Summary
Yuanhua Huang is an Assistant Professor and computational biologist with a decade of experience developing Bayesian and machine learning methods for noisy, high-dimensional single-cell genomics. He builds hierarchical Bayesian models, MCMC samplers and Gaussian-process approaches to tackle problems from allelic expression and clone assignment to splicing quantification in time-series and single-cell RNA-seq. His toolset spans Python, R and occasional C/C++ for performance-critical components, and he has released domain-specific packages such as DICEseq, BRIE and cardelino. Trained at Edinburgh and seasoned by research stints at EMBL-EBI and Harvard collaborators, he combines theoretical rigor with practical software engineering to translate statistical methods into reusable tools. A less obvious strength is his consistent focus on modeling experimental noise and design—turning messy biological data into robust inference about molecular regulation and mutation evolution.
10 years of coding experience
Doctor of Philosophy (Ph.D.), Informatics: machine learning and computational biology, Doctor of Philosophy (Ph.D.), Informatics: machine learning and computational biology at The University of Edinburgh
Bachelor of Engineering (B.E.), Automation Engineer Technology/Technician, Bachelor of Engineering (B.E.), Automation Engineer Technology/Technician at Tsinghua University