamics, climate change impacts, river morphodynamics, and sediment management in regions like the Rhine-Meuse, Mekong, and Mississippi deltas. Key affiliations include the Netherlands Centre for River Studies and the Dutch Centre for River Research. Research interests span delta sustainability, subsidence, sediment transport, and the interplay between human activities and geomorphic processes. He has pioneered studies on adaptive management strategies, subsurface heterogeneity in dike stability, and the socio-ecological implications of delta development. Recent work addresses global change scenarios for deltas, sedimentation-enhancing strategies, and subsidence projections in the Mekong delta. He has published widely on fluvial systems, floodplain management, and the application of remote sensing and modeling tools. Over 100 peer-reviewed articles highlight his contributions to understanding deltaic processes and informing policy through integrated frameworks. Hub Leadership: Water, Climate & Future Deltas Hub (Pathways to Sustainability) Key Projects: Future Deltas initiative, Rhine sediment budget analyses, Mekong subsidence modeling Techniques: Remote sensing, numerical modeling, sediment budgeting, paleoflood reconstruction His work bridges academia, policy, and practice, emphasizing co-creation with stakeholders to achieve climate-resilient delta management. Current research explores pathways to sustainability through integrated socio-biophysical frameworks and adaptive governance strategies.

• Wireless Communications
• 5G/6G Networks
• Massive MIMO
• +۶ مورد دیگر

Maged Elkashlan is a Professor in the Department of Electronic Engineering at Queen Mary University of London, UK. He specializes in wireless communications, with a focus on 5G/6G systems, massive MIMO, reconfigurable intelligent surfaces (RIS), and ultra-reliable low-latency communication (URLLC). His research spans physical layer security, energy-efficient networks, and non-orthogonal multiple access (NOMA). He has authored over 200 papers in top-tier journals and conferences and holds editorial roles in IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, and others. He has supervised numerous PhD students, including current scholars working on RIS and cell-free MIMO. His teaching includes courses on digital signal processing, communication theory, and wireless communications at Queen Mary, the University of Sydney, and the University of New South Wales. He has organized major symposiums at IEEE ICC and VTC, and his work has been recognized through best paper awards and industry collaborations. Editorships: IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Transactions on Molecular, Biological and Multi-Scale Communications Key Research Areas: Cell-Free Massive MIMO, RIS-aided systems, NOMA, URLLC, physical layer security Recent Activities: Symposium co-chair for IEEE VTC 2018 and ICC 2018, guest editor of IEEE Communications Magazine special issues on millimeter-wave and green media Grants & Funding: China Scholarship Council (CSC) for PhD students, various research grants supporting RIS and URLLC projects

Ebroul Izquierdoمشاهده پروفایل

استاد

• Multimedia Signal Processing
• Machine Vision
• Image Processing
• +۷ مورد دیگر

Professor Ebroul Izquierdo holds a position at Queen Mary University of London's School of Electronic Engineering and Computer Science. His academic background includes a BSc, MSc, and PhD. He specializes in Multimedia Signal Processing, Machine Vision, and Mathematical models in image processing. His research focuses on video coding, computer vision, AI applications in agriculture, and security systems. He leads major grants such as the £305k Smart Farm project and the £370k SHIFT initiative. His work includes innovations in camera calibration for sports videos, self-supervised learning models, and federated learning architectures. He is affiliated with the Centre for Multimodal AI and collaborates with organizations like BBC and Innovate UK. His email is ebroul.izquierdo@qmul.ac.uk . Research Grants : Smart Farm and Agri-environmental Big Data Space (£305,116 EPSRC) SHIFT Project (£370,577 EPSRC) KTP: Machine Learning for Sport Broadcast Graphics (£214,061 Innovate UK) Labs & Teams : Centre for Multimodal AI, collaborating with BBC and EU Horizon 2020 initiatives.