جست‌وجوی استاد و پژوهشگر

• Digital Health
• Social Innovation
• Human Activity Recognition
• +۳ مورد دیگر

Jakob Doppler is a full-time Professor at the St. Pölten University of Applied Sciences , where he leads the Center for Digital Health and Social Innovation and the Department of Media and Digital Technologies . He spearheads master's programs in Citizen-Centered Digital Health and Social Care and Digital Healthcare , integrating technology with social care solutions. His research focuses on context computing , human activity recognition , and music information retrieval , with applications in elderly care, rehabilitation technology, and digital innovation. Projects like Smart Companion , EyeQTrack , and SONIGait demonstrate his work in AI-assisted living, XR training, and wearable sonification devices for Parkinson's patients. Publications span digital health frameworks , gait rehabilitation systems , and user-centered design for aging populations. His work bridges computer science and health sciences , emphasizing interdisciplinary collaboration and real-time feedback mechanisms . He actively contributes to ambient assisted living through multi-device platforms and smart home environments .

Massimo Callisto De Donatoمشاهده پروفایل

استادیار

• Internet of Things
• Digital Twin
• Machine Learning
• +۶ مورد دیگر

Massimo Callisto De Donato is an Assistant Professor specializing in Internet of Things, Digital Twin, and Machine Learning applications for real-world systems. His research addresses critical challenges in environmental management, workplace safety, and disaster response through advanced computing solutions. His primary research domains include: IoT infrastructure for real-time industrial and environmental monitoring Digital Twin development for process simulation and prediction Machine Learning frameworks for safety-critical applications Human behavior analysis using community detection algorithms ICT systems for earthquake rescue operations Analysis of his 2020-2025 publications reveals a consistent trajectory toward integrating Digital Twin and IoT technologies with machine learning for predictive capabilities. Key application areas include wastewater management (decanter centrifuge modeling), smart living environments, and earthquake disaster response systems, with increasing emphasis on real-time simulation and human activity recognition. No scientific awards are documented in the available information. Details regarding student supervision, research grants, and laboratory affiliations are not provided in the source materials.

• Multiprocessor Systems
• Real-Time Embedded Computing
• Distributed Signal Processing
• +۳ مورد دیگر

Prof. Dr. Matthias Rosenthal is a Professor of Multiprocessor and Real-Time Systems at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW), where he also serves as Head of the Research/Focus Area Realtime Platforms. He holds a PhD and MSc in Electrical Engineering from ETH Zurich (1993–1997). His research focuses on multiprocessor systems, hybrid multicore architectures, distributed signal processing, embedded GPU computing, and real-time embedded systems. Key projects include In-Flight GNSS Interference Detection, dAIrector (automated multi-camera live production), and novel AFM techniques for industrial quality control. He has led over 15 industry-focused projects, including collaborations with Innosuisse and companies like Harman International. His work emphasizes real-time systems, FPGA-GPU co-design, and embedded AI solutions. Education: PhD (ETH Zurich, 1997), MSc (ETH Zurich, 1993) Awards: CTI Startup Label (2005) Teaching: Lectures on digital systems, real-time computing, and information theory Notable contributions include advancements in embedded machine learning for food waste management, secure boot concepts for Zynq MPSoC, and low-latency wireless video systems. His research bridges theoretical computer engineering with practical industrial applications.

Michael Zinkمشاهده پروفایل

استادیار

• Network Protocols
• Multimedia Distribution
• Wireless Networks
• +۵ مورد دیگر

Michael Zink is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst and an adjunct assistant professor in the Manning College of Information and Computer Sciences (CICS). He is affiliated with the Microwave Remote Sensing Laboratory and has held roles including assistant research professor in computer science. His research focuses on network protocols, multimedia distribution systems, and high-bandwidth data networks, with contributions to the KOMSSYS streaming platform and FPGA-based network architectures. Zink's work spans topics like secure video streaming, LED wall calibration, and federated learning for sustainable UAV operations. He received the Best Paper Award at Multimedia Computing and Networking (2008). Education: PhD from Darmstadt University of Technology. Research Interests: Sensor networks, wireless and wired multimedia distribution, network protocol optimization, video streaming security, and distributed cloud platforms. Recent work includes innovations in hue-preservation for virtual production and carbon-efficient federated learning (CEFL). Technical Contributions: Developed systems for real-time route configuration of entertainment content, scanner noise elimination in film restoration, and profile-based HDR content generation. His team explores network-attached FPGAs in cloud environments and edge-to-cloud workflows for scientific applications. Labs & Teams: Active in the Microwave Remote Sensing Laboratory and collaborates with the Open Cloud Testbed (OCT) for cloud infrastructure research. Engaged in federated cyberinfrastructure projects like the ERN Cryo-EM Pilot.

• Cybersecurity
• Data Protection
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Chaminda Hewage serves as Interim Associate Dean for Research and Reader (Associate Professor) in the Cardiff School of Technologies at Cardiff Metropolitan University. He leads the Computer Security Programme and holds a Ph.D. in Multimedia Communications from the University of Surrey (2009) and a B.Sc. in Electrical and Information Engineering from the University of Ruhuna (Sri Lanka). His research focuses on cybersecurity, data protection in AI/ML systems, blockchain applications, and multimedia communication security. He has authored over 50 peer-reviewed publications and edited major books on emerging technologies' privacy challenges. Education Background: B.Sc. (Hons) Electrical & Information Engineering, University of Ruhuna (2004) Ph.D. in Multimedia Communications, University of Surrey (2009) Postgraduate Certificate in HE Teaching & Learning, Kingston University (2014) Research Interests: Data protection mechanisms in AI/ML Cybersecurity frameworks for Industry 4.0 Blockchain for public and private sector applications Metaverse privacy and security Edge AI and embedded system security Regulatory compliance in digital transformation Recent Contributions: Co-edited the 2024 Springer volume Data Protection: The Wake of AI and Machine Learning , published influential works on Kubernetes security, V2X communication cryptography, and metaverse privacy. Active in international conferences including C3AI and ICCS. Awards & Recognition: Gold Medal for Academic Excellence (2004), University of Ruhuna ORS Scholarship (2005), HEFCE Fellow of the Higher Education Academy Grants & Collaborations: Leads research projects on cybersecurity for SMEs, smart city infrastructure, and digital transformation. Engages with industry partners through Cardiff Met's innovation networks. Advises on UK government cyber policy initiatives. Labs/Teams: Oversees the Cybersecurity Research Group and collaborates with the ZERO2FIVE Food Industry Centre for IoT applications. Active in multidisciplinary teams addressing Industry 5.0 challenges.

Alexander Carôtمشاهده پروفایل

استاد

• Networked Music Performance
• Real-Time Audio Streaming
• Telecommunication Systems
• +۳ مورد دیگر

Prof. Dr. Alexander Carôt is a Professor of Media Informatics at Anhalt University of Applied Sciences, leading the Faculty of Computer Science and Languages as its Dean. His work bridges physics, computer science, and music, with a focus on networked music performance and low-latency audio systems. He developed the SoundJack software, enabling remote collaborative music-making, and contributed to projects like FAST-MUSIC and 5GUK trials for distributed music sessions. His research spans telemedicine applications, real-time streaming optimization, and quantum effects in telecommunications. Education: Ingenieursdiplom (2004) Doctorate in Engineering (2009) Research Interests: Prof. Carôt explores interdisciplinary fields combining technology and music. Key areas include: Real-time audio networking for live performances 5G-enabled tele-music systems Medical tele-rehabilitation using music feedback Low-latency multimedia streaming protocols Grants & Projects: Led initiatives like the FAST-MUSIC project and 5G trials, focusing on distributed music collaboration. Active in EU-funded research on telematic systems and networked audio. Labs/Teams: Directs the SoundJack development team and collaborates with industry partners on 5G infrastructure for arts and healthcare.

Vlad Popescuمشاهده پروفایل

دانشیار

• Telecommunications
• Cognitive Radio Systems
• Software Defined Radio
• +۷ مورد دیگر

Vlad Popescu is an Associate Professor at the Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Technical University of Brasov. His research focuses on telecommunications, IoT, cognitive radio systems, and multimedia applications. He has authored numerous publications in top journals and conferences, including work on 6G-enabled smart cities, edge-cloud orchestration, and QoE-driven smart home systems. His research explores cutting-edge topics such as immersive multi-sensory experiences, real-time object detection, and energy-efficient architectures for Android TV integration. Popescu has contributed to open-source satellite simulators and pioneered studies on blockchain-based TV advertising systems. His work bridges theoretical advancements with practical implementations in smart home environments, IoT gateways, and next-generation wireless networks. Award-winning research includes innovations in SDR-based satellite gateways and hardware-software partitioning for embedded systems. His collaborative projects address challenges in distributed video applications, D2D communications, and social IoT frameworks for smart tourism management.

Peter Uhrigمشاهده پروفایل

استاد

• Cognitive Linguistics
• Construction Grammar
• Computational Linguistics
• +۴ مورد دیگر

Peter Uhrig is a Professor of Digital Linguistics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), focusing on Big Data applications in linguistics. Previously, he held roles as interim professor at the University of Osnabrück (Computational Linguistics & Cognitive Science) and University of Leipzig (English Linguistics). His research integrates cognitive linguistics, corpus linguistics, and computational methods, emphasizing large-scale data analysis and open datasets. Education and career highlights include a PhD and Habilitation from FAU, leadership at TU Dresden’s Center for Scalable Data Analytics, and collaborations with the Max-Planck-Institute for Psycholinguistics. He has secured grants including a 1.1M core-hour NHR award for Russian media analysis and led projects like the Distributed Little Red Hen Lab (Google Summer of Code 2018-2019). Key achievements: Winning the EmpiriST tokenization contest (2018), co-developing Singularity containers for OpenPose, and advancing multimodal corpus tools like NewsScape. Current research emphasizes machine learning applications in linguistic structures and multimodal cognition.

• Next-Generation Wireless Networks
• Wireless Ad Hoc and Sensor Networks
• Cognitive Radio Networks
• +۵ مورد دیگر

Prof. Dr. V. Çağrı GÜNGÖR is a Professor and Department Chair of Computer Engineering at Abdullah Gül University, Kayseri, Turkey. He holds a Ph.D. from Georgia Institute of Technology and has been actively involved in research and academia since 2001. His primary roles include leading the Computer Engineering department and contributing to editorial boards of prestigious journals. Education: B.S. and M.S. in Electrical and Electronics Engineering (Middle East Technical University, 2001/2003); Ph.D. in Electrical and Computer Engineering from Georgia Tech (2007). Research focuses on next-generation wireless networks, smart grid communications, cognitive radio networks, underwater acoustic networks, and AI-driven data mining. His work bridges theoretical advancements with practical applications in smart grids and vehicular networks, emphasizing reliability and efficiency. Notable awards include TUBITAK Young Scientist Award (2017), BAGEP (2017), and IEEE Best Paper Awards (2012, 2006). He leads multiple research projects funded by TÜBİTAK, EU Marie Curie, and industry partners like Alcatel-Lucent and Eaton Corporation. Grants and projects include developing low-power WSNs for structural health monitoring, underwater acoustic networks, and smart grid communication frameworks. He has co-authored over 100 peer-reviewed publications and edited books on industrial wireless networks. Teaching courses include Computer Networks, Wireless Sensor Networks, and Capstone Design Projects. His lab explores cutting-edge solutions in IoT, vehicular networks, and AI-driven network optimization.

• Distributed Databases
• Object-Oriented Databases
• Internet Databases
• +۹ مورد دیگر

Erich Neuhold is a Professor of Computer Science at the University of Vienna and Darmstadt University of Technology. He holds honorary professorships at several institutions, including the University of Guiyang and the New Jersey Institute of Technology. His career spans over five decades, with roles as Director of the Fraunhofer Institute for Integrated Publication and Information Systems (IPSI) and professorships at multiple universities globally. Education: Erich Neuhold earned a Dipl.-Ing. (M.S.) in Electronics and a Dr.-techn. (PhD) in Mathematics and Computer Science from the Technical University of Vienna. He holds special honors in his academic career, including Fellowships from IEEE and the Gesellschaft für Informatik. Research Interests: His work focuses on distributed databases, object-oriented systems, internet databases, information retrieval, semantic web, interoperability, and applications in digital libraries, e-science, and e-government. He pioneered advancements in transaction models, multimedia databases, and semantic integration frameworks. Publications: Over 200 papers and four books, with recent contributions including retrospectives on computing history and foundational work in supercomputing architectures. His articles reflect deep engagement with historical and future trends in computer science. Awards: Recognitions include the Meritorious Service Award from IEEE, medals from academic institutions, and honorary professorships worldwide. Grants & Labs: Led projects like the POREL distributed database system and contributed to initiatives like the EU’s FRESCO e-science cockpit. His work spans academic-industrial collaborations, with budget leadership at IPSI (€10M/year). Labs/Teams: Co-founded the Fraunhofer IPSI and directed research teams in Austria, Germany, and internationally, emphasizing interdisciplinary collaboration in information systems.

• Natural Language Processing
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Aditya Sharma is a researcher at the University of California Santa Barbara , focusing on Natural Language Processing , Computer Vision , and Artificial Intelligence . His work bridges vision-language models, medical AI evaluation, and knowledge graph reasoning. PhD : Advancing AI Understanding in Language & Vision (UCSB, 2024) Research Interests: • Vision-Language Models for geometric reasoning and mixed reality • Medical LLM evaluation and hallucination reduction • Temporal knowledge graph reasoning and multimodal systems Article Trends: Recent publications emphasize vision-language integration (e.g., GeoCoder, OCTO+), LLM reliability in medical contexts (MedHELM), and temporal reasoning (TwiRGCN). He explores applications in dark web security , healthcare diagnostics , and energy-efficient computing .

• Robotics
• Machine Learning
• Electrical Engineering
• +۳ مورد دیگر

Xinyu Qin is a Professor at the Department of Electrical and Computer Engineering within the School of Information Engineering at Guangdong University of Technology. His research focuses on advanced robotics, signal processing, and integrated circuit design, contributing to fields like multi-manipulator systems and Delta-Sigma modulators. Education: Affiliated with prestigious institutions through collaborative research Research Interests: Robotics, Machine Learning, Electrical Engineering His recent publications (2023-2025) demonstrate expertise in robotic task allocation, high-speed circuit design, and explainable AI for healthcare. Award-winning work includes Interactive Explainable Deep Survival Analysis (2024) and SVP: Safe and Efficient Speculative Execution Mechanism through Value Prediction (2023). Key collaborations involve Guoxing Wang and Liang Qi across 16 records. Current projects involve optimizing convolutional neural network accelerators, analyzing atmospheric river impacts on Greenland's crustal deformation, and advancing MASH Delta-Sigma modulator architectures. His work bridges theoretical innovation with practical applications in smart energy systems and autonomous robotics.

• Artificial Intelligence
• Multimedia Systems
• Robotic Interaction
• +۵ مورد دیگر

Wang Yufei is affiliated with Xiamen University, China, holding positions across the School of Informatics, Institute of Artificial Intelligence, Center for Digital Media Computing, and School of Film. The research spans interdisciplinary domains, integrating artificial intelligence, robotics, and multimedia systems for real-world applications. Wang Yufei's research primarily focuses on artificial intelligence , computer vision , robotic interaction , and multimedia systems . Key areas include 3D scene modeling using Gaussian splatting, diffusion models for storytelling and image generation, reinforcement learning for interactive instruction, and AI applications in healthcare, agriculture, and urban planning. The work emphasizes robustness, real-time feedback, and human-centered design. The recent publications demonstrate a strong trend in deep learning applied to vision and multimodal systems , with increasing focus on interactive AI , embodied environments , and domain adaptation . Topics such as differentiable trajectory optimization, backdoor attacks in compression models, and context-aware assistance systems reflect both technical depth and societal relevance. Wang Yufei has not been mentioned in relation to any scientific awards in the provided text. No information is available regarding student advising or research grants. However, the breadth and volume of publications suggest active research leadership and likely involvement in funded projects. Research is conducted in collaboration with teams at Xiamen University, particularly within the Institute of Artificial Intelligence and the Center for Digital Media Computing, focusing on intelligent multimedia and robotic systems. The integration of AI with cultural and educational applications, such as piano instruction and heritage preservation, highlights a unique interdisciplinary lab environment.

Tokunbo Ogunfunmiمشاهده پروفایل

استاد

• Deep Learning
• Artificial Neural Networks
• Adaptive Signal Processing
• +۷ مورد دیگر

Tokunbo Ogunfunmi is a Professor of Electrical and Computer Engineering and Director of the Information Processing and Machine Learning (IPML) Research Lab at Santa Clara University's School of Engineering. He served as Associate Dean for Research and Faculty Development from 2010-2014 and as Associate Dean for Mission, Culture and Inclusion from 2021-2024. Dr. Ogunfunmi holds a Ph.D. and M.S. from Stanford University (1990, 1984) and a B.S. from the University of Ife, Nigeria (1980). His research interests span deep learning, artificial neural networks, adaptive/nonlinear signal processing, digital signal processing, and multimedia signal processing with VLSI/DSP/FPGA implementations. Dr. Ogunfunmi has published 4 books and over 200 refereed journal and conference papers in these areas. His recent publications demonstrate strong focus on deep learning applications, hardware acceleration for neural networks, quaternion-based adaptive algorithms, and efficient video/speech processing techniques. Dr. Ogunfunmi has received numerous awards including the SCU Presidential Special Recognition Award (2020), IEEE Meritorious Service Awards (2021, 2013), Carnegie Foundation Visiting Professorships (2019, 2015), and multiple Best Paper Awards. He has served in significant editorial roles including Senior Associate Editor for IEEE Signal Processing Letters and Associate Editor for several IEEE Transactions. As an active member of the professional community, he is a Senior Member of IEEE, Member of Sigma Xi, and Member of AAAS. His industrial experience includes consulting for major companies such as Broadcom, AMD, NEC, AT&T Bell Labs, and NIKON Precision Research & Development. Dr. Ogunfunmi is also a registered professional engineer in California.

• Quality of Experience (QoE) of Internet applications
• Artificial intelligence and machine learning for communication networks
• Measurement and analytics of encrypted network traffic
• +۵ مورد دیگر

Prof. Dr. Michael Seufert is a Full Professor and Chairholder at the University of Augsburg since October 2023, leading the Chair of Networked Systems and Communication Networks within the Faculty of Applied Computer Science. Previously, he served as a Private Lecturer at the University of Würzburg and completed his Habilitation in Computer Science there in 2023. His academic journey includes a PhD in Computer Science from the University of Würzburg (2017) with a thesis on Quality of Experience and Access Network Traffic Management of HTTP Adaptive Video Streaming, which earned him two prestigious Best Dissertation Awards. His research focuses on Quality of Experience (QoE) of Internet applications, artificial intelligence and machine learning for communication networks, measurement and analytics of encrypted network traffic, and data-driven proactive user-centric network management solutions. His work bridges theoretical foundations with practical applications in network monitoring, security, and performance optimization. Recent research has increasingly incorporated machine learning techniques to address challenges in network management, traffic analysis, and user experience assessment. Prof. Seufert's publication record shows a clear evolution from foundational work on QoE modeling and HTTP adaptive streaming toward more sophisticated applications of machine learning in network operations. His recent publications (2023-2025) demonstrate a strong emphasis on practical ML applications for network monitoring, security, and optimization, with particular attention to real-world constraints and performance requirements. The research spans multiple subfields including encrypted traffic analysis, explainable AI for network management, edge computing, and quality assessment for emerging applications. CNOM Young Professional Award of the IEEE Communications Society (ComSoc) Technical Committee on Network Operations & Management (CNOM) (2024) Best Dissertation Award 2018 of KuVS (Communication and Distributed Systems) special interest group Best Dissertation Award of the IEEE Communications Society (ComSoc) Technical Committee on Network Operations & Management (CNOM) Prof. Seufert actively supervises research projects and advises students through thesis projects and practical modules. His teaching portfolio includes Communication Systems, Management of Communication Networks, Practical Introduction to Internet-Technologies, and seminars on Networked Systems. He leads a research group focused on networked systems and communication networks, with current projects exploring machine learning applications in networking, as evidenced by the organization of the 3rd International Workshop on Machine Learning in Networking (MaLeNe 2025).