Senior Deep Learning Scientist, Illumina AI Lab
Kuan-Hao Chao
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
Research
Selected work
Publications
Open source
Featured software
ShorkieYeast RNA-Seq coverage predictor powered by a fungal DNA language model.
OpenSpliceAIEfficient, modular splice-site prediction framework — easy to retrain on non-human species.
LiftOnGenome-annotation lift-over tool that combines DNA and protein alignments.
SplamDeep-learning splice-site predictor that improves spliced alignments.
“Build what you need, use what you build.”
Background
Experience & education
Experience
- Aug 2025 – PresentSr. Deep Learning Scientist & EngineerIllumina, AI Lab
- May 2024 – Aug 2024Genomics ML Research InternCalico Life Sciences, Kelley Lab
- Jul 2020 – Jan 2021Research AssistantAcademia Sinica, Institute of Information Science
- Jul 2019 – Jun 2020Research StudentAustralian National University, Research School of Biology
- Aug 2018 – Jul 2019Research StudentNational Taiwan University, Centers of Genomic and Precision Medicine
Education
- Sep 2021 – Aug 2025Ph.D., Computer ScienceJohns Hopkins University
- Sep 2021 – May 2023M.S.E., Computer ScienceJohns Hopkins University
- Sep 2016 – Jan 2021B.S., Electrical EngineeringNational Taiwan University
Writing
Posts
Shorkie: learning yeast regulatory code from related fungi
Shorkie tests how fungal pretraining, evolutionary scale, and fine-tuning on yeast regulatory assays help a compact DNA model read expression and variant effects.
Read postOpenSpliceAI: retrainable splice-site prediction in PyTorch
OpenSpliceAI rebuilds SpliceAI as a faithful PyTorch implementation that researchers can retrain across species and use to study variant effects on splicing.
Read postUpdates
Recent news
I give an invited talk in the Machine Learning in Genomics session at ProbGen 2026 at UC Berkeley. The talk is titled “Predicting dynamic expression patterns in budding yeast with a fungal DNA language model.”
The OpenSpliceAI version of record is published in eLife. The GitHub repository and documentation are available.
Our Shorkie preprint describing a fungal DNA language model for predicting RNA-seq coverage from DNA sequence in S. cerevisiae is available on bioRxiv.
My public dissertation talk, “Decoding the Language of Genomes: Bridging Sequences and Function through Deep Learning,” is scheduled for August 25, 2025.
I defend my Ph.D. dissertation at Johns Hopkins University on August 25, 2025, advised by Steven Salzberg and Mihaela Pertea.
Let's work together
I'm always glad to talk about computational genomics, machine learning, or mentoring research projects. Reach out anytime.