About

I am a Postdoctoral researcher in Deep Learning and Computer Vision at the University of Oxford, working with Prof Ben Sheldon.

My research advances deep learning by integrating principles of visual perception, such as texture, shape, and frequency analysis, into novel network architectures. I design and validate new convolutional and hybrid CNN–Transformer models that enhance feature representation and robustness in complex, densely populated scenes.

I also investigate multimodal deep learning and data fusion, developing models informed by the physical properties of remotely sensed data to improve generalisation and interpretability across environmental domains. By advancing deep learning architectures, my work drives methodological innovation validated through real-world remote sensing challenges.

Current research themes:

Semantic segmentation architectures featuring custom convolutional layers for complex visual domains.
Hybrid CNN–Transformer architectures optimising the synergy between local and global feature representations.
Multimodal and multi-spectral data fusion for environmental and industrial monitoring.
UAV-based segmentation models resilient to occlusions, shadows, and light variation.
Domain-invariant feature learning for improved cross-sensor and cross-season performance.

To support deep learning model development, I curate high-quality datasets for computer vision and remote sensing applications.

Background:

Prior to this, I completed an EPSRC-funded PhD at the University of Sussex under Prof. Andy Philippides and Prof. Novi Quadrianto. My research focused on salient feature representation in domain adaptation and semantic segmentation, leading to the development of new models for remote sensing applications.

As part of the Predictive Analytics Lab (PAL), I contributed to a British Academy-funded project on Satellite/Aerial Image Scene Segmentation. I designed novel deep learning models for land-use classification from imagery, integrating multiple data types into a web-based mapping and visualisation tool for peri-urban agriculture in Ghaziabad, India. Please see the demo below. demo During my research internship at Satellite Applications Catapult (supervised by Dr Cristian Rossi), I applied physics-aware AI and remote sensing data to detect and classify cement plants in China by leveraging physical properties such as ambient temperature and soil moisture.

Industrial Experience:

Prior completing my PhD, I worked as a Control and Automation Design Engineer in the oil and gas industry, gaining experience across the full software and hardware development lifecycle — including systems sensing and control, communication protocols, functional safety, and project planning.

Open to Collaboration:

I am always eager to explore new collaborations — Feel free to get in touch if you would like to connect!

Dr Georgios Voulgaris

Recent News