I build data science and machine learning systems that move cleanly from raw data to defensible insight, with an emphasis on well-motivated problems, reproducible pipelines, and model interpretability.

My work sits at the intersection of applied ML, scientific computing, and software engineering, often using public or operational data to prototype end-to-end analyses that could realistically run in production.

• Distributed-ML
  A distributed, dataset-agnostic CT preprocessing pipeline using Dask, designed for large clinical imaging datasets and downstream ML workflows.

• publicdata_ca
  A reusable data acquisition and normalization framework for Canadian public datasets (StatCan, CMHC, CIHI), supporting rapid ML case studies such as housing affordability indices and hospital utilization analysis.

• Applied ML Case Studies
  Short, tightly scoped projects demonstrating:

  • Unsupervised learning (Isolation Forests, autoencoders)
  • Feature engineering from messy public datasets
  • Evaluation under limited or noisy ground truth
  • Clear motivation and decision-oriented outputs

• YesChef GPT
  An AI-powered system that structures generative outputs into machine-readable components (ingredients, preparation steps, pickup notes), emphasizing controllability and downstream usability over novelty.

• C++ medical image registration using ITK (Insight Toolkit)
• MATLAB pipelines using Marching Cubes for carotid artery tracing in CT angiography
• Control systems for LED solar simulators supporting photovoltaic research
• Formal training in medical physics, with strong grounding in measurement, uncertainty, and validation

• Anomaly detection in healthcare operations
  Early detection of unusual demand or utilization patterns using unsupervised and semi-supervised methods.

• Public-sector ML pipelines
  Designing reusable ingestion and feature pipelines that make public data viable for rapid experimentation.

• Evaluation without labels
  Practical techniques for validating unsupervised models when ground truth is incomplete or unavailable.

• Bridging notebooks to systems
  Turning exploratory analyses into maintainable, testable services without losing scientific intent.

I approach data science as an engineering discipline:
start with a clear question, respect the data’s limitations, and build models that can be explained, tested, and trusted.

My goal is to work on problems where statistical thinking, ML techniques, and real-world constraints all matter — especially in healthcare, infrastructure, and public data contexts.