Curriculum Vitae

Profile

I am a PhD student specialising in Machine Learning and Medical Image Analysis, with hands-on experience in both academic research and industry. My expertise includes:

• 🧠 Deep learning
• 👁️ Computer vision
• 🏥 Medical imaging
• 🖥️ Large-scale ML research
• 🎨 3D generative AI models
• 🔬 Medical image segmentation

I am seeking opportunities to apply my expertise in machine learning to real-world challenges in applied research within academia, industry, or startups.

Education

• Ph.D in Machine Learning, University College London, 2021 - Present (Expected 2024)
  • Supervisors: Prof. John Ashburner, Prof. Cathy J. Price FRS
• MRes in Medical Imaging (Distinction), University College London, 2020 - 2021
  • Key modules: Inverse Problems in Imaging (81%), Machine Learning in Medical Imaging (87%)
• MSci in Chemical Physics (1st Class Honours), University of Bristol, 2016 - 2020

Publications

• L. Chalcroft, I. Pappas, C.J. Price, J. Ashburner, “Synthetic Data for Robust Stroke Segmentation.” arXiv preprint, 2024. [Full text] [Code]

• E. de la Rosa, et al. (including L. Chalcroft), “A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge.” arXiv preprint, 2024. Under review at Nature Communications. [Full text] [Code]

• L.F. Chalcroft, et al., “LKA: Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation.” 37th Conference on Neural Information Processing Systems (NeurIPS), 2023. [Full text] [Code]

• L.F. Chalcroft, et al., “Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels.” ASMUS/MICCAI, 2021. [Full text] [Code]

Skills

• Technical Skills:
  • 🐍 Python
  • 🔥 PyTorch
  • 📊 TensorFlow
  • 🧮 JAX^
  • 🖥️ C++^
  • 🦀 Rust^
  • 📐 MATLAB
• Domains:
  • 🧠 Deep Learning
  • 👁️ Computer Vision
  • 🏥 Medical Imaging
  • 🎯 Segmentation

^Limited experience

Research Experience

Computer Vision Researcher

Tractive, London, UK (Jan 2024 - Oct 2024)

• Led ML research for A16Z-backed pre-seed startup using 3D Generative AI for Retopology
• Translated research from both 3D graphics and generative AI literature
• Conducted large-scale training of transformers using PyTorch and FSDP on Google Cloud VMs
• Wrote production backend code in C++ and Rust

PhD Research Student

University College London, London, UK (Aug 2021 - Present)

• Developed physically-constrained synthetic data framework for robust deep learning in medical imaging
• Created convolutional attention models for 3D medical image segmentation; presented at NeurIPS 2023
• Authored PyTorch library (ssUNet) for 3D contrastive learning in medical imaging
• Applied synthetic data and novel VAE models to stroke lesion segmentation tasks
• Enhanced hypernetworks with self-supervised learning for diverse domain adaptation
• Implemented 3D VD-VAE for normative modeling and anomaly detection in medical imaging

MRes Research Student

University College London, London, UK (Sept 2020 - Aug 2021)

• Developed robust segmentation algorithms using hypernetworks for domain-specific medical imaging
• Created custom PyTorch library for CNNs, hypernetworks, adversarial training, and t-SNE visualisation
• Researched image-level false-positives in segmentation, resulting in publication at MICCAI

Research Scientist (Intern)

Schlumberger Cambridge Research, Cambridge, UK (Aug 2018 - Aug 2019)

• Studied non-newtonian fluids for oil/gas drilling through rheology and diffusing-wave spectroscopy

Leadership & Teaching Experience

• 🎓 Fellowship Project Supervisor, Fatima Fellowship, London, UK (June 2023 - May 2024)
• 👨‍🏫 MSc Project Supervisor, University College London, London, UK (Jan 2023 - Present)
• 🔬 Research Supervisor, University College London, London, UK (Sep 2022 - Present)
• 🌟 Outreach Project Supervisor, In2Research & University College London, London, UK (Aug 2022 - Sep 2022)
• 📚 Tutor, Machine Learning and Data Science, Cambridge Spark, London, UK (Jan 2022 - Jan 2024)
• 👨‍🏫 Teaching Assistant, COMP0090: Introduction to Deep Learning, University College London, London, UK (Oct 2021 - Jan 2022)

Grants

• 🖥️ NVIDIA Academic Hardware Grant. Estimated value £5000.
• ☁️ Google GCP, estimated value $1000.