about
I'm an ELLIS PhD student at IST Austria supervised by Francesco Locatello, co-supervised by Arthur Gretton at the Gatsby Computational Neuroscience Unit, University College London.
My current research interests centre around practical machine learning methods for causal inference and causal representation learning. I'm motivated by problems in biology that offer the chance to gain insights into the mechanisms of human disease and behaviour, and in medicine with the potential for real-world impact on patient care.
Previously, I was a full-time staff data scientist at the Centre for Data Science and Digital Health (CREATE) at Hamilton Health Sciences in Hamilton, Canada. During my undergraduate studies, I worked in the lab of Anna Goldenberg at The Hospital for Sick Children in Toronto. In the past, I've also consulted on software engineering, machine learning engineering, and data science projects ranging from back office finance to consumer-facing mobile and web applications.
[github] [scholar] [linkedin]employment
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Junior Data Scientist
Centre for Data Science & Digital Health, Hamilton Health Sciences
2019 - 2024
education
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Doctor of Philosophy, Computer Science
Institute of Science and Technology Austria
2024 - present -
Master of Science, Statistics
University of Toronto
2021 - 2024 -
Honours Bachelor of Science, Bioinformatics and
Computational Biology & Neuroscience
University of Toronto
2015 - 2019
selected publications
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Optimizing Patient Record Linkage in a Master Patient
Index Using Machine Learning: Algorithm Development and
Validation
JMIR Formative Research, 2023 [doi]
Walter Nelson, Nityan Khanna, Mohamed Ibrahim, Justin Fyfe, Maxwell Geiger, Keith Edwards, Jeremy Petch -
The Toronto post liver transplant hepatocellular
carcinoma recurrence calculator: a machine learning
approach
Liver Transplantation, 2022 [doi]
Tommy Ivanics*, Walter Nelson*, Madhukar Patel, Marco Claasen, Lawrence Lau, Andre Gorgen, Phillipe Abreu, Anna Goldenberg, Lauren Erdman, Gonzalo Sapisochin -
Semi-Markov Offline Reinforcement Learning for Healthcare
Conference on Health, Inference, and Learning, 2022 [pmlr]
Mehdi Fatemi, Mary Wu, Jeremy Petch, Walter Nelson, Stuart J Connolly, Alexander Benz, Anthony Carnicelli, Marzyeh Ghassemi -
Opening the black box: the promise and limitations of
explainable machine learning in cardiology
Canadian Journal of Cardiology, 2021 [doi]
Jeremy Petch, Shuang Di, Walter Nelson
open source & side projects
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SanteMPI Record Linkage Configuration Optimizer
Through our work at CREATE, I engineered a plugin for optimizing the parameters of the SanteMPI patient record linkage algorithm using Bayesian optimization. Sante software underpins national deployments in Tanzania and Myanmar. -
Compare Concordance
I authored a Python port of the compareC R library for doing statistical inference about correlated right-censored c-indices, which commonly arise in the comparison of predictive survival models. The package is available on PyPi. -
Generalized Wishart process
I've written a Gibbs sampling implementation of the generalized Wishart process model by Wilson & Ghahramani (2013) in Python, including a custom implementation of elliptical slice sampling for sampling from correlated posteriors arising from correlated Gaussian priors. -
PyTorch Lightning Bolts
I contributed a bugfix to the Python implementation of the Bootstrap Your Own Latent model, an unsupervised image representation learning model. -
Suitcase
While working as a software engineer, I authored a Ruby client library for the Expedia and Hotwire travel APIs. It has over 75,000 downloads. Under active development until 2014.