About
Ut ab ordine chaos, sic ab absurditate veritas.
As from order, chaos; so from absurdity, truth.
PhD studying evals under randomness with GNNs.
Particularly interested in evaluation frameworks, turning research into useful production ML systems, and the intersection of randomness and model performance.
Reach out: will dot leeney at gmail dot com
Timeline
AI Engineer
StackOne
Building AI features and training tool-calling models. First month spent building an AI agent that translates provider errors into clear resolution steps.
ML Engineer
OKKO Health
Built predictive models for early disease detection using remote patient monitoring data for AMD macular degeneration patients. I'm proud to have developed an AI anomaly detection system to support the mission of preventing avoidable blindness.
PhD in Artificial Intelligence
University of Bristol
Researched best practices for evaluations and evaluating under randomness. Thesis: 'Unsupervised Graph Neural Networks' - focused on evaluation methodologies and model comparison. Defined metrics for quantifying model performance under uncertainty, with applications in graph neural networks and beyond.
MEng Engineering Mathematics (1st with Honours)
University of Bristol
Applied mathematics to real-world engineering challenges through an interdisciplinary lens. Core focus on mathematical modelling, data science, and machine learning fundamentals. Master's dissertation explored biologically-inspired RNN Hebbian learning rules for decision-making processes. The program's emphasis was on on bridging pure mathematics with practical engineering applications.