Website | Github | Email: [email protected]

Summary

Applied machine learning researcher and engineer with 7+ years experience spanning:

• AI research and engineering: Developed state-of-the-art deep neural networks (transformers, contrastive pre-training, CNNs) for cancer detection, published in Nature, and tested in NHS hospitals.
• Software Engineering: Owned data pipelines, model training, and production deployments in AWS, using Docker, Terraform, and CI/CD.
• Startup & Team Leadership: Led cross-functional teams (engineers, designers, scientists) to deliver award-winning apps (BAFTA, Webby, Apple App of the Year) with millions of downloads.
• Client-Facing & Stakeholder Management: Experience collaborating with clinical partners (NHS), enterprise clients (Institute of Physics), academia (Imperial College London), and creative institutions (Disney).

Driven by a deep interest in applied AI, dataset engineering, generative AI and language models, with open-source projects in LLMs, computer vision, and interpretability. Excited to deploy safe and reliable AI solutions.

Professional Experience

Kheiron Medical

(Acquired by RadNet in 2024)

Machine Learning Researcher (Sep 2022 – Oct 2024 | 2 yrs 2 mos)

Researched AI computer vision models for cancer detection in mammography.

• Researched and trained contrastive CNN and transformer based computer-vision models, yielding the strongest performance on internal benchmarks by +3% absolute AUC.
• Deployed models to NHS and Hungarian hospitals via large-scale prospective trials published in Nature Medicine, with NYTimes and the BBC coverage.
• Collaborated with doctors, clinicians, software engineers as well as AI researchers at Imperial College to steer my research towards maximum impact in the applied setting.
• Designed and built multi-million image datasets to unlock research leveraging contextual model inputs leading to state-of-the-art results.
• Developed Python libraries to enable valid comparison of model performance across the company.