I am a computational biologist working in Dr. Douglas A. Lauffenburger lab (2019 fall-Present) at MIT. My research interests lie in developing machine learning and deep learning approches to reveal hidden immunological mechanisms in heterogeneous host-pathogen interaction using multi-omics and quantitative imaging. My recent research projects are computational method developments on pathogen genome sequences and cellular signaling to discover features that best predict survival states of individuals with infectious diseases (for example, tuberculosis, HIV and Ebola). I have been collaborating with Dr. Galit Alter (Ragon Institute of MGH, MIT and Harvard) on COVID-19 related projects including Multisystem Inflammatory Syndrome in Children (MIS-C), Common Human Coronaviruses cross-reactivity, and Convalescent Plasma (CCP). I focus on using system serology to identify humoral immune response that correlates with disparate clinical phenotypes, which could provide critical insights into COVID-19 pathogenesis and therapeutics. During my PhD, I worked with Dr. Kwonmoo Lee (now at Boston Children’s Hospital) on developing deep learning and machine learning models for quantitative cellular imaging in cytoskeleton dynamics. I dedicated on time-series data modeling to deconvolute subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging. Before that, as a research associate, I enhanced myself on Bayesian Statistical Learning and Global Optimization under supervision of Dr. Patrick Flaherty.