Our overarching goal is to use machine learning to bring high-throughput immunology (led by Brice Gaudilliere) and clinical phenotyping (led by Martin Angst) together.

Multiomics Analysis

The immune system is involved in a symbiotic interplay with various biological and environmental factors. To understand the immune system in a clinical context these relationships must be characterized accurately. We develop integrative statistical models that bring together various datasets (including from the transcriptome, metablome, microbiombe, and proteome) of patients to both diagnose diseases more accurately and to understand their underlying biological mechanisms.

Flow/Mass Cytometry Bioinformatics

Flow/mass cytometry instruments analyze millions of cells per sample, creating a wide range of challenges for standard bioinformatics tools developed for genomics and proteomics. On the other hand, the large number of observations in these datasets (millions of measurements per patient) provides a unique environment for testing new machine learning concepts. The intersection of high-throughput single-cell biology, cytometry, and machine learning is a major focus of the lab.

Mass Cytometry (CyTOF) in clinical studies

[Primarily lead by the laboratory of Dr. Brice Gaudilliere] High-parameter, single-cell technologies are revolutionizing the fields of human immunology and precision medicine. The lab employs a multidisciplinary approach involving deep immune profiling of patient samples and customized computational methods to link high-dimensional immune datasets to precisely defined clinical outcomes. 

Algorithm Benchmarking for High-throughput Single Cell Assays

Single cell biology is a young field. Shared datasets are scarce and empirical evaluation best practices are not yet adopted by the community. Old algorithms often out-perform new ones when evaluated independently. Users are not sure which algorithms should be used for which problem. FlowCAP is an international initiative to fix this problem. Together with DREAM we further extend this to the general systems biology community.

Clinical Phenotypying

Biological data is as good as the clinical data that accompanies it. Clinical phenotyping has been lagging behind other areas of medicine, mostly relying on data collection using web-based (or worse, paper-based!) self-reported questionnaires and interviews. In partnership with the tech industry here in Silicone Valley, we are evaluating several modern opportunities to bring long-term phenotyping to the 21st century using wearable technologies, virtual agents, and more.

Model Reduction / Explainable Artificial Intelligence

Developing accurate predictive models is just a good start. Reproducing those same accurate results using a limited number of measurements is often essential for regulatory approval, to develop a viable product that would scale well, and to understand the biological processes drive the model’s predictive power. Stanford is home to the world’s leaders in sparse learning, post-selection inference, and other model reduction techniques (e.g., Rob Tibshirani  and Trever Hastie) who help us tackle these challenges.

Prematurity Research

Human pregnancies last precisely nine months. To do that, mothers rely on a finely tuned immune balance (or immune clock), which controls an immunological switch from tolerance to rejection of the fetus (a hemi-allogeneic foreign body!). In collaboration with the March of Dimes and the Bill and Melinda Gates’ foundations, the lab studies the adaptation of feto-maternal immunity to normal and abnormal pregnancies (e.g. associated with preterm birth and preeclampsia). Using an multi-disciplinary approach that integrates deep immune profiling of pregnancy with changes in cell-free RNA expression (collaboration with Steve Quake), metabolomics (Mike Snyder) and the maternal microbiota (David Relman), our goal is to identify peripheral blood signatures that predict pregnancy complications.

Recovery from Trauma

How did the caveman survive after getting attacked by a saber toothed tiger? Turns out the immune system has evolved sophisticated cellular programs that enables us to heal and recover from major trauma. Using high dimensional mass cytometry, we have recently shown that the signaling behavior of a network of innate immune cells measured before surgery predicted surgical recovery. We are now in the process of prospectively connecting these predictions to measurements from wearable devices to provide patients with real-time feedback. Ongoing work in humans and animal models focuses pre-operative interventions (“pre-hab”) that alter a patient’s immune state to improve recovery after surgery.


Stroke is the number one cause of long-term disability in the world. Acute Stroke elicits a profound systemic inflammatory response, not unlike traumatic injury. We are interested in early immunological mechanisms, mobilized hours to days after the ischemic event that predict patients’ long-term neurocognitive recovery. This is a collaboration with Marion Buckwalter  and Maarten G Lansberg.

Space Medicine

Long-term space travel is around the corner! For thousands of years, our immune system has evolved here on earth, under 1g gravity. What happens when gravity disappears? Accumulating evidence suggests that  astronauts immune system is depressed in space. This has both short and long term consequences to their health, with increased predisposition to opportunistic infection and perhaps cardiovascular disease. We are lucky to participate in multidisciplinary and collaborative efforts (including a project launched by Millie Hughes-Fullford, a former STS-40 Spacelab Life Sciences astronaut) to understand the adaptation of the human immune system to long-term exposure space travel.

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