Research
We ask immunological (or biological in general) questions and develop computational frameworks enabling scalable modeling to tackle such questions.
Immunological questions related to cancer, autoimmunity, or infection
Thymic T cell fate decision at the clonal level linked to the formation of the peripheral immune repertoire.
Dissection of peripheral immune homeostasis shaping self/non-self decision boundaries across intracellular, intercellular, cell population, and organismal levels to better guide immunotherapies in autoimmunity and cancer.
Linking (auto-)immune disease-associated GWAS SNPs and disease phenotypes in various biological layers with multiscale computational modeling to better guide immunotherapies in autoimmunity and cancer.
Elucidation of dysregulated dynamical balance between anti-microbial reaction and host-detrimental inflammation in host-viral interactions in the context of SARS-CoV-2 and unknown viral species.
Uncovering dynamical elements driving tumor microenvironments towards immune-suppressive states for better designing cancer immunotherapies.
Exploration of adaptation (resolution vs. persistence) and memory of immune responses across scales (e.g., genetic, epigenetic, and intercellular layers) in the context of chronic inflammation and vaccine design.
Computational frameworks of scalable (high-dimensional and multiscale) dynamical modeling and integration of data from various biological layers aided by machine learning/AI to construct the in silico immune system.
Figure credits: BioRender.com, isbscience.org
Construct flexible modeling frameworks seamlessly incorporating various biological layers
(especially intracellular dynamics and intercellular/multicellular dynamics).
Apply machine learning/AI-based, data science, and Bayesian methods on top of the traditional dynamical systems modeling
for integrating data from various sources such as multi-omics, clinics, and images.
for scalable modeling and its deployment for individualized therapeutics.