Alexandru Buburuzan

Alexandru Buburuzan

DPhil student, Oxford

Email: alexbubu@robots.ox.ac.uk

Bio

I am Alex, a DPhil (PhD) student at the University of Oxford in Autonomous Intelligent Machines and Systems (AIMS CDT). I recently completed a research internship at FiveAI on Vision Language Action models for autonomous driving, advised by Dr. Puneet Dokania.

I 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, later returning as a research engineer. I then joined FiveAI for a research internship on multimodal sensor fusion and synthetic data generation for autonomous driving, advised by Dr. Romain Mueller.

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

Foundation Models for Trajectory Planning in Autonomous Driving: A Review of Progress and Open Challenges
Foundation Models for Trajectory Planning in Autonomous Driving: A Review of Progress and Open Challenges
MObI: Multimodal Object Inpainting Using Diffusion Models
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers

Experience

Five AI
Research Scientist Intern
May 2025 – Mar 2026 Cambridge & Oxford, UK
Vision Language Action (VLA) models for autonomous driving.
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.