Precious Kolawole

Email: preciouskolawole[at]cmail[dot]carleton[dot]ca
Precious_Kolawole

Hi, welcome to my website 👋🏽

About Me

I'm Precious Kolawole. I recently graduated with a Bachelor of Computer Science (Honours, AI/ML) from Carleton University through the Shopify Dev Degree program. Shopify sponsored my degree while I interned across frontend, backend, and machine learning engineering teams.

During my degree, I worked as an undergraduate researcher in the Data Science for Interventional Healthcare (DSIH) Lab with Professor Matthew S. Holden. Through the NSERC Black and Indigenous Summer Research Internship, I explored how deep learning segmentation could simplify ultrasound and reduce the burden of clinical annotation. Before that, at the Nesbitt Biology Lab with Professors Andy Adler and Jeff Dawson, I developed methods to improve concussion diagnosis using video eye-tracking and ocular EMG. I'm first author on a benchmarking paper evaluating annotation‑efficient segmentation methods for ultrasound.

After graduating, I joined Shopify full-time on the Sidekick Assistant team, where I evaluate large language models and design human evaluation workflows. I also continue my research at DSIH. Right now, I'm building a tracked spine ultrasound dataset, reconstructing 3D ultrasound volumes, and aligning them with CT scans and 3D‑printed phantom references to test how far we can simplify ultrasound.

I began in Medical Rehabilitation at Obafemi Awolowo University in Nigeria and later pivoted to computer science. That clinical grounding shapes my career aspiration in AI Medicine: to build data‑efficient AI workflows with clinician‑level reasoning and computational efficiency for real‑world care.

Publications

  • Annotation Is (Almost) Indispensable: The Limitations of Unpaired Domain Translation and Semi-Supervised Approaches to Ultrasound Simplification
    Precious Kolawole, Matthew S. Holden
    SPIE Medical Imaging 2026: Image-Guided Procedures, Robotic Interventions, and Modeling (Accepted for Oral Presentation)
    [Paper]
  • Fairness-Aware Machine Learning for Social Bias Detection in Healthcare Research Datasets
    Precious Kolawole
    Proceedings of Machine Learning Research (PMLR), Deep Learning Indaba 2025
    Poster & Oral Presentation, Deep Learning Indaba 2025, Dakar, Senegal
    [Poster] [Paper]

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