Alexandru Buburuzan

Alexandru Buburuzan

DPhil student, Oxford

✉ alexbubu@robots.ox.ac.uk

Bio

I am Alex, a Research Scientist Intern at FiveAI in autonomous driving, advised by Dr. Puneet Dokania. I am joining University of Oxford as a fully-funded DPhil (PhD) student in Autonomous Intelligent Machines and Systems.

I recently graduated with a BSc in AI from The University of Manchester. I completed my dissertation supervised by Prof. Tim Cootes on counterfactual generation using diffusion inpainting models (see MObI and AnydoorMed).

I began my computer vision journey at 16, in a medical imaging startup. I later continued there as a Research Engineer before joining FiveAI for a year-long research internship, publishing work on multimodal sensor fusion and synthetic data generation for autonomous driving advised by Dr. Romain Mueller.

Interests
  • Computer Vision
  • Multimodal Perception
  • Autonomous Driving
Education
  • DPhil (PhD) Autonomous Intelligent Machines and Systems, 2025 - 2029

    University of Oxford

  • BSc (Hons) Artificial Intelligence with Industrial Experience, 2021 - 2025

    The University of Manchester

Publications

Evidence of a cognitive bias in the quantification of COVID-19 with CT: an artificial intelligence randomised clinical trial

Nature Scientific Reports | Radiologists overestimate COVID-19 lung involvement on CT due to a psychophysical bias, yet AI support …

Evidence of a cognitive bias in the quantification of COVID-19 with CT: an artificial intelligence randomised clinical trial

Experience

 
 
 
 
 
Five AI
Research Scientist Intern
May 2025 – Present Cambridge, UK
 
 
 
 
 
Five AI
Research Engineer Intern
Jun 2023 – Jun 2024 Cambridge, UK
Diffusion models for scene editing and multimodal sensor fusion for 3D object detection.
 
 
 
 
 
Rayscape
Research Engineer
Jul 2021 – Jun 2023 Remote
Developed a CE-marked algorithm for lung nodule segmentation, deployed in over 100 hospitals.
 
 
 
 
 
Rayscape
Machine Learning Intern
Mar 2020 – Sep 2020 Timisoara, Romania
Build a time-efficient AI model for the detection of intracranial haemorrhages meant for speeding up the triaging process.