Decoding the language of immune recognition

OUR ENGINE

We are an interdisciplinary lab harnessing the tools of computational biology, machine learning, and experimentation to decipher the molecular determinants of immune recognition, dissect the major contributing factors in cancer development, and predict responses to cancer immunotherapy.

RESEARCH

IMMUNE RECOGNITION

Deciphering the molecular determinants of antigen recognition by T cells by employing deep learning algorithms and high-dimensional proteomic data.

CANCER ECOSYSTEMS

By combining traditional cancer biology, immunology, genomics, transcriptomics, and computational modeling, we dissect the reciprocal interactions between the evolving tumor genomes and their host immune ecosystems.

IN SILICO PREDICTION OF IMMUNOTHERAPY RESPONSE

With the world’s largest library of molecular, genetic, and clinical data, we can predict how patients with cancer will respond to immune checkpoint inhibitors using machine learning algorithms.

CANCER RISK

We are investigating the major contributing factors in cancer development.

ABOUT US

Our Mission

We employ and develop quantitative approaches to solve both basic science and clinically relevant problems in cancer.

Our Impact

Our work will substantially improve the outcomes of patients with cancer and provide novel targets for therapeutic intervention.

OUR TEAM

We are a dedicated group of scientists, postdocs, and students with diverse backgrounds ranging from cancer genomics to computational biology and machine learning. Each member brings expertise in their field to our unique and highly collaborative research environment.

featured publications

Nature Medicine

Yoo SK*, Fitzgerald CW*, Cho BA*, Fitzgerald BG*, et al. Nature Medicine. 2025.

Science

Krishna C*, Tervi A*, Saffern N*, Wilson EA*, et al. Science. 2024.

Want to work with us?

We are actively looking for talented and highly motivated students, interns, postdocs, and staff scientists to join our laboratory. Apply your skills to help us solve some of the most complex and clinically relevant scientific problems in cancer research.

If you are interested in a position in the lab, please contact Diego Chowell and include a cover letter and curriculum vitae (CV). Letters of recommendations are required at a later stage.