Precious Kolawole Engineer, Shopify · Research Assistant, DSIH Lab
preciouskolawole[at]cmail[dot]carleton[dot]ca

I'm an Engineer at Shopify on the Sidekick Assistant team, where I evaluate large language models and design human evaluation workflows. I also work as a Research Assistant at the Data Science for Interventional Healthcare (DSIH) Lab with Professor Matthew S. Holden, building tracked spine ultrasound datasets and reconstructing 3D ultrasound volumes.

I recently graduated with a Bachelor of Computer Science (Honours, AI/ML) from Carleton University through the Shopify Dev Degree program. During my degree, I explored how deep learning segmentation could simplify ultrasound through the NSERC Black and Indigenous Summer Research Internship. 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 began in Medical Rehabilitation at Obafemi Awolowo University in Nigeria and later pivoted to computer science. That clinical grounding shapes my career aspiration: to build data‑efficient AI workflows with clinician‑level reasoning for real‑world care.

Selected Feature

Here I talk about how coding can change your life and how it starts from the mind. Growing up in Nigeria, I saw how the environment shapes thinking. The discipline of writing code, debugging, and following rules naturally trickles into reshaping things in your life. And neuroplasticity was fun: how learning to code forms new neural pathways. I referenced my article "Tech took me out of the ghetto," but the ghetto here is the mind.

Publications

Limited Paired Labels Suffice for Binary but Not Multiclass Ultrasound Segmentation
Precious Kolawole, Matthew S. Holden
Imaging Network Ontario Symposium 2026 (ImNO 2026) (Submitted)
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]

Selected Feature

Publications

Recent News