جست‌وجوی استاد و پژوهشگر

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

Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.

• Video Coding
• Machine Learning
• FPGA Acceleration
• +۳ مورد دیگر

Jarno Vanne is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on video coding standards, real-time systems, and hardware acceleration, particularly in the context of FPGA implementations and open-source tools. He leads projects involving VVC (Versatile Video Coding), V-PCC (Volumetric Video Coding), and HEVC (High Efficiency Video Coding), with an emphasis on efficiency, low latency, and machine learning integration. Key research interests include point cloud compression, saliency-guided encoding, parallelization schemes, and real-time video communication protocols. His work often addresses challenges in multi-party video streaming, embedded systems, and encryption mechanisms for privacy protection. He has contributed to open-source projects like the UVG dataset, Kvazaar encoder, and CiThruS simulation frameworks. Recent publications highlight advancements in VVC intra encoding optimizations, machine learning-driven partitioning schemes, and FPGA-accelerated solutions for edge computing. His research bridges theoretical video coding algorithms with practical implementations, aiming to improve compression efficiency while maintaining real-time performance.

Zhenghao Chenمشاهده پروفایل

مدرس

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۱ مورد دیگر

Dr. Zhenghao Chen is an Assistant Professor at the University of Newcastle. He holds a B.Eng. H1 and Ph.D. from the University of Sydney (2017 and 2022). His research focuses on Computer Vision, NLP, and Machine Learning, with expertise in Generative AI. He has published in top conferences like CVPR and journals such as IEEE T-IP. Awards include the Google Australia Prize and ACM SIGMM Outstanding Thesis Award. He previously worked at TikTok and Disney Research, and serves on program committees for major conferences. Research interests emphasize generative models and industrial applications. His publications span topics like image compression, facial recognition, and medical imaging. Awards highlight academic and industrial recognition. Teaching includes courses on visual signal understanding and video intelligence. Current roles include HDR recruitment and organizing international workshops.

Ning Wangمشاهده پروفایل

استاد

• 5G and beyond networking
• Future Internet design
• Mobile content delivery technologies
• +۵ مورد دیگر

Professor Ning Wang is a leading academic in communication systems at the University of Surrey's Institute for Communication Systems (ICS), School of Computer Science and Electronic Engineering. He holds a PhD from the University of Surrey (2004) and has expertise in 5G/6G networks, edge computing, and space-terrestrial integration. As a coordinator for the EuroMaster Programme and Communication Networks and Software (CNS) pathway, he leads research in network management, mobile video delivery, and IoT applications. His work has been featured in IEEE ComSoc Technology News three times since 2012. Current leadership roles include 5GIC Work Area 1 leader for content and network context. Research collaborations span global institutions like UCL, ETH Zurich, and industry partners like BT and InterDigital. Notable contributions include SDN-based space-terrestrial network integration (VDPA scheme) and O-RAN automation via federated DRL. Over 130 publications and active participation in standards bodies (IETF, 3GPP) reflect his impact on future network architectures. Educations: BEng in Computing (Changchun University of Science and Technology, 1996) MEng in Electronic Engineering (Nanyang Technological University, 2000) PhD in Electronic Engineering (University of Surrey, 2004) Research Focus: Future Internet design, network intelligence, content-centric networking, and satellite integration. Key projects include EU Horizon Europe SPIRIT (immersive telepresence), ESA TINA (satellite 5G functions), and EPSRC NG-CDI (converged digital infrastructures). His research emphasizes practical solutions like edge-AI for VNF splitting and holographic frame synchronisation. Grants & Projects: Over £20M in grants from EPSRC, EU Horizon, InnovateUK, and Royal Society. Active in EU-funded SAT5G (satellite-terrestrial 5G) and C-DAX (smart grid cybersecurity).

• 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.

Christopher F Barnesمشاهده پروفایل

دانشیار

• Synthetic Aperture Radar (SAR)
• Signal Processing
• Data Mining
• +۵ مورد دیگر

Christopher F. Barnes is an Associate Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, with adjunct status as a Principal Research Engineer at the Georgia Tech Research Institute (GTRI). He holds a Ph.D. from Brigham Young University (1989) and has over 27 years of experience in radar signal processing, software engineering, and applied research. His research focuses on synthetic aperture radar (SAR) analysis, data mining, and image/video-driven technologies with applications in remote sensing, medical imaging, and seismology. Dr. Barnes' expertise includes radar imaging algorithms, software architectures for radar systems, and object-oriented programming. He pioneered methods for three-dimensional coherently fused SAR imaging and developed image-driven systems for hurricane damage assessments and bioinformatics. His work in video tracking and content-based search leverages residual vector quantization and machine vision techniques. Notable achievements include the Georgia Tech Outstanding Professional Education Award (2009) and an Interdisciplinary Research Initiative Award (2006). His contributions span over 140 publications and one patent, with research supported by defense and academic collaborations. Dr. Barnes teaches SAR at professional and graduate levels and advises research in video-driven data mining and medical imaging applications. His current projects explore AI-driven SAR analysis and advanced radar system architectures.

Aljaž Božičمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +۷ مورد دیگر

Aljaž Božič is a Research Scientist at Meta Reality Labs Research , focusing on neural rendering, 3D reconstruction, and AI-driven geometry modeling. He earned his Ph.D. in Computer Science from the Technical University of Munich (TUM) and holds a Master's in Computer Science from TUM and a Bachelor's in Mathematics from the University of Ljubljana . His research spans computer vision, graphics, and artificial intelligence , with a focus on neural rendering , generative AI , and 3D deformable object modeling , targeting applications in VR/AR and robotics. His work includes time-consistent dynamic scene reconstruction (SceNeRFlow), volumetric hair appearance modeling, and high-fidelity walkable VR spaces (VR-NeRF), alongside efficient NeRF distillation and calibration methods (Neural Lens Modeling). Key article trends include Transformer-based monocular reconstruction (TransformerFusion), neural parametric shape models (NPMs), and self-supervised non-rigid tracking (Neural Deformation Graphs). He has contributed to open-source projects like the TransformerFusion GitHub repository , emphasizing MIT-licensed tools for scene reconstruction. At TUM, he served as a Teaching Assistant for courses such as 3D Scanning and Spatial Learning and 3D Vision Seminar , bridging academic instruction with research innovation. His work integrates advanced neural networks with practical optimization techniques, advancing fields like RGB-D reconstruction (DeepDeform) and variational SLAM.

• Machine Learning
• Computer Vision
• Medical Imaging Analysis
• +۵ مورد دیگر

Almabrok Essa is an Assistant Professor in the Department of Mathematics, Computer Science, and Data Science at John Carroll University. His research focuses on advanced machine learning techniques applied to medical imaging, computer vision, and signal processing. He holds a faculty position at Dolan Science Center E207 and can be reached via email at aessa@jcu.edu. His work bridges theoretical computer science with practical applications in healthcare, environmental monitoring, and security systems. Research interests include deep learning architectures for medical diagnostics (e.g., chest X-ray analysis and skin lesion segmentation), steganography for secure image transmission, and remote sensing technologies for geospatial analysis. He has pioneered methods like Multibit LSB matching for data hiding and developed frameworks like COVID-CLNet for pandemic response. His contributions span over 20 peer-reviewed articles from 2015 to 2025, showcasing innovations in face recognition algorithms, brain signal analysis, and environmental pattern recognition. Notable applied projects include real-time heart/respiration monitoring systems and automated machinery threat detection for infrastructure protection. His research demonstrates strong interdisciplinary potential, combining computational methods with domains like biomedical engineering, environmental science, and cybersecurity.

Sergio Tejeda Pastorمشاهده پروفایل

پژوهشگر

• Multimedia Communications
• Video Coding
• 5G Technology
• +۳ مورد دیگر

Sergio Tejeda Pastor is a Researcher at the Fraunhofer Heinrich Hertz Institute , specializing in the Video Communication and Applications department. His work focuses on interactive streaming of volumetric video within multimedia communications. Education: M.Sc. in Telecommunications Engineering from Universidad Carlos III de Madrid (2012) Research Interests: Interactive volumetric video streaming 5GXR and 5G Hybrid ARQ Feedback Prediction Versatile Video Coding (VVC) standards Machine learning applications in video compression Professional Affiliations: Member of the Multimedia Communications group at Fraunhofer HHI since 2021 Active participant in projects like VoluProf and SPIRIT Contact: Mailing Address: Einsteinufer 37, 10587 Berlin, Germany Visitor Address: Salzufer 15/16, 10587 Berlin, Germany

• Image Compression
• Machine Learning
• Visual Media Processing
• +۷ مورد دیگر

Jona Ballé is an Associate Professor in the Electrical and Computer Engineering department at New York University's Tandon School of Engineering. His research focuses on developing efficient representations of visual media through machine learning and end-to-end optimization techniques. Dr. Ballé's research interests center on visual media compression, spanning still images, video, augmented reality, virtual reality, plenoptic imaging, and holographic imaging. His work bridges information theory, computer vision, and machine learning to develop perceptually optimized compression algorithms. He has made significant contributions to understanding the relationship between human visual perception and image statistics, which has led to improved compression results and ultimately contributed to the JPEG AI standard finalized in 2025. His recent publications demonstrate a strong trend toward Wasserstein distortion metrics, neural compression architectures, and rate-distortion optimization. These works span computer vision, information theory, and signal processing domains, with applications in both traditional and emerging visual media formats. His research shows consistent innovation in developing perceptually relevant metrics that balance fidelity and realism in compressed media. Contributed to JPEG AI standard (2025) Co-organizer of Challenge on Learned Image Compression (CLIC) since 2018 Program committee member of Data Compression Conference (DCC) since 2022 Reviewer for top-tier publications including NeurIPS, ICLR, ICML, and IEEE Transactions journals Dr. Ballé has advised numerous graduate students who have co-authored significant publications with him, particularly in the areas of neural compression and perceptual metrics. His research has been supported by institutions including the Simons Foundation. He maintains active collaborations across academia and industry, with his work at Google (2017-2024) directly informing his current academic research. His laboratory focuses on developing open-source implementations of advanced compression techniques, with notable GitHub repositories including Wasserstein Distortion implementation in PyTorch and CoDeX (Learned data compression in JAX), demonstrating his commitment to reproducible research and community engagement.

Touradj Ebrahimiمشاهده پروفایل

استاد

• Image and Video Compression
• Media Security
• Multimedia Signal Processing
• +۵ مورد دیگر

Touradj Ebrahimi is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he heads the Multimedia Signal Processing Group (MMSPG) within the School of Engineering's Institute of Electrical Engineering. He holds a Ph.D. in Electrical Engineering from EPFL and serves as the Convenor of the JPEG standardization committee. His research spans AI-powered imaging, multimedia signal processing, and media security. Research Interests: Professor Ebrahimi's work focuses on next-generation image/video compression (including JPEG AI and DNA-based storage), quality of experience in immersive media, trustworthy media authentication (JPEG Trust), and explainable AI for biometric systems. His group pioneers methods for point cloud compression, deepfake detection, and privacy-preserving multimedia. Publication Trends: Recent articles emphasize AI-driven compression standards, quality assessment for emerging media (point clouds, HDR), and explainability in computer vision. Key themes include JPEG AI standardization, DNA storage for visual data, and robustness evaluation of deepfake detectors. Awards & Honors: IEEE Star Innovator Award in Multimedia IEEE and Swiss National ASE Awards ISO Certificates for MPEG-4/JPEG 2000 contributions Best Paper Award (IEEE Trans. Consumer Electronics) Fellow of IEEE and SPIE Leadership: He founded RayShaper SA, Genista SA, and Emitall SA. Represents Switzerland in ISO/IEC JTC1/SC29 and ITU, advising European Commission initiatives and venture capital firms on multimedia technologies. Facilities: Leads the MMSPG lab at EPFL, focusing on intelligent multimedia systems. The group collaborates globally on standardization (JPEG, MPEG) and EU projects.

• FPGA-based Systems
• Real-Time Video Processing
• Wireless Vision Sensor Networks
• +۵ مورد دیگر

Najeem Lawal is a Senior Lecturer at the Department of Computer and Electrical Engineering (DET) at Mid Sweden University. His research focuses on FPGA-based systems, real-time video processing, and wireless vision sensor networks. He is affiliated with the STC Research Centre and has contributed extensively to hardware architecture optimization for smart cameras and volumetric surveillance systems. Doctoral Thesis: Memory Synthesis for FPGA Implementation of Real-Time Video Processing Systems (2009) Licentiate Thesis: Memory Synthesis for FPGA Implementation of Real-Time Video Processing Systems (2006) Lawal's research explores cost-effective multi-camera dome designs, real-time component labeling, and energy-efficient wireless vision sensor nodes. His work spans architecture exploration, hardware-software partitioning, and memory optimization for FPGA platforms. He has published in journals like IEEE Sensors Journal and conferences including the International Conference on Distributed Smart Cameras. Recent publications highlight advances in volumetric surveillance coverage, calibration of multi-camera systems, and low-power embedded vision architectures. Lawal's projects emphasize practical deployment of smart camera networks for outdoor monitoring applications. He has collaborated with researchers such as Muhammad Imran, Benny Thörnberg, and Mats O'Nils. His expertise includes designing hardware architectures for vision systems, optimizing processing pipelines, and modeling sensor network configurations.

Álvaro Lópezمشاهده پروفایل

پژوهشگر

• Machine Learning
• 5G Networks
• Wi-Fi
• +۳ مورد دیگر

Álvaro López is a Research Associate at the Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, Germany, where he joined the Multimedia Communications Group in 2023. His work focuses on next-generation mobile communications and 5G standardization within the 3GPP RAN2 activities. His educational background includes: B.Sc. in Telecommunications Systems Engineering from Universitat Politècnica de Catalunya (UPC), 2015 M.Sc. in Wireless Communications Systems from Universitat Pompeu Fabra (UPF), 2017 Ph.D. in Information and Communication Technologies from UPF, 2022 Dr. López's research interests center on the intersection of machine learning and wireless communications . He develops autonomous learning techniques for 5G and beyond, with emphasis on Wi-Fi multi-link operations , traffic allocation , and network optimization to enhance next-generation communication systems. His publications (2016-2022) reveal a consistent focus on wireless networking evolution, particularly IEEE 802.11 standards (Wi-Fi 6/7) and 5G. Key trends include machine learning integration for self-organizing networks, multi-link resource management, and performance optimization in heterogeneous wireless environments. No information is available regarding students advised or specific research grants. His 3GPP standardization participation indicates industry-collaborative project involvement. At Fraunhofer HHI, Dr. López contributes to the Multimedia Communications Group's work on video coding, 5GXR, volumetric video, and Versatile Video Coding (VVC), advancing multimedia transmission for emerging applications like virtual reality.

Joohee Kimمشاهده پروفایل

دانشیار

• Image and Video Signal Processing
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Joohee Kim is an Associate Professor in the Department of Electrical and Computer Engineering at Illinois Institute of Technology, where she has been since 2009. She directs the Multimedia Communications Laboratory and focuses on research funded by U.S. Federal Agencies and the Korean Government. Education: Ph.D., Electrical and Computer Engineering, Georgia Institute of Technology (2003) M.S., Electrical Engineering, Yonsei University (1993) B.S., Electrical Engineering, Yonsei University (1991) Research interests include image/video signal processing, computer vision, machine learning, multimedia communication, and real-time 3D reconstruction . Her work spans applications in autonomous systems, robotics, and advanced driver assistance systems, emphasizing low-complexity algorithms and efficient coding techniques. Her recent articles highlight advancements in pedestrian detection via deep learning, 3D reconstruction fusion methods, and optimized depth map coding. These contributions address challenges in real-time systems, energy efficiency, and robust transmission over wireless networks. Awards: None explicitly listed. Grant Activity: Active in federal and international funding for projects like low-delay distributed video coding and error-resilient transmission. Advising: No students listed, though lab leadership implies mentorship roles. Labs/Teams: Director of the Multimedia Communications Lab, collaborating on 3D imaging, video coding, and intelligent systems integration.