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Connor Pink

Connor Pink

I'm doing an M.Sc. in Computer Science at the University of Guelph, in Dr. Sykes' AI and Healthcare Lab. Most of my research is on chest X-rays: segmenting lungs, generating synthetic scans, and testing whether those synthetic scans actually help a tuberculosis classifier.

Before that I did two co-op terms building internal software, at Employment and Social Development Canada and at Peterborough Regional Health Centre.

Burlington / Toronto, ON email github linkedin résumé

Now

Do synthetic X-rays help, or is it just more data?

My thesis looks at training a TB classifier on mixes of real and generated chest X-rays (from a WGAN-GP and a latent diffusion model), to separate two effects: new variety in the data versus simply having more images. Part of it became a short paper at Canadian AI 2026. The diffusion images look better, but neither generator beat just duplicating real images to the same count. Read the paper or the project notes.

Grid of real and synthetic chest X-rays, by source and class.
Fig. 1. Real chest X-rays next to WGAN and diffusion samples, for TB and normal cases.
Connor Pink presenting research slides in a lecture hall.
Fig. 2. Presenting the pipeline at my M.Sc. seminar.
Research

Generative AI for Medical Imaging

My thesis work on synthetic chest X-rays: when they help a tuberculosis classifier, is it because they add new variety, or just because there are more images? Part of it became a short paper at Canadian AI 2026.

2026 · M.Sc. thesis, University of Guelph · paper at Canadian AI 2026

CXR Segmentation and TB Imaging Pipeline

A chest X-ray research pipeline combining tuberculosis classification, lung segmentation, and diffusion generation, with a current focus on segmentation model comparison and efficiency tradeoffs.

2025 · University of Guelph / Graduate Research

ML-Based Bug Detection System

An applied research project for Nevvon that modeled application freezes through session analysis, stuckness heuristics, and predictive factor discovery.

2025 · University of Guelph / Industry Collaboration

All research →