Verified Expert in Engineering
Generative Pre-trained Transformers (GPT) Developer
Michael is a data scientist and machine learning engineer. Most recently, he led CVS’s COVID-19 vaccine demand forecasting, liaising closely with the White House and the CDC as part of Operation Warp Speed. He spends his spare time working on AI ethics problems and is an advocate and mentor for queer AI developers. An experienced leader, Michael has overseen teams of data scientists on health, workforce, and industrial projects.
Amazon Web Services (AWS), Git, Python, Generative Pre-trained Transformers (GPT), GPT, Natural Language Processing (NLP), Spark, PySpark, Computer Vision, Machine Learning, PyTorch
The most amazing...
...project I've coded is a demand diagnosis models to understand reasons for COVID-19 vaccine hesitancy across the USA. It saved hundreds of lives.
Senior Data Scientist
- Served as lead data scientist on various machine learning development, experimentation, and workforce innovation projects providing the incremental annual value of XX million USD.
- Responded to consistent urgent requests from the White House, CDC, and Operation Warp Speed leadership on capacity planning, second-dose adherence, and daily vaccine utilization.
- Implemented a fully customizable suite of COVID-19 vaccine demand forecasting and demand diagnosis models. These models anticipated vaccine demand drops and highlighted potential areas for intervention.
- Collaborated with lead designers to identify and address the impact of social determinants of health on low immunization rates, drawing on SHAP values and ethnographic data to design interventions.
- Acted as a key contributor to CVS's enterprise-wide algorithmic bias policy which set out steps for monitoring and mitigating bias along protected class lines within AI systems.
- Led retail projects including demographic-based demand forecasting for a large supermarket, roster optimization for a large Australian cosmetics chain, and theatre attendance forecasting for a large Australian cinema company.
- Delivered predictive maintenance models for a large shipping OEM, allowing a 66% reduction in sensors.
- Implemented deep learning extensions (such as LSTMs) to the existing demand forecasting product.
- Built production flows using NodeRed and deployed models using AWS serverless code tools.
Australian National University
- Built NLP machine learning models to predict the likely severity of identity theft case reports. Research officer on Australia's first large-scale study on identity theft.
Generalized Demand Forecasting Model
Supermarket Demand Driver Model
Operation Warp Speed Demand Forecasting
• Responded to consistent urgent requests from the White House, CDC, and Operation Warp Speed leadership on capacity planning, second-dose adherence, and daily vaccine utilization.
• Collaborated with lead designers to identify and address the impact of social determinants of health on low immunization rates, drawing on SHAP values and ethnographic data to design interventions.
Python 3, SQL, Python
PyTorch, Pandas, PySpark, TensorFlow, Facebook API, Keras, Scikit-learn
Data Science, Agile Software Development
Jupyter Notebook, Amazon Web Services (AWS), Azure
Convolutional Neural Networks, Machine Learning, Artificial Intelligence (AI), Computer Vision, Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT), Neural Networks, Deep Neural Networks, LSTM Networks, OR-Tools, GeoPandas
Graduate Diploma in Computing
Australian National University - Canberra, Australia
Bachelor's Degree in Law
Australian National Unviersity - Canberra, Australia