Abstract

In this talk, we will go through how public cloud can accelerate the workflow of a recent GWA study by National Taiwan University Medical Center. This project is aiming to establish a COVID-19 mortality prediction model leveraging UK Biobank data. Many researchers were involved and working remotely on this project. We will introduce a practical architecture enabling all researchers to work together efficiently on public cloud like Azure.

In a typical GWA study, “Quality Control” and “Genotype Imputation” are two of the most computationally intense and time-consuming phases and it’s no exceptional for this project. We will examine how we tackle the performance issue and examine the need for scalability to support Genotype Imputation, and eventually to suggest an auto-scale Azure HPC cluster approach to compute on millions of SNPs, reducing the execution time from months to days with cost-effective analysis.

Bio.

Raymond Tsai joined Microsoft in 2005 and currently in the Cloud + AI platform group. He has 20+ years of software development, HPC/EDA, and AI/ML experience. His engagements include almost all the major semiconductor customers worldwide. He has published 100+ technical articles. He holds MS degrees in Computer Science from Southern California University, MS degree in Industrial Engineering from National Jiao Tong University, and EMBA degree from National Taiwan University, as well as holding a PMP certificate.

All faculty and students are welcome to join.