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Ka Yee Yeung, Ph.D.

Phone Number
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Computer Science
University of Washington
Computer Science
University of Washington
Mathematics in Computer Science
University of Waterloo, Ontario, Canada
Mathematics in Computer Science and Actuarial Science
University of Waterloo, Ontario, Canada


My research focuses on the development of machine learning tools, their application to computational biology and the development of containerized tools to enhance the reproducibility of research. My research focuses on the development of methods and containerized cloud-enabled software tools to facilitate the reproducible analyses of big biomedical data. I also develop machine learning methods that blend both computer science and statistics for applications in bioinformatics.

Current Research

I am the PI of a NIH-funded basic research grant titled Intelligent deployment of containerized bioinformatics workflows on the cloud . Our overarching goal is to deliver the latest technological advances in containers and cloud computing to a typical biomedical researcher with limited resources who works with big data. Specifically, we will develop a user-friendly drag-and-drop interface to enable biomedical researchers to build and edit containerized workflows.

Honors and Awards

Recipient of the 2019 Distinguished Research Award at University of Washington Tacoma.

Selected Publications

A graphical, interactive and GPU-enabled workflow to process long-read sequencing data. Shishir Reddy, Ling-Hong Hung, Olga Sala-Torra, Jerald Radich, Cecilia CS Yeung, Ka Yee Yeung. BMC Genomics 22, Article number: 626 (2021).

Building containerized workflows using the BioDepot-workflow-Builder (BwB). Ling-Hong Hung, Jiaming Hu, Trevor Meiss, Alyssa Ingersoll, Wes Lloyd, Daniel Kristiyanto, Yuguang Xiong, Eric Sobie, Ka Yee Yeung. Cell Systems 2019, volume 9, issue 5, pages 508-514.E3.

Holistic optimization of RNA-seq workflow for multi-threaded environments. Ling-Hong Hung, Wes Lloyd, Radhika Agumbe Sridhar, Saranya Devi Athmalingam Ravishankar, Yuguang Xiong, Eric Sobie, Ka Yee Yeung. Bioinformatics 2019, volume 35, issue 20, pages 4173-4175.

Reproducible Bioconductor Workflows Using Browser-Based Interactive Notebooks And Containers. Reem Almugbel, Ling-Hong Hung, Jiaming Hu, Abeer M. Almutairy, Nicole E. Ortogero, Yashaswi Tamta, Ka Yee Yeung. Journal of the American Medical Informatics Association (JAMIA) 2018, 25(1): 4-12 (Editor's Choice).

Model-based clustering with data correction for removing artifacts in gene expression data. William Chad Young, Ka Yee Yeung, Adrian E. Raftery. To appear in The Annals of Applied Statistics 2017. arXiv:1602.06316

GUIdock-VNC: Using a graphical desktop sharing system to provide a browser-based interface for containerized software. Varun Mittal, Ling-Hong Hung, Jayant Keswani, Daniel Kristiyanto, Sung Bong Lee and Ka Yee Yeung. Gigascience 2017, 6(4): 1-6.

fastBMA: Scalable Network Inference and Transitive Reduction. Ling-Hong Hung, Kaiyuan Shi, Migao Wu, William Chad Young, Adrian Raftery, Ka Yee Yeung. Gigascience 2017, gix078.

GUIdock: Using Docker containers with a common graphics user interface to address the reproducibility of research. Ling-Hong Hung, Daniel Kristiyanto, Sung Bong Lee, Ka Yee Yeung. PLOS One 2016, 11(4):e0152686.

A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data. William Chad Young, Adrian E. Raftery, Ka Yee Yeung. Mathematical Biosciences and Engineering (MBE) 2016, 13(6): 1241-1251.