SHORT BIO
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Christos Kyrkou is a Research Lecturer at the KIOS Research & Innovation Center of Excellence at the University of Cyprus. Dr. Kyrkou received his Ph.D. degree in Computer Engineering from the University of Cyprus in 2014. He graduated top of his class and received his B.Sc in Computer Engineering in 2008 from the University of Cyprus . After been awarded a full scholarship he received his M.Sc. in Computer Engineering from the University of Cyprus in 2010. He is an author/co-author of more than 50 scientific publications in international peer reviewed conferences and journals, including 2 best paper awards. In 2020 he worked as special scientist for teaching at the Department of Electrical and Computer Engineering at UCY teaching the course ECE627 Computer Vision. He has worked in several National and European funded research and innovation projects. His research interests lie in the areas of Computer Vision and Deep Learning with emphasis on real-time performance. In particular efficient neural network architectures and techniques, algorithms for visual object recognition and detection, with applications in emergency management, autonomous vehicles, and drones. He serves as a reviewer for major conferences in the areas of computer vision and machine learning such as CVPR, NeurIPS, and ICML.
Edge Vision and AI
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"Through my research I aspire to design efficient & robust Computer Vision and Deep Learning algorithms that enable holistic real-time perception and decision making leading to more intelligent systems"
EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring
YOLOpeds: Efficient real-time single-shot pedestrian detection for smart camera applications
C^3 Net: End-to-end deep learning for active smart camera control
AirCamRTM: Enhancing Vehicle Detection for Efficient Aerial Camera-based Road Traffic Monitoring
RECENT ARTICLES
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EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring
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YOLOpeds: Efficient real-time single-shot pedestrian detection for smart camera applications
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IET CV 2020
DeepCamera: Following Targets Without Bounding Boxes… End-to-end active vision
“We propose a supervised learning technique to control active cameras with Deep Convolutional Neural Networks to go directly from visual information to camera movement..”
UPCOMING EVENTS
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CONNECT WITH ME ON SOCIAL MEDIA & PLATFORMS
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