About
I am a postdoctoral research fellow in the Cancer Data Science Laboratory at the National Cancer Institute, NIH, where I develop computational immuno-oncology applications. My major focus of study is secreted-factor-mediated signaling and cell-to-cell communication — inferring which secreted proteins are active in a tissue and how they route signals between cells, from bulk, single-cell, and spatial transcriptomics.
My work runs from experimental systems for synthetic bacterial signaling (2011–2012, microfluidic concentrator arrays and inkjet-printed cell systems) through to large-scale computational inference of cytokine and secreted-protein activity.
Current focus
- Cell–cell communication and cytokine-activity inference. Co-author on SecAct, a framework for inferring the signaling activities of 1,170 human secreted proteins from spatial, single-cell, and bulk transcriptomics (Nature Methods 2026), plus open-source tools for regression-based activity inference from transcriptomic data. I am the author of secactpy (Python, hosted under the Jiang Lab organization) and spatial-gpu (GPU-accelerated spatial kernels), and a contributor to the related Jiang Lab R packages secact and spacet.
- Clinical biomarker discovery. Prior-informed machine learning for disease-phenotype prediction, including immune-checkpoint-blockade response (IC2Bert, Scientific Reports 2025) and chemotherapy response in TNBC.
- AI in precision oncology. Co-author on “Hallmarks of artificial intelligence contributions to precision oncology” (Nature Cancer 2025), an analysis of where AI has — and has not — moved the needle in oncology.
- Overlap statistics and sparse representation. Effect-size measures (GSSMD, GMDM), sparse-coding-based classifiers, and self-supervised learning for biosignal reproducibility.
Several projects are ongoing at NCI/CDSL; releases and manuscripts will be linked on the Research and Publications pages as they become available.