About

May – Aug 2026Shenzhen, China

Machine Learning Engineer

E3A Healthcare

Led the supervised layer and set the architecture direction of a longitudinal model that reads at-home fetal monitoring and returns a full posterior over days until delivery. The model predicts term delivery to within 3 days against 11 days for the clinical SOTA, and halves the obstetric calendar's preterm MAE.

  • Authored the first design of the cohort mixture-of-experts and its learned gate, and originated the sub-agent architecture now on the model's roadmap.
  • Built the reasoning layer and the coupled-likelihood objective behind the preterm win, 2.02 days better with the same sign on all ten seeds.
  • Wrote the evaluation methodology the project now runs on, and put GPU compute in every engineer's hands by automating multi-region GPU cluster provisioning on GCP.
Oct 2025 – Apr 2026Hamilton, ON

Research Engineer

McMaster Automotive Resource Centre (MARC)

Worked with Ford, MathWorks, and Dana on ML for battery optimization in their products. Cut battery voltage-model error 84% (12.82 mV to 2.03 mV RMSE) with a physics + LSTM hybrid, then carried it through ONNX to MATLAB and Simulink with machine-precision parity. Co-authoring a paper on agentic-AI-accelerated state-of-charge estimation built on a from-scratch EKF and SVSF.

Apr 2024 – Aug 2025London, ON

Machine Learning Researcher

Western University, Vision & Robotics Lab

Designed a biologically inspired recurrent vision model that reached 97.82% adversarial accuracy on MNIST without a single adversarial training example, outperforming MIT's CORNet-S by 12 points across MNIST, CIFAR-100, and ImageNet100.

Nov 2022 – May 2024London, ON

Student Researcher, Medical Imaging

Robarts Research Institute

Partnered with Synaptive Medical on surgical-vision AI: a U-Net at 95% DICE and sub-0.12 s per frame on live operating-room footage, plus a marker-based AR guidance prototype.