Now Recruiting · Shanghai, 2026

AI for Precision Oncology

We integrate clinically driven artificial intelligence and mechanistic systems biology to make immunotherapy predictive, equitable, and programmable.

Three connected pillars of
precision immunotherapy

01

Predictive AI for
Precision Immunotherapy

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 Decision
02

Systems Immunology of
Resistance Mechanisms

Using single-cell omics, spatial transcriptomics, and multi-scale modeling, we map immune suppression, metabolic stress, and cellular heterogeneity driving treatment resistance.

From Correlation to Mechanism
03

eOncoImmune
Digital Twin Platform

Our 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 Design

Dr. Tiangen Chang

PhD 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 Biography
Dr. Tiangen Chang

International
Collaborators

The 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.

Join the Grand Adventure of Science

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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