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Medical Bioinformatics · Western University

Aiden Wesseling

Fourth-year Medical Bioinformatics at Western, finishing April 2027. So far that has meant crop disease diagnosis, 3D movement capture, health economics and Canadian housing data. The constant is not the subject. It is finding the question worth answering, and being straight about how far the answer goes.

Graduating

April 2027 · Western University

Based

London, Ontario

Domains

Healthcare · Agriculture · Sport · Public data

Selected work

04 entries

01

Canadian Tobacco Research Foundation · 2026

Ontario Tobacco Pathology Assistant

A grower can lose a crop to a misread leaf symptom. This tool takes a written description and photos of a sick plant and returns ranked likely conditions with the cited source behind each one, follow-up questions where the evidence is thin, and a refusal when it cannot make a confident call.

AgricultureDiagnosticsLLM retrieval
02

Co-founded venture · ongoing

SwingDNA

Two consumer cameras, no markers, and a 3D reconstruction of a golf swing accurate to roughly 9 to 11 mm per joint. I wrote the triangulation and biomechanics layer on top of RTMPose for 2D keypoints and OpenCV for the camera geometry. Every metric carries uncertainty propagated from that geometry, and joints the cameras could not see are flagged rather than filled in, so a change between two sessions can be told apart from the noise of the system.

Sports & movementComputer vision3D kinematics
03

Honours thesis · expected April 2027

Cost-Effectiveness of the JAKPOT Rule

When is a genetic test worth ordering at all? A decision-analytic model comparing a clinical rule that selects which patients receive JAK2 mutation testing against testing everyone referred for erythrocytosis in Ontario, with parameter uncertainty carried through to the result rather than reported as a single number.

HealthcareHealth economicsR
04

Course group project · 2026

Housing Burden Analysis

Out of more than 1,100 candidate variables on Canadian household data, which ones actually predict whether a household is housing burdened, and do the high-risk households fall into distinct types? Elastic Net and XGBoost with SHAP to find and explain the drivers, PCA, UMAP and K-Means to separate the population into profiles.

Public dataMachine learningPython

In progress

Due January 2027

NFL Big Data Bowl 2027

Competing in the ninth annual Big Data Bowl on NFL player tracking data. The entry will report what the analysis cannot resolve and how much additional data it would take to resolve it.

Target March 2027

Hockey Metric Reliability

How many games before a given player metric is signal rather than noise, and how large a within-player change has to be before it is detectable rather than drift. The same reliability machinery as SwingDNA, on public hockey data.

Get in touch

Have a question worth answering?