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Christine GuillemotView profile

Visiting Professor

• Image Processing
• Computer Vision
• Video Compression
• +3 more

Christine Guillemot is a Visiting Professor at STC Research Centre, Mid Sweden University, and Research Director at Inria (France). She holds a PhD from ENST Paris (1985–1997) and previously worked at France Telecom, Bellcore (USA), and as Director of Research at Inria. Her research focuses on visual data modeling, compression, and processing (2D/3D video, light fields, 360° video) for applications in multidimensional imaging and visualization. She leads a team of 20 researchers at Inria and is Senior Area Editor of IEEE Trans. on Image Processing. Education: PhD in Telecommunications, ENST Paris (Year unspecified) Research Interests: Christine’s work spans image and video compression, 3D capture and visualization, and signal processing for multimedia. She emphasizes practical applications of advanced imaging technologies, including multiscopic 3D systems and light field analysis. Editorial & Leadership: She serves as Senior Area Editor for IEEE Transactions on Image Processing and regularly participates in major conferences (e.g., SPIE-VCIP, IEEE-ICIP). Her team at Inria develops cutting-edge solutions for visual data representation and communication. Labs/Teams: Head of a research group at Inria and affiliated with the Realistic 3D research group at STC, focusing on multidimensional imaging and visualization.

Yun Raymond FuView profile

Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Yun Raymond Fu is the COE Distinguished Professor of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Sciences. His research focuses on Artificial Intelligence, Machine Learning, Computer Vision, and Big Data Analytics, leading the SMILE Lab. He holds a PhD from the University of Illinois at Urbana-Champaign and joined Northeastern in 2012. His work spans anomaly detection, video analysis, and multimodal learning, with over 200 publications in top journals and conferences. Education: PhD in Electrical and Computer Engineering, University of Illinois, 2008. Research Interests: Machine Learning, Computer Vision, Pattern Recognition, Deep Learning, and Cyber-Physical Systems. His lab develops algorithms for video anomaly detection, social media analytics, and domain adaptation. Awards: Includes 7 Young Investigator Awards, 12 Best Paper Awards, and fellowships from IEEE, IAPR, AAAS. Recently recognized as a AAAI Fellow and recipient of the Edward J. McCluskey Technical Achievement Award (2024). Labs & Entrepreneurship: Founded AI startup Giaran (acquired by Shiseido in 2017). SMILE Lab explores cutting-edge AI applications in healthcare, autonomous systems, and multimedia.

• Video Streaming
• Virtual Reality
• Multi-Access Edge Computing
• +3 more

Matti Siekkinen is a Lecturer in the Department of Computer Science at Aalto University. He is also affiliated with the Helsinki Institute for Information Technology (HIIT), the Professorship Di Francesco Mario, and the Computer Science Lecturers group. His research focuses on latency-critical mobile multimedia services, cloud-based virtual reality (VR), and edge computing solutions to optimize network performance and user experience. Education: Doctoral degree in Engineering and Technology (2007) from Université Côte d'Azur Master's degree in Engineering and Technology (2003) from Helsinki University of Technology Research Interests: His work emphasizes improving Quality-of-Experience (QoE) in mobile and cloud gaming, optimizing video streaming for VR applications, and deploying edge computing to reduce latency. Key topics include foveated rendering, dynamic resource allocation, and energy-efficient mobile service delivery. He has led projects such as CloudVR (2019–2020) and Latency-Critical Mobile Multimedia Services (2016–2019), which investigate network architecture and latency reduction strategies. Recent Research Trends: Recent publications highlight advancements in foveated compression techniques, head-pose prediction for remote VR, and neural network-based depth map packing. His work often bridges theoretical models with practical implementations in edge and cloud environments. Grants & Projects: Principal Investigator for CloudVR: High-Quality Mobile VR Using Cloud Acceleration (Business Finland) Principal Investigator for Latency-Critical Mobile Multimedia Services (Academy of Finland) Labs & Groups: He leads the Siekkinen Matti group, focusing on innovative solutions for mobile and cloud-based multimedia systems. Collaborations include Tsinghua University and industry partners like Elisa.

Alexis LechervyView profile

Assistant Professor

• Multimodal Learning
• Frugal AI
• Metric Learning
• +3 more

Alexis Lechervy is an Assistant Professor at the University of Caen within the GREYC Laboratory's Image team. His research develops machine learning methods for efficient multimedia analysis and retrieval systems. Core research themes include: Multi-exit neural architectures for adaptive computation Multimodal fusion strategies for visual and textual data Metric learning for content-based retrieval Interactive learning systems for human-AI collaboration Resource-efficient deep learning models Dr. Lechervy's work bridges fundamental machine learning research with applications in medical imaging, biometrics, and multimedia retrieval. His algorithms optimize the balance between computational efficiency and predictive accuracy in resource-constrained environments.

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Dr. Maryam Haghighat is a Lecturer at QUT's School of Electrical Engineering & Robotics, specializing in Machine Learning, Computer Vision, and Robotics. She holds a PhD from UNSW's School of Electrical Engineering and Telecommunications, where her thesis earned the 2020 UNSW Award for Outstanding Doctoral Thesis. Her postdoctoral research at the University of Oxford's Big Data Institute (2020–2022) focused on medical image analysis for the PathLAKE project. Currently, she leads AI projects with over $10M in funding, including ARC grants and industry collaborations. Her research spans applications in robotics, healthcare, and remote sensing. Notable contributions include developing machine learning algorithms for histopathology image analysis and robust multi-robot localization systems. Awards include the UNSW Doctoral Thesis Award (2020). She actively supervises PhD students in topics like multimodal fusion and 3D visual understanding. Publications highlight advancements in AI-driven medical imaging, hyperspectral transformers, and illumination-aware video compression. Her work bridges theoretical machine learning with practical applications in healthcare, robotics, and environmental monitoring.

Håkon Kvale StenslandView profile

Associate Professor

• Machine Learning
• Multimedia Systems
• Real-Time Processing
• +5 more

Håkon Kvale Stensland is an Associate Professor in the Department of Networks and Distributed Systems at the University of Oslo (UiO), affiliated with Simula Research Laboratory. His work focuses on multimedia systems, machine learning applications in medical imaging and sports analytics, distributed computing, and GPU optimization. He has contributed to projects like HyperKvasir (a gastrointestinal dataset) and SmartIO (PCIe networking for device sharing). Key research areas include real-time video processing, 3D convolutional neural networks for event detection in soccer, and energy-efficient multimedia workloads. He has co-authored over 50 publications in venues like ACM Multimedia, IEEE Transactions, and Nature Communications. His tools and datasets, such as Saga and Bagadus, emphasize collaborative machine learning and real-time sports analytics. Recent work (2024-2025) explores prompt generation for medical segmentation and multi-host device sharing in high-performance clusters. His research bridges theoretical computer science with practical applications in healthcare, sports, and distributed systems.

Wei-Shinn KuView profile

Professor

• Data management systems
• Data analytics
• Cybersecurity
• +3 more

Wei-Shinn Ku is a Professor in the Department of Computer Science and Software Engineering at Auburn University . He earned his Ph.D. in Computer Science from the University of Southern California in 2007, following M.S. degrees in Computer Science (2003) and Electrical Engineering (2006), and a B.S. in Information and Computer Education from National Taiwan Normal University (1999). As a program director at the National Science Foundation (2019–2022), he shaped research directions in data science and cybersecurity. Dr. Ku leads the Data Science and Engineering Laboratory (DSE Lab) at Auburn University, focusing on data management systems , data analytics , cybersecurity , and mobile computing . His recent work includes privacy-preserving techniques for IoT networks, spatio-temporal modeling for traffic prediction, and federated learning frameworks for mobile social networks. He has secured multiple NSF grants for projects like Indoor Spatial Query Evaluation and Data Integrity in Clouds . Dr. Ku's 15 most recent articles (2024–2025) span domains including traffic prediction , air quality modeling , secure cloud computing , and IoT security , with subfields like graph neural networks, quadtree-based optimization, and homomorphic encryption. His research has been recognized with awards such as the Auburn Alumni Engineering Council Research Award (2018, 2012) and the AFRL Summer Research Associate (2016). He has advised 18 current and graduated students in Ph.D. and M.S. programs, including Wenlu Wang, Ting Shen, and Kazuya Sakai. His professional service includes chair roles at IEEE/ACM conferences (ICDE, CIC, ICLR) and editorial positions at journals like IEEE Transactions on Knowledge and Data Engineering .

• Computer Vision
• Machine Learning
• Multimedia Signal Processing
• +2 more

Hossein Malekmohamadi is a Senior Lecturer in Computer Vision at De Montfort University, affiliated with the School of Computer Science and Informatics within the Faculty of Computing, Engineering and Media. His work bridges computer vision, machine learning, and energy systems. PhD, MSc, BSc in Computer Science or related fields Postgraduate Certificate in Higher Education (PGCLTHE) His research focuses on computer vision, machine learning, and multimedia signal processing, with applications in energy data analysis, Internet of Energy (IoE), and human-computer interaction via EEG signal processing. He has pioneered techniques like Gramian Angular Field (GAF) encoding for energy data visualization and edge computing solutions for real-time classification. Recent publications highlight his work on neural networks for aerodynamic modeling, EEG-based energy data analysis, and 3D feature compression methods. He holds a U.S. Patent for paper classification based on three-dimensional characteristics and is recognized as a Senior Fellow of the Higher Education Academy.

Jose Manuel Claver IborraView profile

Professor

• Parallel and Distributed Computing
• Cloud Computing
• Video Coding
• +7 more

Dr. José Manuel Claver Iborra is a Full Professor at the Department of Computer Science, School of Engineering (ETSE-UV), University of Valencia. He holds a PhD in Computer Science (Parallel and Distributed Computing program) from the Technical University of Valencia and an MSc in Physics (specialized in Electronics and Computer Science) from the University of Valencia. As an IEEE Senior Member, he focuses on cloud computing, video coding, parallel/distributed systems, reconfigurable computing, and network protocols for real-time applications. Current Research: Cloud-based video encoding, GPU acceleration for DNA analysis, FPGA-based network protocols, and indoor localization systems Academic Leadership: Coordinator of the UV-Tirant node in the Spanish Supercomputing Network (RES) His recent publications analyze GPU-based motion estimation, heterogeneous computing for video standards (H.264/AV1), and QoS scheduling algorithms. He supervises PhD and Master's theses on sensor networks, FPGA programming platforms, and parallel applications. Scientific Awards IEEE Senior Member He has directed funded projects on cloud infrastructure, distributed video processing, and reconfigurable systems since the 1990s.

• Robotics
• Mechatronics
• Biomedical Engineering
• +5 more

Ramin Almasi is an Assistant Professor at AGH University of Science and Technology in Kraków, Poland, affiliated with the Faculty of Mechanical Engineering and Robotics and the Department of Robotics and Mechatronics. His research bridges robotics, biomedical imaging, and signal processing. Research Focus Medical imaging (ultrasound elastography, OCT analysis) Machine learning for retinal diagnostics Signal processing and audio watermarking Publication Trends His recent work (2012–2025) emphasizes cross-disciplinary projects: biomedical applications (retinal imaging, tissue stiffness) and robust signal processing methods (audio watermarking). Keywords span machine learning, medical diagnostics, and real-time systems. Contact Email: ralmasi@agh.edu.pl

• Edge Computing
• Federated Learning
• Internet of Things
• +5 more

Sasu Tarkoma is a Professor at University of Helsinki specializing in next-generation computing systems with over two decades of research experience. His work bridges theoretical computer science with practical applications in smart cities, environmental monitoring, and industrial systems. His research interests focus on edge computing infrastructure , federated learning architectures , and AI-driven environmental monitoring systems . Tarkoma's work addresses critical challenges in distributed intelligence, particularly in resource-constrained environments where privacy, energy efficiency, and real-time processing are paramount. Recent work explores the integration of large language models with edge systems and novel approaches to 6G network architectures. The publication record reveals a strategic evolution from foundational mobile computing research to cutting-edge work at the intersection of AI, networking, and sustainability. His recent articles demonstrate particular strength in solving practical implementation challenges for federated learning in industrial settings and developing energy-efficient approaches to AI deployment in constrained environments. Tarkoma maintains an extensive collaborative network across European institutions, frequently partnering with researchers from Aalto University, University of Oulu, and international partners in Asia. His work shows increasing emphasis on environmental applications, particularly air quality monitoring systems using UAVs and mobile sensors.

• Perception Modeling
• Cognition Modeling
• Machine Learning
• +7 more

Sebastian Bosse serves as Head of the Interactive & Cognitive Systems group at Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, Germany, and as a Lecturer at the German University in Cairo. He holds significant leadership positions including Chair of the ITU focus group on Artificial Intelligence for Agriculture since 2021, membership on the board of the Video Quality Expert Group (VQEG), and the advisory board of the International AIQT Foundation. German University in Cairo - Lecturer Fraunhofer Heinrich Hertz Institute - Head of Interactive & Cognitive Systems group VISTA, York University - Affiliate member IEEE Transactions on Image Processing - Associate editor Dr. Bosse received his Dr.-Ing. degree in computer science with highest distinction from Technical University Berlin in 2018 after studying electrical engineering and information technology at RWTH Aachen University and Polytechnic University of Catalonia. His research focuses on modeling perception and cognition through machine learning and computer vision, with applications spanning multimedia, augmented reality, medicine, agriculture, and industrial production. His work demonstrates a strong interdisciplinary approach connecting theoretical computer science with practical implementations across diverse sectors. Dr. Bosse has established himself as a leader in AI applications for agriculture through his ITU focus group chairmanship and has contributed to video quality standards through his VQEG board membership. His research trajectory shows consistent progression from foundational work in image and video compression to advanced cognitive systems. Board member, Video Quality Expert Group (VQEG) Advisory board, International AIQT Foundation Affiliate member, VISTA, York University, Toronto Associate editor, IEEE Transactions on Image Processing Chair, ITU focus group on Artificial Intelligence for Agriculture (since 2021) Dr. Bosse founded the Interactive & Cognitive Systems research group at Fraunhofer HHI in 2020 after ten years as a research engineer in the institute's Image & Video Compression and Machine Learning groups. His laboratory focuses on developing systems that bridge human cognitive processes with machine intelligence across multiple application domains.

Erhan A. İnceView profile

Professor

• Mobile Communications
• Channel Coding
• OFDM/OFDMA Techniques
• +4 more

Prof. Dr. Erhan A. İnce is a Professor at the Department of Electrical & Electronic Engineering, Eastern Mediterranean University (EMU). He holds a Ph.D. in Communications from Bradford University (1997) and has been at EMU since 1998, progressing through academic ranks from Assistant Professor (1998–2006) to Full Professor (2016–present). His research focuses on mobile communications, channel coding, OFDM techniques, and video/signal processing. He has supervised over 25 master's and doctoral students, including notable works on turbo codes, interference alignment, and image processing. Education: Bachelor's in Electrical Engineering, Bucknell University (1990) M.S. in Electrical Engineering, Bucknell University (1992) Ph.D. in Communications, Bradford University (1997) Research Interests: His work spans communications theory, signal processing, and multimedia applications. Key areas include turbo codes over fading channels, OFDM-based systems, facial recognition, and traffic surveillance using video analytics. Publications: Over 30 journal and conference papers in IEEE Transactions, Elsevier journals, and international conferences like CSDSP and EUSIPCO. Recent topics include blind interference alignment in cellular networks and tensor-based signal processing. Awards: Best Poster Award at CSDSP 1998. Service: Served as a reviewer for IEEE journals and conferences, and held administrative roles in EMU’s Faculty of Engineering, including Professor Representative (2018–present).

• Signal Processing
• Digital Communication
• Information Theory
• +8 more

Gregory W. Wornell is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE). He leads the Signals, Information, and Algorithms Laboratory and co-directs the MIT Center for Wireless Networking. He chairs the EECS Graduate Area I (Systems, Communication, Control, and Signal Processing) and is part of MIT's Computational and Systems Biology Initiative. Education: B.A.Sc. (Honors), Electrical Engineering & Computer Science, University of British Columbia (1985) S.M. (1987) and Ph.D. (1991), Electrical Engineering & Computer Science, MIT Research Interests: Focuses on signal processing, digital communication, and information theory, with applications to wireless networks, sensor systems, multimedia technologies, and quantum communication. His work emphasizes algorithms for interference cancellation, multimedia authentication, and source coding. Awards & Recognition: IEEE Fellow HP-MIT Alliance Award (2001) Promoted to Full Professor at MIT (2002) Grants & Leadership: Leads major research initiatives, including work funded by the HP-MIT Alliance. Oversees graduate programs and industrial collaborations. Labs & Teams: Directs the Signals, Information, and Algorithms Laboratory within RLE, fostering interdisciplinary projects in wireless networking and quantum communication.

Roger ZimmermannView profile

Professor

• Multimedia and Distributed Systems
• Multimedia and Mobile Networking
• Spatio-temporal Data Management
• +8 more

Roger Zimmermann is a Full Professor at the School of Computing, National University of Singapore (NUS), where he is also a Co-PI at the Grab-NUS AI Lab and leads the Location AI project. He previously served as Deputy Director of the NUS Smart Systems Institute (SSI) and Co-Director of the Centre of Social Media Innovations for Communities (COSMIC), both funded by Singapore’s National Research Foundation (NRF). Before joining NUS, he was a Research Area Director and Research Assistant Professor at the University of Southern California (USC). Ph.D. in Computer Science, University of Southern California (1998) M.S. in Computer Science, University of Southern California (1994) His research focuses on multimedia systems , spatio-temporal data management , streaming media architectures (especially DASH), machine learning applications , AR/VR , and location-based services . He leads the Media Management Research Lab (MMRL) at NUS, which conducts cutting-edge work in distributed multimedia and intelligent systems. His work combines theoretical depth with real-world applications in urban computing, smart mobility, and immersive media. The recent publications reflect a strong trend toward multimodal learning , spatio-temporal AI , adaptive streaming , and urban intelligence . His team explores zero-shot learning, 3D scene understanding, traffic forecasting, and open-vocabulary audio-visual segmentation, often leveraging foundational models and deep neural architectures. There is a clear emphasis on real-time, scalable systems for smart cities and immersive experiences. Dr. Zimmermann has received numerous accolades, including: DASH-IF Excellence in DASH Award (multiple years) Best Paper Awards at ACM SIGSPATIAL, IEEE ICME, and ACM MMSys Silver Award at ACM MMSys 2020 Grand Challenge IEEE Communications Society Best Editor Award (2017) ACM Distinguished Member (2017) Top 1% Publons Reviewer in Computer Science (2018) He has advised numerous students and led major research initiatives funded by MOE, NRF, A*STAR, NSF, and industry partners like Seagate, Intel, and HP. He has served as General Chair for IEEE MIPR 2023, ACM Multimedia 2020, and IEEE ISM 2015, and as TPC Co-Chair for several top-tier conferences. His editorial roles include Associate Editor for IEEE Transactions on Multimedia (TMM), ACM TOMM, and IEEE OJ-COMS. He leads the Media Management Research Lab (MMRL) , which focuses on intelligent multimedia systems, spatiotemporal data mining, and immersive media technologies. The lab develops scalable solutions for real-world challenges in urban computing, smart transportation, and interactive media.