We integrate clinically driven artificial intelligence and mechanistic systems biology to make immunotherapy predictive, equitable, and programmable.
We develop AI models that leverage routinely collected clinical data to predict immunotherapy response and toxicity before treatment begins — making precision oncology broadly accessible.
From Data to DecisionUsing single-cell omics, spatial transcriptomics, and multi-scale modeling, we map immune suppression, metabolic stress, and cellular heterogeneity driving treatment resistance.
From Correlation to MechanismOur long-term goal is to integrate patient-specific data, immunological knowledge, and multi-scale computational models to simulate tumor–immune co-evolution, enabling in silico trials and rational therapy design.
From Understanding to DesignPhD in Computational Biology at University of Chinese Academy of Sciences. Postdoctoral Fellow at U.S. National Cancer Institute. His research program lies at the intersection of clinically driven AI and mechanistic systems biology, with a focus on decoding the complex, multi-scale interplay between the tumor microenvironment and the systemic immune system. He has published as first and/or corresponding author in premier journals, including Nature Cancer, Science Immunology, Cancer Discovery, and Annals of Oncology.
Full BiographyThe AISI Lab maintains active collaborative relationships with leading international research institutions and medical centers. Collaborative work has been published in Cell, Cancer Cell, and Cancer Discovery.
We are recruiting Research Scientists, Postdoctoral Fellows, and Research Assistants worldwide. We welcome interdisciplinary backgrounds — computational biology, immunology, oncology, AI, mathematics, and beyond.
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