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Detlev MarpeView profile

Researcher

• Video Coding
• Image Compression
• Signal Processing
• +3 more

Detlev Marpe is a leading researcher at the Fraunhofer Heinrich Hertz Institute (HHI), serving as Head of the Video Coding & Analytics Department and Head of the Image & Video Coding Group. His work focuses on advancing video compression standards, including HEVC (H.265) and its extensions. He has contributed significantly to tools like entropy coding, transform coding, and scalable video coding. His research emphasizes efficient compression techniques, such as adaptive context models and wavelet-based methods, with applications in multimedia communication and low-delay video encoding. Affiliations: Fraunhofer Institute for Telecommunications HHI, Berlin, Germany Roles: Department Head, Research Group Leader, and Adjunct Lecturer at TU Berlin (2013/14) Research Interests: Video coding standards (HEVC, H.264/AVC), entropy coding (CABAC), wavelet-based compression, scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimization. His work bridges theoretical advancements with practical implementations, addressing challenges in compression efficiency, scalability, and real-time applications. Publications & Awards: Over 200 publications in top-tier journals and conferences, including IEEE Transactions and SPIE. Notable awards include the Chester Sall Best Paper Award and multiple Best Paper Awards from IEEE journals. His contributions to video coding standards have been adopted in global specifications like MPEG and ITU-T. Grants & Labs: Involved in major research projects on HEVC extensions, 3D video coding, and low-delay applications. Collaborates with industry partners and academic institutions globally. His team at HHI develops reference software and test models for emerging standards.

• American Poetry
• Transnational/Asian American Studies
• Empire Studies
• +6 more

Dr. Mahshid Mayar is a DFG-funded Post-Doctoral Researcher at the North American Studies Program within the Department of English, American, and Celtic Studies at the University of Bonn. Her research focuses on intersections of literature and history, empire studies, childhood studies, and archives. She holds an academic blog at http://mahshid-mayar.me/blog/ and can be reached at mahshid.mayar@uibk.ac.at. Her research expertise spans American Poetry and Documental Protest Poetry Transnational/Asian American Studies (race & racialization) Empire Studies, including cartographies and age/race dynamics Childhood Studies and the history of education She also explores sound studies and archival politics, examining silences in cultural narratives. Her articles (2019–2025) critically analyze imperial pedagogies, cartographic scripts, and silences across disciplines. Notable works include studies on video games as cultural-political tools and the role of archives in shaping historical narratives. Dr. Mayar has organized conferences, workshops, and panels, reflecting her commitment to academic outreach. Her work bridges literary analysis with historical and geographical contexts, offering interdisciplinary insights into empire, childhood, and cultural memory.

Miguel MachullaView profile

Researcher

• Music Psychology
• Gender Studies
• Queer Studies
• +3 more

Miguel Machulla is a Researcher at the Department of Journalism and Communication Research, Hanover University of Music, Drama and Media. His academic background includes a Master's in Biology, Music, and Education from Ruhr University Bochum and Technical University Dortmund. He has held research positions at TU Dortmund, University of Cologne, and currently contributes to the BMBF-funded project Degree 5.0 focused on digital teacher education. Research Interests: Machulla's work bridges music psychology, gender/queer studies, and media interactions. He investigates how music shapes intergroup dynamics, supports LGBTQ+ identity formation, and enhances educational methodologies. Key themes include queer music analysis, media's role in environmental advocacy, and video-based reflective practices in pedagogy. Publications: His recent articles and conference papers (2023-2025) demonstrate empirical rigor in studying music's sociocultural impact, particularly through quantitative analysis of queer music characteristics, music functions in identity development, and innovative task designs for music education. Methodologically, he employs structural equation modeling, discourse analysis, and cross-cultural comparisons. Affiliations: Active member of the German Society for Music Psychology (DGM), Society for Music Research (GfM), and Research Hub at Hanover University. Associated with the Cologne Systematic Musicology Lab and BMBF project Degree 5.0 .

Lutz PrecheltView profile

Professor

• Software Engineering
• Empirical Software Engineering
• Agile Processes
• +5 more

Lutz Prechelt is Professor of Informatics at Freie Universität Berlin where he heads the Software Engineering research group (AG SE). His academic journey spans theoretical foundations, industry experience, and extensive empirical research in software engineering. He has held significant administrative positions including Executive Director of the Institute of Informatics and member of various academic senates. Prechelt's research interests have evolved from artificial intelligence and neural networks to empirical software engineering, with a recent focus on qualitative approaches to Agile processes and Pair Programming. His work has consistently emphasized methodological rigor, having conducted pioneering controlled experiments on topics like type-checking, inheritance depth, design patterns, and the Personal Software Process. His research has also ventured into plagiarism detection (JPlag), melody recognition, and qualitative studies of pair programming dynamics. His publication record shows a clear transition from quantitative to qualitative approaches in empirical software engineering. Early work featured controlled experiments on specific technical aspects of software development, while more recent publications focus on qualitative analysis of team dynamics, agile methodologies, and research methodology itself. A notable thread throughout his career has been platform comparison studies (Plat_Forms) examining how different technologies affect development outcomes. Ernst Denert Software Engineering Award 2020 Prechelt has served extensively as reviewer for major funding agencies including DFG, EU, ERC, and BMBF, and as editor for journals like Journal of Universal Computer Science and Transactions on Pattern Languages of Programming. He founded the Forum for Negative Results (FNR) to address publication bias in scientific research. His research group has developed several notable software tools including Saros for distributed pair programming, JPlag for plagiarism detection, and tools for maintainable lecture videos.

Anestis TerzisView profile

Professor

• Digital System Design
• FPGA Technology
• Camera Monitor Systems
• +6 more

Professor Anestis Terzis serves as a Professor of Digital Systems Design and Head of the Institute for Communication Technology (IKT) at Technische Hochschule Ulm within the Faculty of Electrical Engineering and Information Technology. He coordinates the International Electrical Engineering Program and is responsible for the Vehicle Systems specialization. His office is located in Room W2405 at Albert-Einstein-Allee 53-55, 89081 Ulm, Germany. Professor Terzis specializes in digital system design with focus on FPGA, VHDL, and high-level design methodologies. His research spans Camera Monitor Systems (CMS) for automotive mirror replacement, advanced camera-based driver assistance systems, vehicle electronics, digitalization in laboratory didactics, and autonomous driving technologies. He has pioneered work in digital mirror systems compliant with ISO 16505 standards and has contributed significantly to image processing for automotive applications. His publication record demonstrates consistent contributions to automotive imaging technology, with recent work focusing on image compression impacts on detection quality, CMS image quality parameters, and programmable processing for autonomous vehicles. Professor Terzis has edited the comprehensive Handbook of Camera Monitor Systems published by Springer, establishing himself as a leading authority in this specialized field. As Head of the Institute for Communication Technology, he leads research initiatives connecting digital systems design with automotive applications. He also serves as Coordinator for the Study with In-depth Practice program and is a member of the Institute for Vehicle System Technology (IFS), demonstrating his commitment to both theoretical advancement and practical implementation in automotive electronics.

Ulrike Pestel-SchillerView profile

Researcher

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +2 more

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

Juraj SomorovskyView profile

Professor

• System Security
• Network Security
• Cryptography
• +3 more

Prof. Dr.-Ing. Juraj Somorovsky is a faculty member at Paderborn University, serving as Head of the System Security department within the Faculty of Computer Science, Electrical Engineering and Mathematics. His work focuses on practical security analysis and censorship circumvention techniques. Academic Rank: Professor Department: Computer Science Office: F2.315, Fürstenallee 11, Paderborn Research Interests span multiple subfields of cybersecurity including: Internet censorship resistance TLS protocol security 3D printing vulnerabilities E-learning platform security Network traffic obfuscation Cyber-physical system threats Recent publications demonstrate expertise in combining applied cryptography with practical security analysis. He leads the System Security group and contributes to the NERD postgraduate research training group focused on digitalization challenges.

• Internet Law
• Data Protection Law
• Information Technology Law
• +3 more

Sebastian Brüggemann is a German legal scholar specialising in Internet, data-protection and IT law. Since 2013 he has been a part-time lecturer (Lehrbeauftragter) at the Faculty of Law of Eberhard Karls University of Tübingen, and he holds parallel teaching appointments in data-protection and IT law at the Universities of Applied Sciences in Kehl and Ludwigsburg as well as at ZHAW School of Management and Law in Winterthur, Switzerland. In private practice he works as in-house counsel (Syndikusrechtsanwalt) for a large international IT company, focusing on data-protection and technology law. Education: 2003-2011: Studies of Law and General Rhetoric, Eberhard Karls University of Tübingen 2009: First State Law Examination, Tübingen 2011: Magister Artium in General Rhetoric, Tübingen 2010-2012: Legal clerkship (Referendariat), including stages at the German University of Administrative Sciences Speyer and eco – Verband der Internetwirtschaft 2012: Doctorate in Law (Dr. iur.), Tübingen – dissertation on third-party disclosure claims against Internet providers in copyright enforcement 2013: Admission as attorney (Rechtsanwalt) 2013-2015: Part-time LL.M. in Information Technology Law, Carl von Ossietzky University of Oldenburg Research interests span the intersection of law and digital technology: data-protection compliance under the GDPR, cybersecurity regulation, copyright in digital media, network neutrality, M2M/IoT legal issues, and the legal framework of behavioural advertising. His work frequently analyses German, European and comparative US developments. He has authored a monograph, numerous commentaries to GDPR/BDSG provisions, model IT contracts, and more than two dozen peer-reviewed articles and case notes since 2013, identifying recurring themes such as data portability, ISP liability, streaming and copyright, and the challenges of Industrie 4.0. Professional memberships & science communication: editorial board of Privacy in Germany , core team member of the Telemedicus IT-law portal, member of IAPP, DGRI and GRUR, and co-author of sector guidelines issued by Bitkom and eco association. There is no indication of supervised doctoral or master’s students in the supplied material; his teaching activities concentrate on specialised continuing-education courses and part-time lectures.

Ali C. BegenView profile

Professor

• Multimedia Networking
• Adaptive Streaming
• Low-Latency Live Streaming
• +7 more

Ali C. Begen is a Professor in the Computer Science Department at Ozyegin University in Istanbul, Turkey. He is also the founder of Networked Media , a technology consultancy specializing in IP video solutions. Prior to academia, he was a Technical Lead at Cisco in San Jose, California, and has over 40 US patents in media transport protocols. Education: PhD in Electrical and Computer Engineering (Georgia Tech, 2006); BSc in Electrical Engineering (Bilkent University, 2001) Professional Recognition: Emmy® Award for Technology and Engineering (2022); ACM SIGMM Test of Time Paper Award (2022); SVTA Fellow (2023); IEEE Distinguished Lecturer (2016-2018) Research Focus: Begen’s work bridges low-latency live streaming , media-over-QUIC transport (MOQ) , and adaptive streaming protocols (DASH, WebRTC) . His recent research explores unifying real-time communication and content delivery under a single protocol to reduce complexity and improve scalability. Publications: Recent articles analyze bandwidth prediction , QUIC prioritization , and cross-protocol collaboration , reflecting a trend toward data-driven, network-aware media delivery systems. Honors: Emmy® Award (2022) ACM SIGMM Test of Time (2022) IEEE Distinguished Lecturer (2016, re-elected 2018) SVTA Fellow (2023) Royal Society Newton Fellowship (2019) Advising & Collaborations: Begen has served on thesis committees for over 15 PhD/MS students at institutions like Ozyegin University, University of Klagenfurt, and Koc University. He actively contributes to standards bodies (IEEE, ACM) and industry consortia like the Streaming Video Technology Alliance.

Aljaž BožičView profile

Researcher

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +7 more

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.

Benjamin BrossView profile

Part-Time Lecturer

• Video Coding
• Signal Processing
• Machine Learning
• +3 more

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.

• Computer Architecture
• Hardware Acceleration
• Deep Learning
• +6 more

Hyuk-Jae Lee is a prominent researcher in computer architecture and hardware acceleration for deep learning systems, with an extensive publication record spanning over two decades. His work primarily focuses on hardware implementations for video processing, memory systems, and neural network acceleration. Through numerous collaborations with researchers at Korean institutions (particularly with Hyun Kim, Chae-Eun Rhee, and Xuan Truong Nguyen), Lee has established himself as a leading figure in circuit design for AI applications. Lee's research interests center around computer architecture, hardware acceleration, deep learning systems, video coding and compression, memory systems, and image processing. His work demonstrates a consistent focus on bridging the gap between theoretical algorithms and practical hardware implementations, with particular emphasis on optimizing performance and efficiency for real-world applications. His recent work shows a strong shift toward accelerating large language models and transformer-based architectures, reflecting current trends in AI hardware. Analysis of Lee's recent publications (2023-2025) reveals a clear research trajectory toward solving memory bandwidth and computational efficiency challenges in modern AI systems. His work spans the spectrum from low-level circuit design to high-level system architecture, with particular strength in memory systems optimization and hardware acceleration for neural networks. The consistent publication record in top-tier IEEE journals demonstrates sustained research productivity and impact in the field. Throughout his career, Lee has collaborated extensively with a core group of researchers, suggesting stable research teams and laboratories focused on hardware acceleration. His publications in IEEE Transactions on Circuits and Systems, IEEE Transactions on Computers, and IEEE Transactions on Video Technology indicate recognition by multiple relevant academic communities.

• Computational Neuroscience
• Robotics
• Machine Learning
• +3 more

Florentin Wörgötter is a faculty member at the Department for Computational Neuroscience , Georg August University of Göttingen, Germany. His research bridges robotics , computational neuroscience , and machine learning , focusing on action prediction, neural networks, and human-robot interaction. Key Research Areas : Action segmentation, semantic decomposition of manipulation sequences, 3D object reconstruction, and sensor fusion for infant movement classification. Recent Trends : Combining task-dependent learning with optimal path search, using foundation models for graph-based action recognition, and improving CNN interpretability through influence functions. He collaborates extensively with researchers like Minija Tamosiunaite , Tomas Kulvicius , and Poramate Manoonpong , contributing to journals such as NeuroImage , Robotics and Autonomous Systems , and IEEE Transactions on Neural Networks . His work often integrates deep learning , semantic reasoning , and biologically inspired models for robotic applications.

Kristian KerstingView profile

Associate Professor

• Data Mining
• Machine Learning
• Statistical Relational Artificial Intelligence
• +5 more

Kristian Kersting is an Associate Professor in the Computer Science Department at Technical University of Darmstadt, Germany, having moved from TU Dortmund in 2017. He holds additional affiliations including Adjunct Assistant Professor roles and has contributed to institutions like the University of Bonn and Wake Forest University. His research focuses on data mining, machine learning, and statistical relational AI, with applications in medicine, plant phenotyping, and traffic analysis. He has authored over 150 peer-reviewed publications and received numerous awards, including the ECCAI Dissertation Award 2006 and Best Student Paper Award at ECML 2006. Education includes a Ph.D. from the University of Freiburg and postdoctoral research at MIT. He has held roles such as Assistant Professor at the University of Bonn and co-chaired major conferences like ECML PKDD 2013. His work emphasizes interdisciplinary applications, including collaborations with industries like goedle.io and pflegix.de. Scientific contributions span probabilistic logic learning, lifted inference, and neuro-symbolic systems. His research group's activities include projects on plant disease detection, traffic prediction, and collective attention analysis. Key awards highlight his impact in AI and data science.

• Video Quality Assessment
• Medical Imaging
• Light Field Displays
• +6 more

Maria G. Martini is a Professor at Kingston University London, UK, with an extensive research portfolio spanning over two decades in multimedia quality assessment, video compression, and medical imaging. Her work demonstrates strong international collaboration, particularly with European researchers including Péter A. Kara (31 publications) and Nabajeet Barman (30 publications). Her primary research interests focus on Video Quality Assessment , Medical Imaging , Light Field Displays , Neuromorphic Vision Sensors , and Quality of Experience modeling. Recent work has centered on medical image quality assessment, neuromorphic vision sensor data compression, and gaming video streaming applications, reflecting her ability to adapt to emerging technologies while maintaining core expertise in quality metrics. Analysis of her recent publications (2022-2025) reveals a strong trend toward specialized quality assessment methodologies for emerging visual technologies, including neuromorphic sensors, light field displays, and medical imaging applications. Her work bridges theoretical quality metrics with practical implementation challenges, often addressing standardization needs and dataset documentation to improve research reproducibility. Martini has contributed significantly to quality metric standardization efforts, particularly regarding the Bjøntegaard Delta metric and SSIM-PSNR relationships for compressed content. Her recent publications in IEEE Transactions and other high-impact journals demonstrate continued research leadership in the field. Her research methodology consistently combines objective quality metrics with subjective evaluation frameworks, addressing both technical implementation challenges and human perception aspects. This dual approach has positioned her work as influential in both academic and standardization contexts.