Senior Deep Learning Scientist, Illumina AI Lab
I build machine learning for genomics — sequence-to-function models, genome annotation, and DNA language models.
I am a Senior Deep Learning Scientist at the Illumina AI Lab. I earned my Ph.D. in Computer Science from the Center for Computational Biology, Johns Hopkins University (August 2025), advised by Steven Salzberg and Mihaela Pertea. My research focuses on AI for genomics — sequence-to-function modeling, genome annotation, and DNA language models. I hold a B.S. in Electrical Engineering from National Taiwan University and exchanged in my final year at the Australian National University.
What I work on
Gene expression
Shorkie is a masked-DNA language model pretrained on 165 fungal genomes and fine-tuned on high-resolution yeast transcription-factor induction time-course RNA-seq data to predict dynamic expression patterns.
RNA splicing
A dilated residual convolutional neural network that decodes RNA splicing, alternative splicing, and the impact of genetic variants on cryptic splicing — released as Splam and OpenSpliceAI.
Genome annotation
Graph-based methods that combine fragmented DNA and protein alignments to assemble more accurate genome annotations, implemented in the LiftOn annotation lift-over tool.
Genome assembly
Han1: the first gapless, reference-quality, fully annotated Southern Han Chinese genome, assembled from PacBio HiFi and Oxford Nanopore long reads using T2T-CHM13 as a guide.
Pangenome indexing
New renaming heuristics and an SMT solver that make the Wheeler-graph recognition problem computationally feasible — the foundation for compact, searchable pangenome indexes.
Selected work
“Build what you need, use what you build.”
Background
Experience
Education
Writing
The story behind Shorkie — a fungal DNA language model that learns the regulatory grammar of budding yeast and predicts how its genes turn on and off over time.
Read postThe story behind OpenSpliceAI — a faithful, fast PyTorch reimplementation of SpliceAI that anyone can retrain on any species, and use to read the splicing impact of genetic variants.
Read postUpdates
Excited to share that I’ll be giving a talk in the Machine Learning in Genomics session at the Probabilistic Modeling in Genomics Conference 2026 at UC Berkeley! My talk is titled “Predicting dynamic expression patterns in budding yeast with a fungal DNA language model.”
I’m excited to share our new preprint on Shorkie—a semi-supervised framework that predicts RNA-seq coverage from DNA sequence in S. cerevisiae.
Excited to announce I’ve joined Illumina Artificial Intelligence Lab as a Senior Deep Learning Scientist & Engineer.
Friends, I’m thrilled to invite you to my public dissertation talk, “Decoding the Language of Genomes: Bridging Sequences and Function through Deep Learning” on August 25th. Hope to see you there!
Heart full—I defended my PhD at Johns Hopkins on August 25, 2025. I’m deeply thankful to my advisors, Steven Salzberg & Mihaela Pertea; my committee—Ben Langmead, David Kelley, and Anqi Liu; labmates, friends, and family—for your unwavering support.
I'm always glad to talk about computational genomics, machine learning, or mentoring research projects. Reach out anytime.