PhD Candidate @ The University of Queensland (UQ) · Computational Pathology · Spatial Omics · Machine Learning
Research theme: Learning molecular signals and clinical biomarkers directly from routine histopathology slides by aligning pathology foundation models with spatial transcriptomics/proteomics.
🔄 Career pivot
Chemical Engineering → Digital Transformation → Computational Biology → Computational Pathology. I started in Chemical Engineering (Honours), led digital transformation projects in industry (Unilever and Nestle), pivoted to structual biology (Tokyo Tech), and found my home in AI for pathology & spatial omics—building scalable, reproducible pipelines to bridge images and molecules.
- Slide → Gene Expression
- Multimodal Fusion: Combine WSIs + spatial omics + clinical metadata for subtype and risk stratification in breast cancer.
- Efficiency @ Scale: Memory-safe WSI+ST pipelines on HPC (Slurm, Apptainer), using Dask/Zarr where helpful.
Computational pathology, spatial omics, representation learning, contrastive/knowledge distillation, uncertainty & interpretability.
I’m happy to collaborate on WSI × Spatial Omics, foundation model adaptation, and benchmarks. If you have relevant datasets or clinical questions, let’s talk!
Contact: [email protected]
- ENTJ · enjoys building structured learning trackers and clean pipelines.
- Friendly reminder: science is a team sport — please open an issue if any repo is missing steps or you hit env problems.
- StrengthsFinder (Top 5): Competition · Command · Maximizer · Significance · Ideation