RNA therapeutics
Using sequence models to prioritize RNA therapeutic designs before experimental screening.
Research
Interpretable AI for RNA therapeutic design
With a background in rare disease genetics and spliceosomal biology, I develop interpretable models that make splicing predictions easier to test and use in RNA therapeutic design.
Focus
RNA biology as a design problem
Individualized RNA therapies are now possible, but broader implementation is limited by the high cost of antisense oligonucleotide (ASO) screening and unanticipated toxicity.
My work connects sequence analysis, splicing mechanisms, and predictive models to address these bottlenecks and make RNA therapeutic design more interpretable.
Rare disease genetics
Variant-centered thinking grounded in patient genetics and the practical constraints of small-patient populations.
Spliceosomal biology
Models grounded in RNA processing, splice-site choice, and splicing mechanism.
Interpretable models
Predictive systems designed to identify causal signals and guide experimental decisions.