A core component of ImmunoX’s educational focus is our Computational Immunology Emphasis. In addition to the core immunology curriculum, students pursuing the computational immunology emphasis will participate in courses and program activities that prepare them to apply and create computational solutions to answer immunological research questions. Students pursuing this path should plan to take either BMI 206: Statistical Methods of Bioinformatics (Fall) or BMI 203: Biocomputing Algorithms (Winter) by the end of their second year to form the foundation of their computational understanding. This is followed by the annual spring ImmunoX Computational Immunology minicourse and annual summer hackathon.

For more information, please contact ImmunoX@ucsf.edu.

For incoming graduate students looking to demonstrate interest in pursuing the Computational Immunology Emphasis, please fill out this form.

One of the key offerings of our Computational Immunolgy Emphasis is the ICBI (ImmunoX Computational Biology Initiative) Hackathon. This is an annual event where trainees, staff members, and faculty are combined into teams to work through a series of challenges over two days. Past challenges include making immunological insights from cell data of rare patient phenotypes and biological extrapolations made from COVID-19 patient tissue data.

Faculty Leadership Team

As with the rest of the ImmunoX Graduate Program, the Computational Emphasis places a heavy emphasis on knowledge gained in research labs. To that end, those looking to specialise in computational immunology are recommended to join a lab which focuses on that field.

For its part, ImmunoX has prioritized the central funding of such research through the ImmunoX Computational Biology Initiative, or ICBI. A joint effort between the Data Science CoLab and ImmunoX, ICBI seeks to promote data science participation and innovation in the biological space. The overarching goal of the program is to leverage data collected by the entire UCSF ImmunoX research community, taken from a cross-section of health and disease, to discover treatments for human disease. This directly benefits our trainees in that they have access to multiple different labs data sets through the Data Science CoLab, greatly expanding their possibilities of their research projects