郭昕育 Xinyu Guo

Researcher & developer

Los Angeles, CA

Background

My research focuses on the intersection of genomics, statistical learning and deep learning, where I build tools that make sense of complex biological data and uncover patterns driving disease and therapy insights.

I'm especially drawn to new technology — from LLM-powered systems to computer vision pipelines. Every new algorithm is a chance to experiment and build something that bridges science and real-world impact.

Outside research I stay equally curious: usually behind a camera, chasing moments that say something about the people in them.

Research areas

Genomic foundation modelsCancer genomics & precision oncologyVariant-effect prediction (SNV, SV, fusion)Single-cell & spatial transcriptomicsSelf-supervised & contrastive learningGWAS / TWAS & statistical geneticsScientific AI agents & tool use

Technical

Python · R · C++ · SQL · Bash · TypeScriptPyTorch · PyTorch Geometric · JAXHugging Face · scikit-learn · CUDAGNN / GAT · state-space models (Mamba)RNA-seq · ChIP-seq · ATAC-seq · NGS somatic callingNextflow / WDL · DuckDB · Parquet · DockerAWS SageMaker · Bedrock · HealthOmicsMulti-GPU distributed training · HPC

Experience

Summer 2026
AI Research Scientist Intern
Abbott Cancer Diagnostics — genomic AI & precision oncology
2022 — now
Graduate Researcher
University of Southern California — biological pathology AI/ML
2020 — 2022
Graduate Researcher
Johns Hopkins University — statistical genetics, multi-omics
2020
Research Assistant
Washington University in St. Louis — clinical informatics

Education

2022 — 2026
Ph.D., Computational Biology & Bioinformatics
University of Southern California · Viterbi Fellow
2020 — 2022
M.S., Biostatistics
Johns Hopkins University · Delta Omega Honor Society
2018 — 2020
B.A., Mathematics & Computer Science
Washington University in St. Louis · Cum Laude