In Stanford’s Center for Biomedical Informatics Research, you will have the opportunity to work in close collaboration with clinicians, scientists, and healthcare systems with access to deep clinical data warehouses (e.g., electronic medical records and biobanks), implementation and evaluation opportunities in live healthcare systems, and professional development resources like (grant) writing workshops and clinical shadowing experiences. Research topics range from machine learning, designing, and evaluating clinical decision support content to disintermediate scarce medical consultation resources, evaluating large-language model applications in healthcare systems, systematically identifying ineffective clinical processes, bioinformatics analyses of population health, as well as more conventional outcomes research on the implications of physician practice against challenging issues in healthcare.
Specific near-term (funded) projects include:
(1) Developing and evaluating “AI doctor” collaborative systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user interfaces to power automated electronic consultation services to expand access to healthcare services.
(2) Measuring, predicting, and implementing appropriate antibiotic use for common infections using electronic phenotyping, supervised machine learning, live Epic/FHIR implementations for silent deployment, and multi-site data coordination.
(3) The Stanford Center for Asian Research and Education in AI (CARE AI) is seeking a fellow to contribute to interdisciplinary research at the intersection of AI, bioinformatics, and Asian health equity, with a special focus on diabetes genomics. Would include leading efforts to analyze and compare genomic data in collaboration with basic science teams. Would include mentorship opportunities for Summer student scholars.
The position will allow for the exploration of additional research threads further tailored to the applicant’s interests and career goals.
The strongest applicants will have experience in one or more key interdisciplinary areas (not all are expected, that’s the point of learning together):
Computer Science or Informatics:
Proficiency in programming and software development with a habit for robust unit testing. Our group mainly develops software in a Python + SQL environment with use of large language model APIs, cloud computing environments, and R for additional statistical analysis. For decision support prototype development and evaluation, web-based user interface design, human-computer interaction testing experience can be valuable.
Statistics and Mathematics:
Machine learning (supervised and unsupervised) methodology and evaluation including discrimination vs. calibration measures and (hyper)parameter optimization through cross-validation. Observational research methods including interpreting multivariate regression, missing data imputation, propensity score matching, and bootstrap simulations.
Biomedical / Healthcare Science:
Understanding of clinical decision-making processes, healthcare quality metrics, financial incentives, and decision support interfaces and pitfalls.
A Ph.D. in a quantitative field with a strong programming and statistics background
Track record of completed research projects
Well-written, peer-reviewed papers are expected.
Specific responsibilities and research projects will be tuned to the career goals, technical strengths, and interests of the applicant.
– CV
– Example research paper
– 2-3 references
– A brief career goal statement (that reflects alignment with the projects we would likely pursue together)
