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Mariusz JakubowskiView profile

Assistant Professor

• Digital signal processing
• Embedded systems
• Wireless technologies

Dr. Mariusz Jakubowski is an Assistant Professor at Vistula University's Faculty of Computer Science, Graphics and Architecture. He holds a Ph.D. in Computer Science (with distinction) from the Faculty of Electronics and Information Technology, Warsaw University of Technology (2012), focusing on hardware-based motion estimation algorithms for video data compression. His research interests include digital signal processing, embedded systems, and wireless technologies. He has authored multiple articles in international conferences and Philadelphia-listed journals. Education: Ph.D. in Computer Science, Warsaw University of Technology (2012) Professional Experience: 2002–2008: Researcher at Industrial Telecommunications Institute (PIT-RADWAR), designing microprocessor systems for military radar control/diagnostics. 2016–Present: Employed at the Integrated Circuits and Electronic Systems Design Department, Institute of Microelectronics and Photonics. 2013–Present: Assistant Professor at Vistula University, teaching Electronics for IT Specialists, Digital Technology, Computer System Architecture, and Embedded Systems. Awards: Recipient of the First Degree Team Award from Warsaw University of Technology's Rector (2012–2013). Projects: Involved in EU-cofunded projects at Vistula University. His work bridges hardware design, signal processing, and embedded systems applications in both academic and defense sectors.

Heiko SchwarzView profile

Professor

• Video Coding
• Image Compression
• Multimedia Systems
• +3 more

Prof. Heiko Schwarz is a Professor at Freie Universität Berlin and Head of the Image & Video Coding Group at the Fraunhofer Heinrich Hertz Institute (HHI). He holds a Dipl.-Ing. (1996) and Dr.-Ing. (2000) in Electrical Engineering from the University of Rostock. Research & Expertise: Specializes in video coding standards including HEVC (H.265) and H.264/MPEG-4 AVC. Key contributions include scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimized encoding. His work has been integral to international standards through participation in ITU-T VCEG and ISO/IEC MPEG. Developed core tools for HEVC and its 3D video extensions Co-designed H.264/AVC's motion compensation and entropy coding Co-editor of H.264/AVC and SVC reference software Awards & Recognition: 2014 SMPTE Certificate of Merit 2012 German Future Prize Nomination 2011 Karl Heinz Beckurts Award 2009 IEEE Best Paper Award Academic Engagement: Associate Editor of IEEE Transactions on Circuits and Systems for Video Technology (2016–present). Teaches Data Compression at FU Berlin. Leads a research group focusing on next-generation video coding technologies. Labs/Teams: Directs the Image & Video Coding Group at Fraunhofer HHI, advancing standards like HEVC and exploring wavelet-based video coding techniques.

• Photonics
• 3D Imaging
• Lidar Technology
• +3 more

Sandor Plosz is a Researcher at Heriot-Watt University, affiliated with the School of Engineering & Physical Sciences and the Institute of Sensors, Signals & Systems. His work focuses on advanced lidar technology, 3D imaging, and real-time data reconstruction using single-photon sensors. He holds a position in the Institute of Sensors, Signals & Systems, specializing in photonics and signal processing applications. Research interests include developing algorithms for 3D reconstruction, optimizing lidar systems, and leveraging graphics processing units (GPUs) for efficient data handling. His recent studies address challenges in reconstructing 3D videos under environmental obscurants, emphasizing robustness and real-time performance. Publications highlight contributions to multiscale 3D reconstruction methodologies and real-time processing techniques. No scientific awards are explicitly mentioned, and no advising/grant information is provided. His primary affiliation is through the Institute of Sensors, Signals & Systems, contributing to interdisciplinary research in computational imaging.

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.

• Health Disparities
• Cancer Care
• Health Equity
• +3 more

Do Lee is a Researcher at the COPPER Center within the Yale School of Medicine at Yale University. She holds a B.S. in Elementary Education from the University of Maryland, College Park, and an MPH in Biostatistics from George Washington University. Her research focuses on addressing racial and socio-economic disparities in cancer care to advance equitable healthcare. She contributes to interdisciplinary efforts in health equity, biostatistics, and public health, leveraging her expertise to improve patient outcomes through data-driven strategies. Affiliated with both the COPPER Center and the Department of Internal Medicine, her work integrates statistical methodologies with clinical and translational research. While no awards are explicitly noted, her contributions to health disparities research reflect a commitment to impactful translational science. Her scholarly publications span neuromorphic computing, artificial synapse electronics, and efficient machine learning techniques, demonstrating a blend of computational innovation and applied health research. Collaborations likely bridge engineering and medical domains to address complex healthcare challenges.

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.

• Structural Engineering
• Historical Constructions
• Masonry Mechanics
• +4 more

Marialuigia Sangirardi is a Postdoctoral Research Associate at the University of Oxford's Department of Engineering Science, affiliated with Mansfield College. Prior to Oxford, she worked at UMinho - ISISE (Portugal), La Sapienza, and Roma Tre (Italy). Her research focuses on masonry constitutive modeling , historic buildings , and soil-structure interaction , with emphasis on low-impact retrofitting strategies and computer vision-based structural monitoring . Current projects include MINT (in-situ testing methods for historic masonry) and collaborations with Professor Sinan Acikgoz. Publications highlight her expertise in lime mortar elasticity , seismic retrofitting , and numerical simulation of historical structures. She serves as a reviewer for international journals in structural preservation and mechanics.

Aggelos K. KatsaggelosView profile

Professor

• Signal Processing
• Communications
• Computer Vision
• +3 more

Aggelos K. Katsaggelos is a Professor at Northwestern University's McCormick School of Engineering and Applied Science, affiliated with the Department of Electrical and Computer Engineering. He holds a Diploma in Electrical and Mechanical Engineering from the Aristotelian University of Thessaloniki (1979) and M.S./Ph.D. degrees from Georgia Tech (1981/1985). His research focuses on Signal Processing, Communications, Computer Vision, and Life Sciences, with emphasis on image/video recovery, Bayesian frameworks, and medical imaging applications. He leads the Image and Video Processing Laboratory, addressing challenges like compression artifact removal, light field estimation, and EEG-fMRI data fusion. Awards: IEEE Third Millennium Medal, IEEE Signal Processing Society Meritorious Service Award, multiple best paper awards. Service: Former Editor-in-Chief of IEEE Signal Processing Magazine; Board of Governors member, IEEE Signal Processing Society. Innovations: Co-inventor of 16 patents, with work spanning neural interface technologies and computational imaging.

Paul L BendichView profile

Adjunct Professor

• Topological Data Analysis
• Machine Learning
• Computational Topology
• +4 more

Paul L Bendich is an Adjunct Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from Duke University (2008). His research focuses on adapting topological and geometric methods for data analysis, particularly in topological data analysis (TDA). He has pioneered TDA methodologies for applications in machine learning, sensor fusion, and environmental modeling. Current appointments include leading research initiatives in multi-modal data analysis and reinforcement learning optimization. Key areas of expertise include computational topology, persistent homology, and topological signal processing. He teaches courses on topological data analysis (COMPSCI 434, MATH 412) and has developed educational programs like Data+ at Duke. Grants include NSF-funded projects (BIGDATA: F: DKA: CSD) and Air Force Office of Scientific Research initiatives. Recent work emphasizes topological methods in AI safety (topological parallax), reinforcement learning efficiency, and geophysical feature tracking. Professional activities include conference presentations on TDA applications and editorial work for journals. His research bridges theoretical mathematics with practical data-driven challenges in science and engineering.

• 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.

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

Eugene Belilovsky is an Associate Professor at the University of Montreal's Department of Computer Science and Operational Research and an Assistant Professor at Concordia University's Department of Computer Science and Software Engineering. He is also an Associate Member of Mila – Quebec Institute for Artificial Intelligence. His research focuses on computer vision, deep learning, and their applications in areas like continual learning and few-shot learning at the intersection of vision and natural language processing. Belilovsky's expertise includes distributed systems, federated learning, and optimization. His work addresses challenges in model generalization, spurious correlations, and efficient training strategies. He has advised numerous graduate students, including Charles-Étienne Joseph, Medric B. Djeafea Sonwa, Gwendolyne Legate, and Irene Tenison. His recent publications highlight contributions to federated learning, continual pre-training, and fairness in AI systems. Notable work includes optimizing distributed learning protocols and mitigating forgetting in evolving data streams. Belilovsky's research also explores clinical applications, such as diagnosing hepatic steatosis using ultrasound imaging through deep learning techniques. He collaborates with institutions like Mila and the DIRO department, advancing interdisciplinary projects in AI-driven healthcare, robotics, and language modeling.

• Computer Architecture
• High-Performance Computing (HPC)
• Cloud Computing
• +4 more

Prof. Weikuan Yu is a Professor in the Department of Computer Science at Florida State University. His research focuses on computer architecture, high-performance computing (HPC), cloud computing, parallel file systems, and deep learning applications. He holds the role of Chair and can be contacted via yuw@cs.fsu.edu or (850) 644-5442. His expertise includes optimizing storage systems and I/O behaviors in scientific workflows, developing scalable distributed systems, and applying machine learning to improve computational efficiency. Notable projects include work on burst buffer systems (e.g., BurstFS, TRIO), persistent memory management (PHAST), and distributed deep learning frameworks (e.g., compression techniques for time-evolutionary data). Recent research trends emphasize enhancing HPC storage efficiency through novel file systems and I/O emulation, as well as leveraging machine learning for fault tolerance and configuration tuning. His work bridges hardware-software co-design to address challenges in exascale computing and big data analytics. Prof. Yu has contributed to multiple open-source projects and frameworks, including OpenSHMEM-based key-value stores and MapReduce optimizations. His publications highlight advancements in parallel processing, distributed algorithms, and energy-efficient memory architectures.

• Biomedical Image Analysis
• Medical Imaging
• Computer-Aided Diagnosis
• +4 more

Marios Pattichis is a Professor of Electrical and Computer Engineering at the University of New Mexico (UNM), serving as Director of Online Programs since 1999. He leads the Image and Video Processing and Communications Lab (ivPCL) and co-founded Cosmiac. His work focuses on biomedical image analysis, video analytics for clinical decision support, and educational technology initiatives like the AOLME project targeting underrepresented middle-school students. Pattichis holds 10 patents, over 80 journal papers, and $15.9M in research funding from NSF, NIH, and AFRL. Education: Ph.D. and M.S. in Computer/Electrical Engineering from UT Austin (1998, 1993), with dual B.A./B.Sc. in Mathematics and Computer Sciences (1991). Research Interests: Biomedical CAD systems, explainable AI in imaging, dynamically reconfigurable architectures, and bilingual STEM education. His lab develops tools for stroke risk assessment via carotid ultrasound, solar coronal hole segmentation, and classroom activity analysis to measure student participation. Awards: 2022 EAMBES Fellow for contributions to biomedical image analysis. Editorships include IEEE Transactions on Image Processing and IEEE Signal Processing Letters. Grants & Teams: Directed over $15.9M in grants, advised numerous students (though none listed explicitly), and collaborates with educators on AOLME's integrated math/CS curricula. Active in codec comparisons for healthcare video streaming (VVC vs. AV1) and FPGA-based image processing acceleration. Labs/Initiatives: ivPCL (medical imaging/video systems); DRASTIC (adaptive video processing); ESTRELLA (bilingual STEM outreach). Involved in IEEE journal special issues on video analytics and multilingual education.

• Computational Imaging
• Compressed Sensing
• Signal Processing
• +5 more

Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.

• 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.