Research

I work at the interface of RNA biology, machine learning, and neurogenomics, with a focus on how alternative splicing is regulated in the brain and how genetic variation disrupts RNA regulatory programs in neurodevelopmental disorders.

Overview

My research connects three complementary threads: (i) the systematic discovery and quantification of regulated splicing events — in particular microexons — from large-scale RNA-seq data; (ii) interpretable machine-learning models of the sequence and structural grammar that governs exon inclusion; and (iii) the integration of these molecular maps with human genetics to interpret disease-relevant variants. A common thread is reproducible workflow engineering that scales from single datasets to transcriptome-wide, cohort-scale analyses.

Microexons and neuronal splicing regulation

Microexons are very short exons whose inclusion is often tightly regulated across tissues, developmental stages, and neuronal cell types. My work has helped make systematic microexon discovery and quantification practical through MicroExonator, and has connected microexon regulation to neuronal development and autism biology.

Key themes include microexon discovery and quantification, neuronal alternative splicing, brain-enriched and developmentally regulated splicing programs, and autism-associated genetic variation affecting microexon regulation.

Machine learning for splicing and variant interpretation

I use machine learning to model the regulatory logic controlling microexon inclusion. The goal is to understand how local sequence context, splicing regulatory motifs, and disease-associated variants affect exon inclusion in brain-relevant transcripts, and to use these models for variant effect prediction.

Key themes: interpretable machine learning for RNA regulation, sequence-to-splicing models, the regulatory grammar of neuronal microexons, and variant effect prediction for splicing.

RNA structure and alternative splicing

I have co-led work showing that RNA secondary structures can modulate alternative splicing at transcriptome scale. This includes G-quadruplexes and Alu-mediated RNA duplexes as regulatory layers that shape exon inclusion and primate-specific transcriptome evolution.

Key themes: RNA structure as a splicing regulatory layer, G-quadruplexes, Alu-mediated RNA duplexes, and primate transcriptome evolution.

Reproducible computational biology

I care about building scalable and reproducible pipelines. My technical work uses Snakemake, Git, Conda, Unix/HPC environments, and GPU computing for machine-learning workloads, applied to bulk and single-cell RNA-seq, alternative splicing analysis, and RNA regulatory genomics.

Current and emerging directions

Current work explores RNA regulatory mechanisms and transcriptomic signatures in brain disorders. I am interested in how RNA regulation, splicing, and genetic variation interact in neurodevelopmental disease.