Detecting Concept Drift in Medical Triage

2020 ACM SIGIR Conference on Research and Development in Information Retrieval

I developed an algorithm for detecting concept drift (a species of data shift) via model miscalibration.

The paper uses machine learning medical triage models as a motivating example.

See the below section on my master's thesis for more context.

Concept Drift in Medical Referrals Triage

2020 M.Sc. thesis

My M.Sc. was funded by a scholarship form the Presicion Driven Health Partnership. I worked on detecting when a medical machine learning model has become obsolete and requires retraining.

I won the annual "best M.Sc." prize from the Auckland University computer science department. One of my examiners said it was "the best master's thesis they had seen".

Feature Importance for Biomedical Named Entity Recognition

2019 Australasian Joint Conference on Artificial Intelligence

I did this research during a research intership at Orion Health.

The paper surveys features which have been used in biomedical natural language processing, and evaluates each feature's utility for a deep learning approach to biomedical named entity recognition.

Predicting Air Quality from Low-Cost Sensor Measurements

2018 Australasian Conference on Data Mining

I worked with the National Institute of Water and Atmospheric Research (NIWA) on modelling air quality using low cost sensors.

Low-cost sensors pose a number of challenges: they are unreliable, noisy, and may require ongoing calibration. This paper explores these challenges, and evaluates several approaches to spatiotemporal modelling of air quality using the sensors.