Bio

I am a third-year PhD student at The University of York, supervised by Prof. William Smith. My research focuses on computer vision-based scene understanding, emphasising the integration of non-visual features and semantic or temporal cues. I aim to minimise reliance on task-specific data, reducing the need for extensive dataset curation, to enhance the flexibility and adaptability of computer vision systems for diverse applications. I am particularly interested in test-time training, large foundation models, and diffusion-based techniques, primarily for addressing segmentation and tracking tasks in video data.

Timeline

1. 

   University of York
   PhD Student
   2021 -

2. 

   Spectral Compute
   GPU Software Engineer
   2023 -

3. 

   Owl & Lark
   Lead AI Engineer
   2024 -

4. 

   Satis AI
   Lead Computer Vision Engineer
   2022 - 2023

5. 

   Visio Impulse
   Computer Vision Scientist
   2020 - 2021

6. 

   HayBeeSee
   Robotics Engineer
   2018 - 2020

Thesis

Beyond Visual Features
PhD thesis, University of York, in preparation. People infer the unseen constantly; vision systems work frame by frame on visible pixels and lose the thread the moment the evidence goes away. The thesis treats cross-domain retrieval, amodal video segmentation and out-of-view point tracking as a single problem at three distances: evidence abstracted, then occluded, then absent. It argues the barrier is as much one of supervision as of architecture. The project page puts all three to you at once, on nine seconds of broadcast hockey.

Education

1. 

   University of York
   PhD in Computer Vision and Machine Learning
   2021 -

2. 

   University of York
   MSc Intelligent Robotics
   Distinction
   2017 - 2018

3. 

   Anglia Ruskin University, Cambridge
   BSc (Hons), Audio and Music Technology
   1st Class + Richer Sounds Award
   2014 - 2017