Javier Ruiz Atencia is a PartTime Lecturer in the Department of Computer Engineering at Miguel Hernández University of Elche, specializing in Computer Architecture and Telecommunications Technology. He teaches courses including Operating Systems, Programming Languages, and Network Architecture. Current academic appointments at UMH Teaching responsibilities across multiple engineering programs Active research in video coding and perceptual quality optimization Technical expertise in HEVC/VVC standards and neural network applications Research focus centers on perceptual video coding techniques that leverage human visual system characteristics. His work explores: Hybrid contrast & texture masking models QP optimization algorithms Rate-distortion performance analysis Next-generation video compression strategies Perceptual quality assessment frameworks Technical Contributions include development of HEVC intra prediction emulators and network architecture implementations. Current projects focus on integrating dual hybrid neural networks for improved VVC encoding efficiency.
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.
Shiqi Wang is an Associate Professor in the Department of Computer Science at City University of Hong Kong. He holds a Ph.D. from Peking University (2014) and a B.Sc. from Harbin Institute of Technology (2008). His career includes postdoctoral and research roles at the University of Waterloo, Nanyang Technological University, and Microsoft Research Asia. He specializes in semantic/visual communication, AI content management, and image/video quality assessment. Education: Ph.D. in Computer Application Technology (2014), Peking University B.Sc. in Computer Science and Technology (2008), Harbin Institute of Technology Research focuses on Large Visual-Language Models (LVLMs) , Generative Face Video Coding , and Information Forensics . Recent work includes video coding innovations, AI-driven quality assessment, and bias mitigation in facial analysis. Awards include the IEEE Multimedia Rising Star Award (2021) , NSFC Excellent Young Scientist Fund (2020) , and multiple best paper awards at IEEE conferences. He serves as Associate Editor for IEEE Transactions on Image Processing and leads MPEG standardization efforts for generative video coding. Professional activities include TPC roles at ICML, CVPR, and ACM Multimedia. His lab actively collaborates on standards for generative AI and multimedia systems, with a focus on ethical AI and cross-domain applications.
Vladimir Zlokolica is a researcher affiliated with Singidunum University, holding a PhD in Telecommunications and Information Processing from Ghent University (2006) and a Master's degree from the same institution (2005). He obtained his Bachelor's degree in Electrical Engineering from Novi Sad (2001). His research focuses on Medical Imaging , Computer Vision , and Automotive Perception Systems , particularly in advanced image processing techniques for 3D medical imaging and motor vehicle environmental modeling. His work includes segmentation algorithms for cardiovascular diagnostics and innovative methods for automotive image generation and enhancement. Publications from 2017-2020 demonstrate expertise in 3D image analysis , medical applications , and automotive vision systems . Notable contributions include techniques for left atrial appendage segmentation, vessel delineation, and quality metrics for 3D visual content. Despite no explicit scientific awards mentioned, his collaborations with institutions like Ghent University and Singidunum University highlight his academic engagement.
David Broneske is a Researcher at the Otto von Guericke University of Magdeburg , Germany. His work spans Database Systems , Heterogeneous Computing , and Machine Learning Applications , with a focus on GPU/FPGA Acceleration and Non-volatile Memory (NVM) Optimization . He has contributed to projects like ADAMANT (co-processor integration) and GridTables (H2TAP data stores). Key Research Areas : Database acceleration via specialized hardware, Graph database applications in clinical/biological domains, and AutoML for domain-aware model selection. Collaborations : Frequent co-author with Gunter Saake, Bala Gurumurthy, and Sajad Karim on topics like NVM Storage and GPU-based Query Execution . Publications : Over 105 papers (2012–2025) covering Protein Identification Systems , Entity Resolution , and Software Evolution Datasets . Workshops : Co-organized the Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC) and contributed to standards like Backlogs/Interval Timestamps for temporal graph queries.
Massimo Violante is a Tenured Associate Professor at the Department of Control and Computer Engineering (DAUIN) , Politecnico di Torino. He serves as Deputy Director of the Polytechnic School and holds leadership roles in multiple research centers including the Interdepartmental Center PEIC (Power Electronics Innovation Center) and the Spin-Off/Start-Up Evaluation Commission . With expertise in embedded systems , functional safety , and sleep analysis , he bridges automotive engineering with biomedical applications through his research. Scientific Branch: IINF-05/A - Information Processing Systems ERC Sectors: PE7_11 (Components/Applications), PE6_1 (Computer Architecture), PE6_3 (Software Engineering) SDG Alignment: Goal 3 (Health) and Goal 9 (Innovation) His research focuses on fault tolerance , driver safety aid systems , and functional safety standards like ISO26262. He leads groundbreaking work in real-time sleep prediction and automotive electronics reliability , including patented solutions for drowsiness detection and safety-critical systems . Recent projects span from GreenChips-EDU (education ecosystem for sustainable microelectronics) to HiEFFICIENT (wide band gap power electronics for electric vehicles). Violante has supervised numerous PhD students including Sara Groppo (health monitoring for newborns), Michele Guagnano (sleep monitoring), and Pietro D'Agostino (Industrial IoT solutions). He has received prestigious recognition including the IEEE Best Paper Award (2005) and leadership roles as Program Chair for major symposiums like the IEEE European Test Symposium . His teaching portfolio includes foundational courses in Model-based Software Design , Operating Systems for Embedded Systems , and Technologies for Autonomous Vehicles . Major Projects: EU-funded: Cynergy4MIE (2024-2027), ShapeFuture (2024-2027), A-IQ Ready at EDGE (2023-2025) Nationally/Regionally funded: EMC2 (2014-2017), MIE (2014-2017), FELIX (2007-2010) Commercial contracts: Automotive Academy (2019-2020), Vodafone IoT Academy courses (2019-2021), Smartwatch-based drowsiness detection systems As a prolific inventor, he holds multiple international patents in driver state monitoring , microsleep prediction , and emergency intervention analysis . His work demonstrates a unique convergence of automotive engineering , biomedical applications , and high-reliability computing .
Dr. Nariman Farvardin serves as President and Professor of Electrical and Computer Engineering at Stevens Institute of Technology. Previously Senior Vice Provost at University of Maryland, he holds a PhD from Rensselaer Polytechnic Institute. His research focuses on data compression, wireless communications, and image processing. He has led major projects including a $5.5M Army Research Laboratory initiative on telecommunications and received honors including the Presidential Young Investigator Award and Academic Leadership Award from Carnegie Corporation. His patented inventions include image compression algorithms and wireless protocols. Funded by agencies including NSF, NASA and Mitsubishi, his work spans theoretical foundations to practical implementations like VLSI-based compression and CDMA network optimization. He has supervised over 30 graduate students and published 100+ works on communication systems.
Osama Alshaykh is a Lecturer and Research Professor in the Department of Electrical & Computer Engineering at Boston University. He holds additional roles as CEO/CTO of Nxtec Corporation and CTO of Talon Corporation, as well as a Civic Tech Fellow at the Faculty of Computing & Data Sciences. His research focuses on Machine Vision, Digital Signal Processing, Data Learning, Robotics, Network Communications, and Image Compression. Dr. Alshaykh has received notable awards including the BU ECE Best Class Award (2016), BU ECE Award of Excellence in Teaching (2015), and the Fulbright Scholarship (1991). He teaches advanced courses such as EC601, EC500, EC463, and EC464. His research spans innovative areas like 5G infrastructure planning, mobile device media transfer systems, and healthcare collaboration technologies. Recent work includes applying Mask R-CNN for utility pole planning and developing frameworks for virtual education hospitals. Professional affiliations include primary faculty status at BU’s Electrical & Computer Engineering Department, alongside his industry leadership roles. He is actively involved in advancing multimedia systems, network protocols, and healthcare technology through interdisciplinary collaborations.
Professor Anil Kokaram is a distinguished academic and researcher in Electronic Engineering at Trinity College Dublin (TCD), Ireland. He holds a PhD in Signal Processing from the University of Cambridge (1993). As a Fellow of Engineers Ireland and recipient of an Academy Award (Oscar) for his work in video processing, he is renowned for contributions to digital video restoration, multimedia forensics, and video compression. From 2011–2017, he led the Media Algorithms Team at YouTube/Google, advancing cloud-based video transcoding and enhancement technologies. His research bridges academia and industry, with innovations in Bayesian inference, motion estimation, and neural network applications for video processing. **Education**: PhD in Signal Processing, University of Cambridge (1993). **Key Roles**: Former Associate Editor of IEEE Transactions on Video Technology and Image Processing. Founded GreenParrotPictures (acquired by Google), producing video enhancement software. **Research Focus**: Video compression artifacts, perceptual quality metrics (e.g., ViSQOL), and adaptive streaming algorithms. **Notable Projects**: Developed frameworks for automated sports broadcasting, noise reduction in medical imaging, and synchronization of user-generated videos. **Awards**: 2007 Science & Engineering Academy Award (Oscar), 2007 Fellow of Engineers Ireland. **Labs/Teams**: Leads the Signal Media Algorithms group at TCD, collaborating with industry partners like Google on large-scale video analysis systems. His work emphasizes practical applications of signal processing in creative industries, including film post-production and virtual production.
Дину Драган is a Professor at the Department of Applied Computer Science, Faculty of Technical Sciences, University of Novi Sad. His academic journey includes a BSc (2003), MSc (2008), and PhD (2013) in Computer Science and Informatics, with a focus on medical imaging, data compression, and multimedia systems. He has been affiliated with the university since 2004, progressing from Assistant Lecturer to Associate Professor (2014). Research interests include compression techniques in healthcare systems, PACS implementation, and multimedia technologies. He has contributed to international journals like Computer Science and Information Systems as an editor and reviewer, and his work on JPEG2000 compression in DICOM standards has been impactful. He was awarded the FESTO Young Research Scholarships (2008) for research presented at the DAAMA International Symposium. Teaching responsibilities include courses on Human-Computer Interaction, Multimedia Systems, Data Compression, and Programming Languages. He actively participates in academic conferences and editorial boards, emphasizing interdisciplinary collaboration in technical and medical informatics.
Jon Calhoun is an Associate Professor in the Holcombe Department of Electrical and Computer Engineering at Clemson University, directing the Future Technologies in Heterogeneous and Parallel Computing (FTHPC) Laboratory. He holds a Ph.D. in Computer Science (2017) from the University of Illinois at Urbana-Champaign and dual B.S. degrees in Computer Science and Mathematics (2012) from Arkansas State University. Ph.D., Computer Science – University of Illinois at Urbana-Champaign B.S., Computer Science – Arkansas State University B.S., Mathematics – Arkansas State University His research focuses on fault tolerance and data compression in high-performance computing (HPC) systems. Key areas include silent data corruption (SDC) mitigation, error-bounded lossy compression for scientific workflows, and improving checkpoint-restart mechanisms. His work addresses critical HPC bottlenecks in data movement, storage, and reliability, with applications spanning exascale computing, medical imaging, and autonomous vehicle systems. Recent publications analyze compression trends in scientific data, including lossy techniques for DICOM files, federated learning communications, and real-time pedestrian detection systems. His group develops frameworks like SZ3 and LibPressio to optimize data management in HPC environments. NSF CAREER Award (2020) R&D 100 Award (2021) for SZ framework CECAS Junior Faculty Teaching Award (2022) Arkansas State Alumni Academy (2022) IEEE Senior Member (2022) The FTHPC Laboratory investigates heterogeneous computing systems, funded by the National Science Foundation, DOE, and the US Army. His research integrates fault injection analysis, data reduction techniques, and energy-efficient scalable solvers.
Dr.-Ing. Sebastian Bosse heads the Interactive & Cognitive Systems research group at Fraunhofer HHI in Berlin, Germany. His work integrates computational perception and cognition with machine learning and human-machine interaction , addressing applications in multimedia , augmented reality , medicine , agriculture , and industrial production . Research Interests include: Computational Perception & Cognition Machine Learning for Computer Vision Human-Machine Interaction in immersive environments AI-Driven Quality Assessment Neurophysiological Response Analysis Multi-View Gesture Recognition Recent Articles focus on multi-view coding , VR/AR applications , deepfake detection , agricultural AI , and neural quality metrics . Key collaborations span surgical robotics , explosive ordnance exploration , and agricultural phenotyping . Labs & Teams : Leads the Interactive & Cognitive Systems group at Fraunhofer HHI, specializing in pose/gesture analysis , contact-free interaction , and image quality estimation using subjective testing and neurophysiological data .
Yu Sun is a Professor in the Department of Computer Science and Engineering at the University of Central Arkansas (UCA). He holds a Ph.D. in Computer Science & Engineering from the University of Texas at Arlington. His research focuses on Multimedia Computing, Video Compression and Communication, Image Processing, and Wireless Videos. His work emphasizes optimizing video coding standards like HEVC, AVS2, and SHVC, with contributions to algorithms for rate control, intra prediction, and scalable video techniques. Research interests include developing efficient algorithms for 360-degree videos, generative adversarial networks (GANs) for image classification, and real-time video transmission over wireless systems. His publications span over two decades, addressing challenges in video compression efficiency, bufferless rate control, and neural network applications in multimedia systems. Dr. Sun’s articles highlight trends in hybrid coding strategies, probability-based optimization, and spatial/temporal scalability in video coding. His work bridges theoretical advancements and practical implementations for emerging technologies such as virtual reality video streaming and collaborative robotics. His academic webpage is available at https://faculty.uca.edu/yusun/ .
Bernd Münzer is a researcher at the Institute of Information Technology at Alpen-Adria-Universität Klagenfurt. His work focuses on medical multimedia, video retrieval systems, and human-computer interaction, particularly in endoscopic and laparoscopic video analysis. He contributes to projects involving deep learning for content identification, interactive exploration tools, and medical data visualization. Medical video analysis and retrieval Human-computer interaction in multimedia systems Endoscopic imaging and surgical skill assessment Dynamic content descriptors for video processing Collaborative video search frameworks Temporal analysis of clinical multimedia data His research includes developing tools like ECAT for endoscopic annotation, diveXplore for interactive exploration, and datasets such as Cataract-101 for surgical analysis. Projects emphasize medical multimedia applications, efficient video encoding, and augmented interfaces for clinical workflows.
Dr.-Ing. Henryk Richter is a postdoctoral scientific assistant at the University of Rostock, affiliated with the Communications Engineering research group. His work spans multimedia engineering, focusing on video coding and real-time systems. Education : PhD in Electrical Engineering from University of Rostock (2006), thesis on cross-standard video decoding concepts. Research Interests : Data compression, image/video coding, multistandard video decompression, and real-time signal processing. Publications : 15 most recent articles (2005–2024) highlight advancements in video codecs, biomedical signal processing, and hardware optimization. Contact : henryk.richter@uni-rostock.de
