SHORT BIO

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Dr. Christos Kyrkou is a Research Lecturer at the KIOS Research & Innovation Center of Excellence at the University of Cyprus. He earned his Ph.D. in Computer Engineering from the University of Cyprus in 2014, having graduated top of his class with a B.Sc. in Computer Engineering in 2008 and completing an M.Sc. in Computer Engineering in 2010 with a full scholarship, all from the University of Cyprus.

With a research focus at the intersection of Computing, Machine Learning, and Vision, Dr. Kyrkou has made significant contributions in the areas of computation/data-efficient deep learning design/search and perception learning for vision systems, specifically in recognition, detection, segmentation, and control. His work has resulted in over 60 scientific publications, including two best paper awards.

Dr. Kyrkou's expertise extends to actively participating in the conception, design, and implementation of numerous National and European funded research and innovation projects. His high-level research activities encompass applications in emergency management, traffic monitoring, autonomous vehicles, and surveillance. He is also a reviewer for renowned conferences and periodicals in the fields of computer vision and machine learning, including TPAMI, CVPR, NeurIPS, and ICML.

Edge Vision and AI 

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"My research is dedicated to advancing the scientific frontiers of machine learning, by finding efficient and robust representations in computer vision and deep learning to provide holistic real-time perception and decision-making in diverse applications."

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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UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire.   📰 📘 🎥 📦 💻 🎮Live Demo  
IEEE JSTARS 2020

YOLOpeds: Efficient real-time single-shot pedestrian detection for smart camera applications

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This work addresses the challenge of achieving a good trade-off between accuracy and speed for efficient deep-learning-based pedestrian detection in 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..” 

READ POST ON MEDIUM 𝚳 📰 

UPCOMING EVENTS

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CONNECT WITH ME ON SOCIAL MEDIA & PLATFORMS

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