About
§ 01 · Background
Background
I am a Machine Learning Engineer focused on medical AI. Since 2019, I have worked at AstraZeneca R&D in Maryland, building deep learning systems across drug discovery and medical imaging.
My work spans three threads. Drug discovery ML — multimodal models that fuse cell painting microscopy with SMILES chemical representations to predict compound activity (IC50), with companion graph-based pipelines for multiplex immunofluorescence analysis. Medical imaging and segmentation — transformer-backed interactive segmentation tools for 3D volumetric data, patch-based unsupervised pipelines for imaging at scales that exceed GPU memory. Tooling and infrastructure — a unified ingestion and preprocessing platform that has become the team’s standard data layer (sixteen multi-center datasets, 150K CT volumes), and the visualization suites that R&D labs use to interpret model outputs.
Before AstraZeneca, I was a Software Engineer at Ann Arbor Algorithms, building containerized end-to-end deep learning pipelines for medical imaging — classification, 3D bounding-box detection, anomaly identification — on multimodal datasets of millions of points. I trained as a bioinformatician at the University of Michigan (M.S., 2018) after my biomedical sciences B.S. at the University of Macau.
§ 02 · Now
What I am working on
Medical AI continues to be the through-line.
Recent threads at AstraZeneca
- Automatic contrast phase classification of polyphasic CT scans (poster, AACR 2026).
- Multimodal IC50 prediction at scale across compound libraries.
Currently
- Adversarial-critique multi-agent systems — analyst → challenger → synthesizer architecture, locally hosted models via Ollama, lightweight Streamlit interface.
- GPU compute environments — Proxmox/KVM virtualization with PCIe passthrough for specialized ML and cross-platform development workloads.
§ 03 · Skills
Skills
Programming — Python (expert), C++, R.
Machine learning and AI — PyTorch (expert), Keras, scikit-learn, Hugging Face, HDBSCAN, UMAP, MLX, Ollama.
Medical AI and imaging — MONAI, nnU-Net, DICOM, NIfTI, ITK / SimpleITK.
Infrastructure and MLOps — Docker, Kubernetes, Git, Proxmox, KVM, Dash, Agile.
§ 04 · Contact
Contact
The fastest paths to reach me: