جست‌وجوی استاد و پژوهشگر

Hany Elgalaمشاهده پروفایل

استادیار

• Telecommunications
• Digital Signal Processing
• Embedded Systems
• +۶ مورد دیگر

Dr. Hany Elgala is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany, State University of New York, and Director of the Signals and Networks (SINE) Lab. Previously, he served as a Research Professor at Boston University, co-leading the Multimedia Communications Lab and acting as Communications Testbed leader at the NSF Smart Lighting ERC. His research focuses on telecommunications, digital signal processing, and embedded systems, with specialization in visible light communications (VLC), LiFi networks, IoT security, and AI-driven wireless solutions. He has coordinated industrial projects with Airbus and EADS on optical wireless networks in aircraft cabins. Elgala's work emphasizes heterogeneous wireless networks, backscatter communication, and security in IoT. He has authored/co-authored ~50 publications/patents, contributing to fields like spectral efficiency, energy optimization, and network coexistence. His leadership in the SINE Lab drives innovations in 6G networks, UAV-based systems, and hybrid LiFi-WiFi architectures. His research portfolio includes breakthroughs in VLC system design, machine learning applications for physical layer optimization, and jamming-resistant IoT networks. Recent efforts focus on indoor flying networks and low-complexity VLC frameworks leveraging multi-task learning and autoencoder-based modulation.

Edward Brownمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Hypertext
• Scientific Visualization
• +۳ مورد دیگر

Dr. Edward Brown is an Associate Professor in the Department of Computer Science at Memorial University of Newfoundland, part of the Faculty of Science. His research focuses on human-computer interaction, hypertext systems, scientific visualization, and legal aspects of technology. He holds degrees from Memorial University (B.Sc.), the University of Toronto (M.Sc., Ph.D.), and the University of Victoria (LL.B.). His current research includes augmented reality applications for marine navigation, multimedia teaching strategies for K-12 education, and the design of user interface agents. Notable projects involve developing hypermedia tools for educational environments and studying complexity in finite-domain problems using genetic algorithms. Dr. Brown also practices technology law and has expertise in intellectual property issues. Teaching responsibilities include courses such as CS2760 (Encountering the Computer) and CS3718 (Programming in the Small). His work bridges computer science with pedagogical innovation and legal implications of emerging technologies.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

• Multimedia Communications
• Self-organizing Systems
• Mobile Systems
• +۷ مورد دیگر

Prof. Ralf Steinmetz is a Full Professor of Multimedia Communications at Technische Universität Darmstadt since 1996 and head of the Multimedia Communications Lab. His research focuses on adaptive multimedia systems, self-organizing networks, mobile/sensor networking, and educational technologies. He has held leadership roles at IBM's European Networking Center and Fraunhofer IPSI. His work spans cybersecurity, IoT, disaster communication systems, and industrial networking. Education : Dr.-Ing (PhD) in Electrical Engineering, TU Darmstadt (1986) Habilitation in Computer Science, Goethe University Frankfurt (1994) Research Interests : He pioneers innovations in networked multimedia systems , including fault-tolerant communication, aerial-ground disaster monitoring (e.g., CAMON system), and 5G/Industry 4.0 infrastructure. His work emphasizes resilience, adaptive protocols, and cyber-physical integration. Recent Work Trends : Publications from 2022-2025 highlight advancements in: Wireless communication protocols (ESP-NOW, LoRa) Cybersecurity (intrusion detection via machine learning) Disaster response networks (aerial-ground cooperation) Time-sensitive networking for industrial applications Key Contributions : Developed Nature 4.0 sensor systems for biodiversity monitoring Pioneered P4-programmable network hardware solutions (P4-CODEL, P4-BNG) Advanced UAV-based emergency communication frameworks

• Computer Music
• Optical Networks
• Optimization
• +۹ مورد دیگر

Cristina Emma Margherita Rottondi is an Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino . She is a member of the Photonext Interdepartmental Center and contributes to research in telecommunications, computer music, and network optimization. Her work spans privacy-preserving protocols, smart grid communication, and low-latency audio streaming. Research Interests : Networked Music Performance, Optical Networks, Smart Grid Privacy, Machine Learning. Education : Not explicitly listed. Research Areas include: Smart Grid Privacy : Developing secure protocols for data aggregation and distributed energy optimization. Optical Network Design : Investigating machine learning-driven solutions and spatial division multiplexing. Networked Music Performance : Addressing latency and inclusivity in remote musical collaboration. Publication Trends highlight interdisciplinary work at the intersection of telecommunications , machine learning , and music technology . Recent articles focus on privacy-preserving smart grids , 5G-enabled musical IoT , and UDP packet trace datasets . Scientific Awards : 2020 Charles Kao Award Best Paper Awards at IEEE Online Greencomm (2014), DRCN (2017), and others N2Women Rising Star (2020) Advising includes PhD candidates working on networked music performance , accessible musical education , and medical wearable devices . She has contributed to national patents for inclusive audio hardware.

Tobias Lunteمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Gaze Tracking
• Assistive Technologies
• +۳ مورد دیگر

Tobias Lunte is a PhD student and research assistant at the University of Oldenburg's Department of Media Informatics and Multimedia Systems. His work focuses on assistive technologies for education, human-computer interaction (HCI), and multimodal interface design, particularly using gaze-tracking and augmented reality (AR) to address reading difficulties in children. He holds an M.Sc. in Engineering of Socio-Technical Systems (2020) and received the Best Thesis Award in Computer Science by OLDIES e.V. His research integrates real-time gaze behavior analysis to develop adaptive reading assistance tools, collaborating with parents, speech therapists, and educators. He also teaches courses on ethnographic methods, Gestalt principles in design, and quantitative evaluation techniques. In educational development, Tobias redesigned modules like Interactive Systems (BSc) and Human-Computer Interaction (MSc) into a cohesive HCI teaching framework. He pioneered a remote hardware-oriented Maker's Lab course during the 2020 digital semester, which was nominated for Best Course of 2019/20. His practical projects span physical computing, AR applications, and sensory notification systems for clinical environments. Scientific Awards: Best Thesis Award in Computer Science by OLDIES e.V. (2020) He supervises student projects in gaze-tracking, physical computing, and ARVR, including notable works like the Puzzle Box prototype, Sherlock AR game system, and Sensor Pet health reminder tool. His research combines technical innovation with pedagogical insights to create user-centered solutions across education and healthcare domains.

Dominik Walterمشاهده پروفایل

پژوهشگر

• Hardware-Software Co-Design
• Processor Arrays
• Embedded Systems
• +۳ مورد دیگر

Dominik Walter is a Researcher at the Department of Computer Science 12 (Hardware-Software Co-Design) at the University of Erlangen-Nuremberg (FAU), where he has been working since 2019. His research focuses on innovative approaches to hardware-software integration, particularly in the domain of processor array architectures and embedded systems design. Walter completed his educational journey at the University of Augsburg, earning a B.Sc. in Computer Science and Multimedia (2013-2016) followed by an M.Sc. in Computer Science (2016-2019). This strong foundation in both theoretical and applied computer science has informed his current research direction. His primary research interests center around Hardware-Software Co-Design, with particular expertise in Tightly Coupled Processor Arrays and Invasive Computing paradigms. Walter's work explores how to optimize resource allocation in parallel computing environments, improve energy efficiency in embedded systems, and develop novel approaches for real-time data processing. His research bridges theoretical computer architecture concepts with practical implementation challenges. Walter's publication record from 2020-2025 reveals a consistent research trajectory focused on processor array architectures and their applications. His work spans both theoretical foundations and practical implementations, with contributions to sustainable computing, real-time systems, and hardware acceleration techniques. A notable pattern in his research is the application of invasive computing principles to solve complex parallel processing challenges across various domains. As a researcher at FAU's Department of Computer Science 12, Walter is part of a vibrant research community focused on cutting-edge hardware-software integration problems. His work contributes to the department's reputation in processor architecture and embedded systems design, with potential applications in high-performance computing, embedded AI, and energy-efficient computing systems.

Qinru Qiuمشاهده پروفایل

استاد

• Neuromorphic computing
• Machine learning
• Green computing
• +۵ مورد دیگر

Dr. Qinru Qiu is a Distinguished Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. She holds a PhD in Electrical Engineering from the University of Southern California (2001) and a BS from Zhejiang University (1994). Her research focuses on neuromorphic computing, energy-efficient systems, and machine learning applications, emphasizing power and thermal management in embedded and high-performance computing systems. She leads NSF's Center for Alternative Sustainable and Intelligent Computing (ASIC) and serves on editorial boards of IEEE journals and conference committees. Education: PhD (USC, 2001), MS (USC, 1998), BS (Zhejiang University, 1994) Her research interests include neuromorphic architectures for cognitive applications, energy harvesting systems, and hardware-accelerated machine learning. Recent work explores in-memory computing with memristive devices and energy-efficient anomaly detection frameworks. She has published widely in top venues like IEEE/ACM conferences and journals. Notable awards include the NSF CAREER Award (2009), IEEE Region 1 Technological Innovation Award (2020), and ACM Distinguished Member (2022). She is an IEEE Fellow and served as CEDA Society Distinguished Lecturer (2023–2024). Dr. Qiu advises numerous graduate students in neuromorphic systems and energy-efficient computing. Her professional services include roles as Associate Editor for IEEE TNNLS and TCDS, and conference organizing roles at ISLPED and ICCAD.

• Embedded Systems
• AI Algorithms
• FPGA/VHDL Design
• +۵ مورد دیگر

Jürgen Wassner is a Professor of Edge Computing and Co-Head of the Competence Center for Intelligent Sensors and Networks at the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a master's degree from Technical University of Dresden and a PhD from ETH Zurich. His career spans industry roles in Silicon Valley Group Inc. and telecommunication R&D before joining academia in 2007. His research focuses on embedded systems, AI at the edge, FPGA/VHDL design, real-time systems, and powerline communication . He leads projects such as 'Visual-Servoing Testbed with AI-Hardware in the Loop' and 'Low-Cost High-Performance Intelligent Camera for Space Debris Mitigation,' collaborating with companies like Diehl Aerospace. Recent publications emphasize wire fault detection using powerline communication, AI-based odor classification systems, and hardware acceleration for CNNs. His work bridges theoretical research with practical applications in avionics, rail freight, and space debris mitigation. Wassner advises Master of Science in Engineering students and has been recognized for his media engagement including a feature in the Luzerner Zeitung. He teaches Digital Design (FPGA, VHDL) and Digital Signal Processing , emphasizing hands-on implementation of cutting-edge systems.

• Computer Science
• Electrical Engineering
• Robotics
• +۷ مورد دیگر

Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.

• Computer Architecture
• Embedded Systems
• Hardware/Software Co-Design
• +۵ مورد دیگر

Hammam Alsafrjalani is an Associate Professor of Practice in the Department of Electrical & Computer Engineering at the University of Miami. His roles include teaching undergraduate and graduate courses such as Computer Architecture, Embedded Systems, and Microprocessors, and supervising senior design projects. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from the University of Florida (2015 and 2011, respectively), and a B.S. from the New Jersey Institute of Technology (2007). His research focuses on computer architecture, embedded systems, heterogeneous-and-configurable systems, AI for cybersecurity, and runtime optimization. Notable achievements include developing optimization techniques for energy, performance, and temperature in multicore systems, yielding up to 25% energy savings. His work has been published in peer-reviewed journals and conferences. Awards include the Faculty Learning Community Fellowship (2022 and 2019), Most Innovative Faculty Member (2020 and 2019), and the Student-Choice Best Engineering Faculty Member Award (2019). He has contributed to proposals in computer science, applied science, and multidisciplinary projects with psychology and communication schools. Teaching tools include Quartus Prime, Gem5, and Python, with a focus on hands-on projects. His research emphasizes practical applications in consumer electronics and augmented reality, leveraging industry experience from internships at Intel and Microsoft, and prior roles at Dialogic and Vonage.

Benjamin Brossمشاهده پروفایل

مدرس پاره‌وقت

• Video Coding
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Benjamin Bross is a part-time lecturer at HTW University of Applied Sciences Berlin and heads the Video Coding Systems group at Fraunhofer Heinrich Hertz Institute. He specializes in video coding standards, including HEVC (H.265) and VVC (H.266), contributing to their development and standardization. His work emphasizes open-source implementations like VVenC and VVdeC, deployed in broadcast and streaming systems. Education: Dipl.-Ing. in Electrical Engineering (RWTH Aachen University, 2008). Active in ITU-T VCEG and ISO/IEC MPEG since 2010, leading core experiments and editing key standards. Recognized with IEEE Best Paper (2013), SMPTE Merit (2014), and an Emmy (2017) for HEVC contributions. Research focuses on advanced compression techniques, machine learning integration, and real-time encoding. His team develops VVC tools for 4K/UHD, low-latency streaming, and adaptive bitrate systems. Recent work includes optimizing partitioning strategies and reducing encoding complexity in VVC implementations. Awards highlight his impact on video technology: IEEE Consumer Electronics Best Paper (2013), SMPTE Journal Certificate (2014), and an Emmy for HEVC (2017). Teaching emphasizes practical coding standards and their applications in multimedia systems.

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۶ مورد دیگر

Claude D'Amours is a Professor in the Department of Electrical and Computer Engineering at the University of Ottawa, Faculty of Engineering. With a research career spanning over two decades, his work has significantly contributed to the fields of wireless communications, signal processing, and network security. His current research focuses on cutting-edge technologies including 5G/6G networks, UAV communications, reconfigurable intelligent surfaces, and physical layer security. Dr. D'Amours' research interests center on advanced wireless communication systems, with particular emphasis on MIMO technologies, CDMA systems, spectrum sensing techniques, and channel modeling for emerging applications. His work bridges theoretical foundations with practical implementations, addressing critical challenges in modern wireless networks including interference management, security vulnerabilities, and efficient resource allocation. Recent research has expanded into UAV communications, where he investigates channel characteristics and reliability issues in drone-based networks. Analysis of his recent publications reveals a strong focus on security aspects of wireless communications, particularly physical layer security mechanisms to counter jamming attacks and eavesdropping. His work on RF fingerprinting for device authentication in industrial IoT settings demonstrates practical applications of his theoretical research. The trend in his publications shows increasing attention to 5G/6G technologies, with particular emphasis on millimeter wave communications, hybrid precoding architectures, and novel approaches to spectrum sharing. Multiple publications on UAV-to-ground channel modeling and performance analysis Research on physical layer security mechanisms against jamming attacks Work on hybrid precoding architectures for mmWave MIMO systems Studies on RF fingerprinting for secure industrial IoT applications Investigations into spectrum sensing techniques for cognitive radio Dr. D'Amours maintains active research collaborations with numerous colleagues, particularly Michel Kulhandjian, Hovannes Kulhandjian, and François Chan, resulting in a consistent stream of high-impact publications across top-tier IEEE journals and conferences. His research group appears to focus on both theoretical analysis and practical implementation of advanced wireless communication techniques, with applications spanning from critical mission communications to industrial IoT security.

• Computer Vision
• Stereo Matching
• 3D Reconstruction
• +۳ مورد دیگر

Michael Bleyer is a Researcher in the Department of Computer Vision at the Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics. His work focuses on advanced imaging technologies, particularly in stereo matching, sensor design, and applications in augmented/mixed reality. He has contributed to projects funded by the Vienna Science and Technology Fund (WWTF), Austrian Science Fund (FWF), and the Federal Ministry of Transport, Innovation, and Technology (bm:vit). Education: Diplom-Ingenieur (Dipl.-Ing.) from TU Wien (2002), followed by a Dr.techn. (PhD) thesis on 'Segmentation-based stereo and motion with occlusions' (2006). He has supervised four students, including Armin Haßlacher (2012), Gregor Braun (2011), Roman Gross (2009), and Christian Rhemann (2005). Research Interests: Bleyer’s work bridges theoretical computer vision and practical sensor engineering. Recent trends emphasize SPAD-based imaging systems for low-light environments and head-mounted displays, addressing challenges like dark current compensation and temporal filtering. Earlier contributions include global stereo matching algorithms, optical flow estimation, and 3D scene reconstruction. Grants and Advising: Projects include Temporal-Consistent Stereo Matting (2009–2015, WWTF) Energy Functions for Global Stereo Matching (2007–2012, FWF) Video Engine Design Methodology (2006–2015, bm:vit) His advising spans topics like color in stereo matching and image filtering optimization.

Ammar Belatrecheمشاهده پروفایل

مدرس ارشد

• bio-inspired intelligent systems
• machine learning
• spiking neural networks
• +۵ مورد دیگر

Dr. Ammar Belatreche is a Senior Lecturer in Computer Science and Programme Leader for the MSc Advanced Computer Science at Northumbria University's Department of Computer and Information Sciences. He joined Northumbria University in May 2016 after previous positions as a Research Associate and Lecturer at Ulster University. He is an active member of the Computational Intelligence and Visual Computing (CIVC) research group. Dr. Belatreche earned his PhD in Computer Science from Ulster University in 2007. His professional qualifications include: Member of the Association of Computing Machinery (ACM) since 2012 Fellow of the Higher Education Academy (FHEA) since 2010 Member of the Institute of Electrical & Electronic Engineers (IEEE) since 2009 His research focuses on bio-inspired intelligent systems, machine learning, spiking neural networks, face detection and recognition, structured and unstructured data analytics, capital markets engineering, and image processing. Dr. Belatreche has extensive experience across academic and R&D in these areas, leading numerous research and consultancy projects. His recent work demonstrates a strong emphasis on neuromorphic computing, particularly spiking neural networks and their applications in computer vision, financial analysis, and biometrics. Analysis of his recent publications shows a clear trend toward advancing spiking neural network architectures, with particular focus on quantization, pruning, and binary implementations to improve efficiency. His research spans multiple domains including computer vision (face recognition, palm-vein recognition), financial technology (stock price manipulation detection), and neuromorphic engineering. Many of his recent papers (2024-2025) appear in top-tier conferences like ICLR and journals like IEEE Transactions on Neural Networks and Learning Systems. Dr. Belatreche has received professional recognition including: Fellowship with the Higher Education Academy (FHEA) Role as Associate Editor for the journal Neurocomputing He has successfully supervised or co-supervised 8 PhD students to completion and serves as a Program Committee Member and reviewer for numerous international conferences and journals. As Programme Leader for the MSc Advanced Computer Science, he plays a significant role in shaping postgraduate education in computer science at Northumbria University. His research group work bridges theoretical advances in neural computation with practical applications across multiple domains. Based in CIS 305 at Northumbria University's Newcastle campus, Dr. Belatreche continues to advance research in neuromorphic computing and its applications while contributing to academic leadership through his programme leadership role.