جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Deep Learning
• Continual Learning
• +۴ مورد دیگر

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
• +۴ مورد دیگر

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
• +۴ مورد دیگر

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
• +۵ مورد دیگر

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
• +۴ مورد دیگر

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.

• Image Processing
• Video Compression
• Visual Content Security
• +۲ مورد دیگر

Benoît Macq is a Full Professor at UCLouvain, affiliated with the Louvain Polytechnic School (EPL) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), specifically within the Electrical Engineering Department (ELEN). His academic base is at the Maxwell building in Louvain-la-Neuve, Belgium. His educational background includes a degree as a Civil Engineer in Electrical Engineering and a Doctorate in Applied Sciences. Professor Macq's research focuses on image processing for visual communications , spanning from algorithmic research to industrial applications. His primary areas of expertise are: Video Compression Visual Content Security Medical Imaging Multimodal Interactions He has been recognized for his contributions by being elected as a Full Member of the Royal Academy of Belgium (Class of Technology and Society) on May 29, 2009, and will serve as the Director of his Class for 2025 and 2026. Within the university, he is part of the ICTEAM research institute and the ELEN department, contributing to the Electrical Engineering Center's activities.

Håkon Kvale Stenslandمشاهده پروفایل

دانشیار

• Machine Learning
• Multimedia Systems
• Real-Time Processing
• +۵ مورد دیگر

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.

Yang Huaمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Visual Recognition
• +۳ مورد دیگر

Dr. Yang Hua is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on computer vision, machine learning, and their practical applications, including visual recognition, robust learning with limited data, and hardware-aware systems. He has supervised multiple PhD students in areas like generative adversarial networks and domain adaptation. Affiliations: Institute of Electronics, Communications & Information Technology; Co-Investigator in multiple research projects (e.g., R5159ECS, R2733CPH). Teaching: Courses include 'How to do Research' and 'Database and Programming Fundamentals' for MSc and PhD students. Research Interests: Dr. Hua's work addresses challenges in visual recognition, domain adaptation, and federated learning. He emphasizes real-world applications and has contributed to competitions like PASCAL VOC Challenge and VOT-TIR2015. Awards: Recognized with Queen's University STAR award (2019), VOT-TIR2015 tracker win, and multiple PASCAL VOC Challenge wins. His research has been funded by Microsoft Research and European conferences. Grants & Projects: Includes 'Artificial Intelligence for Vision-Based Navigation' and collaborations on medical imaging (e.g., AMD diagnosis). He leads projects on federated learning and domain adaptation. Labs/Teams: Active in the Institute of Electronics, Communications & Information Technology, contributing to distributed AI and biomedical applications.

Wei-Shinn Kuمشاهده پروفایل

استاد

• Data management systems
• Data analytics
• Cybersecurity
• +۳ مورد دیگر

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 .

Jose Manuel Claver Iborraمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• Cloud Computing
• Video Coding
• +۷ مورد دیگر

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.

Amanda Fernandezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Amanda Fernandez is an Assistant Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), where she contributes significantly to research, teaching, and interdisciplinary collaboration. She will be a faculty member in the newly launching College of AI, Cyber and Computing in Fall 2025, reflecting her central role in advancing UTSA's strategic focus on emerging technologies. Her research lies at the intersection of artificial intelligence, deep learning, computer vision, and computer science education . She directs the UTSA Vision and AI Lab (VAIL) and serves as Machine Learning & Deployment Thrust Lead for MATRIX: The AI Consortium for Human Well-Being , and as a Principal Investigator in the Consortium on Nuclear Security Technologies (CONNECT) . Her work emphasizes explainable and robust AI, with applications in nuclear materials, VR/AR, and biomedical signals like ECG. The 15 most recent publications reflect a strong trend in explainable AI, adversarial robustness, and AI-enhanced education . Her work spans technical AI research (e.g., ICML, CVPR workshops) and educational innovation (e.g., SIGCSE, RESPECT), demonstrating a dual commitment to advancing both the science and pedagogy of computing. Themes include model attribution, training optimization, semantic segmentation, and the integration of LLMs in introductory CS courses. Senior Member of the IEEE Senior Member of the National Academy of Inventors (NAI) Funded by the National Science Foundation (NSF) Supported by the U.S. Department of Energy (DOE) Supported by the U.S. Department of Defense (DOD) Fernandez is deeply committed to mentoring undergraduate researchers through her CS-CURE course, which provides hands-on research experience. She advises student organizations including ACM-W and GDSC , and leads initiatives such as CS4SA to increase Latinx participation in computer science. Her grants support both research and educational outreach, emphasizing inclusivity and workforce development. She leads the Vision and AI Lab (VAIL) at UTSA and is an integral member of two major research consortia: MATRIX and CONNECT , fostering collaboration across institutions including Los Alamos National Lab, Argonne National Lab, and UNLV.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +۶ مورد دیگر

Prof. Dr. Didier Stricker is a distinguished Professor of Computer Science at Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU) and serves as Scientific Director and Head of the Augmented Reality Research Department at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Augmented Vision Group, which comprises approximately 30 researchers working across various domains of computer vision and augmented reality. His work bridges academic research with industrial applications through collaborations with major companies including Sony, Google, and John Deere. His educational background includes electrical engineering studies at the Polytechnic Institute of Grenoble and the Technical University of Karlsruhe. He earned his doctorate from the Technical University of Darmstadt in 2002 with a dissertation on "Computer Vision-Based Calibration and Tracking Methods for Augmented Reality Applications." Prof. Stricker's research spans virtual and augmented reality, computer vision, human-computer interaction, cognitive interfaces, and on-body sensor networks. His work focuses on developing practical applications that enhance human capabilities through advanced visual computing technologies. He has pioneered approaches in video and sensor analytics, particularly in creating cognitive interfaces that respond intelligently to user needs and environmental contexts. His recent publications reveal a strong emphasis on 3D scene understanding, real-time processing for augmented reality applications, and the integration of large language models with spatial reasoning capabilities. There's a clear trend toward more sophisticated multimodal approaches that combine vision, language, and spatial understanding to create more natural and intuitive human-computer interactions. Among his notable achievements: Innovation Prize of the German Society of Computer Science (2006) Organized the first IEEE & ACM International Symposium on Mixed and Augmented Reality (ISMAR) in 2002 Member of the ISMAR steering committee from 2000-2007 Multiple best paper and demonstration awards at major conferences Several registered patents in tracking and augmented reality technologies Prof. Stricker has supervised numerous PhD and Master's students through his leadership of the Augmented Vision Group. His research is supported by significant funding from both European and national research organizations, as well as through industrial partnerships. He serves as an expert reviewer for various research funding bodies and contributes to the academic community through editorial roles for journals and conferences in VR/AR and computer vision. The Augmented Vision Group under his direction maintains strong connections with industry partners and participates in numerous collaborative research projects including LUMINOUS, SHARESPACE, I-Nergy, BIONIC, and VIDETE. These projects span applications in language-augmented XR systems, social experiences in hybrid spaces, AI for energy systems, personalized body sensor networks, and 4D scene analysis.

Jiří Žáraمشاهده پروفایل

استاد

• Virtual Reality
• Computer Graphics
• Computational Geometry
• +۳ مورد دیگر

Jiří Žára is a Professor at the Czech Technical University in Prague, currently serving as Head of the Department of Computer Graphics and Interaction within the Faculty of Electrical Engineering. With a career spanning over four decades at the institution, he has held roles from PhD student (1982-1986) through Assistant Professor (1986-1997), Associate Professor (1997-2007), to Full Professor status since 2007. Department Head: 2008-present Computer Graphics Group Leader: 2002-2008 Supervisor: Computer Graphics Laboratory His research focuses on Virtual Reality , 3D Modeling , and Computational Geometry , with particular emphasis on VR educational applications, historical city visualization, and geometric algorithms. He has contributed significantly to VRML standards and interactive visualization techniques. Key research trends evident in his 96+ publications include real-time deformation techniques for 3D models, adaptive caching systems in rendering, and automated colorization algorithms for historical cartoons. His work demonstrates strong interdisciplinary connections between computer graphics , cultural heritage preservation , and educational technology . As academic supervisor, he has guided numerous students including: Ondřej Pluhař (Interactive Tokamak Golem Model, 2011) Daniel Šrám (Virtual Gallery in St. Peter & Paul Church, 2010) Jan Zimandl (Castle Park VRML Tour, 2009) He leads multiple EU-funded projects including: ARiSE (Augmented Reality in School Environments) VHCE (Virtual Heart of Central Europe for cultural heritage) V3C (Visual Computing Competence Center) RCI (Research Center for Informatics)

• Perception Modeling
• Cognition Modeling
• Machine Learning
• +۷ مورد دیگر

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 İNCEمشاهده پروفایل

استاد

• Mobile Communications
• Channel Coding
• Multi-carrier Techniques (OFDM, OFDMA)
• +۷ مورد دیگر

Prof. Erhan İnce is a faculty member in the Department of Electrical and Electronic Engineering at Eastern Mediterranean University (EMU), where he has served since 1998. He holds a PhD in Communications from the University of Bradford (1997) and MS/BS degrees in Electrical and Electronic Engineering from the University of Bucknell (1992, 1990). His research focuses on mobile communications, channel coding, OFDM/OFDMA, statistical signal processing, and facial recognition. He has supervised numerous PhD and MS students, including Ramin Bakhshi, Syed Amjad Ali, and Waseem Qassab Bash. Key projects include 'Monitoring KKTCELL's Base Stations' (2018-2019) and 'Background Subtraction and Lane Occupancy Analysis' (2011). He received an award at the 1998 Symposium on Communication Systems & Digital Signal Processing. His work spans peer-reviewed journals (IEEE Access, Digital Signal Processing) and books on video surveillance. He actively contributes to IEEE and other academic communities.