I use single-cell genomics and artificial intelligence to study how cancer evolves and spreads — across breast, lung, and hematological disease — and I teach the next generation of scientists to do the same in R.
scDNA, scRNA, and scATAC to reconstruct mutational and copy-number lineages — resolving how tumors evolve one cell at a time.
Integrating spatial expression with digital pathology to read the tumor microenvironment in its native tissue context.
Graph transformers, CNNs, and graph neural networks applied to phylogenetic and pathology data to predict metastasis and outcomes.
A free video course, a division-wide computational training program, and a forthcoming R-based textbook in computational cancer biology.
I'm a computational biologist and data scientist at MD Anderson Cancer Center, in the Department of Translational Molecular Pathology. I apply single-cell sequencing, spatial transcriptomics, AI modeling, and digital pathology to understand disease biology and identify precision biomarkers for new cancer therapies — and I lead computational training across the division, embedding bioinformatics support in faculty research and mentoring the next wave of fellows.
I care as much about making these methods learnable as advancing them — which became Leighton Lab, a free course teaching computational cancer biology in R.
I wear several hats across the division: DPLM Computational Data Consultant, DPLM Computational Training Lead, and TMP T32 Fellowship Consultant — providing embedded bioinformatics across faculty research, leading computational training, and mentoring fellows.
Built a graph transformer predicting metastasis from AI-annotated phylogenetic trees in TRACERx lung adenocarcinoma; integrated spatial transcriptomics and digital pathology to study the tumor microenvironment; designed AI surveillance for transfusion medicine.
Developed single-cell DNA sequencing approaches for triple-negative breast cancer using multi-patient panels and computational phylogenetics; integrated scDNA/scRNA/scATAC to find actionable biomarkers in AML.
Designed bulk NGS pipelines profiling Hippo-pathway-disrupted pancreatic tumors; combined molecular biology and computation to study tumor and stem-cell biology.
Three degrees across the physical, life, and formal sciences — plus early biophysics research with laser optical tweezers and molecular motors.
Additional co-authored work in Diabetologia, Regenerative Medicine, and Biochemistry Insights. Full list on Google Scholar.
Before genomics there was a tennis court and a music stand — a competitive junior tennis player and a multi-instrumentalist long before a scientist. I still think the best problem-solving borrows from both: the pattern-recognition of sport and the structure of music. The philosophy degree just gave me the vocabulary to argue about it.
Open to conversations about computational oncology, collaborations, and roles where rigorous data science meets real biological questions.