Harris Papadopoulos

About

Harris Papadopoulos is a leading researcher at the intersection of artificial intelligence and environmental sustainability, with a primary focus on deep learning for real-time object detection. His most impactful work centers on the automated sorting of Construction and Demolition Waste (CDW), a critical challenge for the circular economy. In his highly cited 2023 study (95 citations), Papadopoulos conducted a rigorous benchmark of state-of-the-art deep learning models, comparing single-stage and two-stage detectors for their speed and accuracy in identifying waste materials. This work is not merely academic; it provides the foundational computer vision system for a successful waste-sorting robot, directly translating algorithmic performance into a tangible environmental solution. By solving the real-time localization and classification problem for CDW, Papadopoulos has bridged the gap between advanced AI architectures and practical, industrial-scale recycling. His contributions are essential reading for researchers in applied deep learning, computer vision, and sustainable engineering, demonstrating how cutting-edge AI can directly address pressing global waste management challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-time construction demolition waste detection using state-of-the-art deep learning methods; single–stage vs two-stage detectors

   95 citations · 2023
2. 2
   Artificial Intelligence Applications and Innovations

   19 citations · 2013
3. 3
   Real-Time Construction Demolition Waste Detection Using State-of-The-Art Deep Learning Methods; Single – Stage vs Two-Stage Detectors

   4 citations · 2023

Key Collaborators

Contact & Links