Where single-cell biology, machine learning, and medicine meet.
Four connected research areas, from cancer immunology through single-cell and spatial omics to AI for clinical decision-making. Each features selected work I have contributed to.
Fig. 02Cells in a learned embedding, linked by a nearest-neighbor graph
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Machine learning for single-cell & spatial omics
Implementing and benchmarking deep-learning models — graph neural networks and transformers — for single-cell and spatial omics, with contributions to an integrated cell atlas of endoderm-derived organoids, a benchmark of feature selection for single-cell data integration and a study of RNA outside segmented cells in spatial transcriptomics.
Fig. 02A cytotoxic T cell’s track through tumor tissue to its target
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Tumor immunology & cell therapy
How cytotoxic T cells navigate and survive inside tumors — from CAR T-cell therapy of solid tumors in preclinical models and intravital imaging of T-cell–myeloid interactions to single-cell and spatial maps of the tumor microenvironment.
Fig. 02A peptide in the HLA groove, with one polymorphic residue
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Transplant immunology & hematologic malignancies
Extending a pipeline that predicts minor histocompatibility antigens from exome and transcriptome data of allogeneic stem-cell transplant cohorts, with mitochondrial and nuclear variant calling for chimerism and mosaicism analysis.
Fig. 02A clinical record read token by token, attending to key entities
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AI for clinical decision-making
Testing where large language models can support oncology care — from treatment recommendations for tumor-board cases to plain-language surgical reports for patients — and learning shared representations of health across diseases, modalities and time.