



AI-driven engineered T cell therapies for cancer
and autoimmune diseases
Our Research
Our laboratory integrates cutting-edge synthetic biology, immunology, and machine learning to engineer next-generation T cell therapies for cancer and autoimmune diseases.
The lab has 3 major research areas:
1
Analysis of clinical multi-omic and muti-scale datasets from T cell therapy trials to identify mechanisms of resistance
2
Building AI systems to generate T cell designs predicted to improve patient outcomes and reduce toxicity
3
Genetic screens and evaluation of novel T cell designs in models that mimic clinical resistance mechanisms

Graphics by
Photo by Jonathan Scott
We are a team of physicians and scientists specializing in medicine, immunology, cancer, autoimmunity, bioengineering, bioinformatics, and artificial intelligence.
Our culture is positive, collaborative, innovative, and fast-paced.
What drives us is the unique opportunity to create advanced T cell therapies that could transform patient care. Learn more about our team here.
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We welcome applications from outstanding and motivated undergraduate researchers, graduate students, postdoctoral scholars, and physician-scientists specialized in immunology, bioengineering, bioinformatics, and/or AI who are interested in joining our interdisciplinary research program. Here is how to apply​
Please consider donating to the Good Lab to support our research here
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Select “Stanford Medicine” from the dropdown menu
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Select "Other Stanford Designation"
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Type in "Zinaida Good Lab" in the "Other" box
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Then, complete the remainder of the form
Our Partners
The Good Lab is part of the Division of Immunology and Rheumatology and the Division of Computational Medicine within the Department of Medicine at Stanford University. We are members of the Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford Center for Digital Health, Stanford HAI, Stanford Bio-X, and the Parker Institute for Cancer Immunotherapy. Dr. Good leads Stanford Center for Cancer Cell Therapy's Data Hub. Our team is grateful to our supporters and partners.








