Always building. Always learning.

Here are some of the projects I have been working on:

	Project Name	Description	Year	Status
	arxiv code search	Searching through arxiv papers (with ML) to see if they include the code and data to reproduce the work. Active research. (Github repo)	2022	🛠️ (active)
	PyPHM	Machinery data, made easy. Open-source Python package to easily download and prepare common PHM (prognostics and health management) datasets. Use PyPHM before feature engineering or model training. (Github repo; install with pip)	2022	🛠️ (active)
	Surrogate Modeling of Time Series with GANs	Using generative adversarial networks (GANs) for modeling of time series. Applied to applications in manufacturing systems. Active research (not yet public).	2022	🛠️ (active)
	Time Series ML Pipeline	Developing scalable ETL/ML pipeline for rapid testing of feature engineering and machine learning techniques. For use on industrial time series data. Leverages HPC or cloud infrastructure. Active research. (Github repo)	2022	🛠️ (active)
	EarthGAN	Can we visualize a large scientific data set with a surrogate model? Demonstrating a proof-of-concept using the Earth mantle convection data set and GANs. Recipient of the Innovation Award at IEEE VIS 2021. (Github repo; youtube presentation; preprint article)	2021	⏸️ (paused)
	Knowledge Informed Machine Learning	External knowledge can enhance machine learning. We use knowledge from reliability engineering, and integrate it into a machine learner through the use of a Weibull-based loss function. Demonstrated on bearing remaining-useful-life prediction. Published (accepted) in Journal of Prognostics and Health Management. (Github repo; preprint article)	2021	✔️ (complete)
	CDC Birth Data	Personal project exploring the CDC birth data files from 1968 to 2020. Developed for HPC and local compute. (Github repo)	2021	🛠️ (active)
	Compute Canada Tutorials	New to Compute Canada and high performance computing? Here are some tutorials to get you started. (Github repo; youtube link)	2021	✔️ (complete)
	Anomaly Detection for Tool Wear Monitoring Using a $\beta$-VAE	Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder ($\beta$-VAE). Published in the International Journal of Hydromechatronics. (Github repo; preprint article)	2020	✔️ (complete)
	Beautiful Plots	A collection of beautiful plots, and other data visualization explorations. (Github repo)	2020	🛠️ (active)
	Feature Engineering in Tool Wear Monitoring	Demonstrating feature engineering and classical machine learning for use on tool wear monitoring. Applied to industrial partner’s manufacturing environment. Discussed in thesis, Feature Engineering and End-to-End Deep Learning in Tool Wear Monitoring.	2020	✔️ (complete)
	Anomaly Detection in the Wild	Talk on real-world anomaly detection methods within health care, astronomy, finance, and manufacturing. Presented at Pycon Canada 2019 (video of presentation was lost, but here’s the repo and pdf)	2019	✔️ (complete)
	AI in Public Health	Presentation on AI within the public health domain. Talk given to medical residents and MOH’s at KFL&A Public Health. (pdf)	2019	✔️ (complete)
	Deep Learning for Partial Discharge Detection	Final project for Deep Learning course (CISC-867). Used a convolutional autoencoder to detect faults in medium voltage power lines. Received an A+ in the course too! (Github repo; final paper)	2019	✔️ (complete)