Photo of Warren Hall (left): Heller Manus Architects; Photo of Wayne and Gladys Valley Center for Vision (right): ucsf.edu

Where is the lab based?

Reflecting the interdisciplinary nature of our research, our lab has locations on both the UC Berkeley and UC San Francisco Mission Bay campuses.

Warren Hall 120D
2195 Hearst Ave
Berkeley, CA 94709

Wayne and Gladys Valley Center for Vision
6th Floor
480 16th Street
San Francisco, CA 94158

Are you looking for new members?

We are actively recruiting research assistants/engineers, visiting students, and postdoctoral fellows. We are especially interested in hearing from individuals from nontraditional or underrepresented backgrounds. If you are interested in joining or learning more about the lab, please read position-level descriptions below and complete this Google Form.

We are looking for relevant research experience in any of the following areas: machine learning on medical data, statistics, longitudinal time-series, geospatial data, risk stratification, ethical implications of ML, health equity, and correcting for missing or biased data.

How can I join as a postdoc?

We welcome applications from PhD holders (or soon-to-be PhD holders) in computer science, biostatistics, biomedical informatics, computational medicine, or related fields. Candidates with research experience applying machine learning to medical data are especially encouraged to apply. Competitive applicants will have a strong publication record in top machine learning, medical, biomedical informatics, or statistics venues.

Postdocs with external funding sources such as the Miller Fellowship, President’s Fellowship, BIDS Data Science Fellowship, or Chancellor’s Fellowship are encouraged to get in touch.

How can I join as a PhD student?

We are looking to add 1-2 PhD students starting Fall 2024. Please see Irene’s advising statement. Unfortunately, due to the volume of emails, Irene cannot respond to or meet with all prospective students. Please apply directly to the UC Berkeley and UCSF Computational Precision Health or the UC Berkeley Electrical Engineering and Computer Science doctoral programs. In your PhD application, please explicitly mention your interest in working with Professor Irene Chen.

Existing EECS and CPH PhD students interested in rotating through the lab should email Irene directly.

We are looking for students with a strong foundation in machine learning or statistics. Many students will have previous research experience or publications at top machine learning conferences. Other key qualities are a well-defined research vision, a passion for equity and fairness, and clear communication skills.

Students primarily interested in machine learning should apply to EECS. For students interested in the intersection of machine learning and health care, either the CPH or EECS programs would be appropriate; similar thesis research can be conducted as a part of either program. Rather, the major difference is the emphasis of the training. CPH students take additional classes on the intersection of disciplines and have access to the wide UCSF community to develop a deeper appreciation of the impact that their research will have on clinical practice.

For advice on CS PhD admissions, Jean Yang and Maria Antoniak have written useful guides, and Himabindu Lakkaraju has organized and recorded an informative panel.

How can I join as an undergraduate or Master’s student?

Undergraduates are encouraged to apply through the Undergraduate Research Apprentice Program. We are looking for students who have taken at least one machine learning course and received an A. For masters students, we typically expect students to have taken a graduate-level machine learning course or have had significant related research experience.

How can I work with the lab as a collaborator?

We welcome partners on projects with clinicians, payers, and other health organizations. We have found it helpful when potential collaborators have an idea of the proposed project idea, available data, and/or current challenges. Please email Irene with your information.

What future projects are you interested in?

The lab’s research interests generally revolve around questions related to machine learning, healthcare, equity and fairness, and the overlap of these topics. Ongoing projects including auditing algorithmic bias, learning and improving clinical treatment protocols, and climate change health. Student input is very important, and Irene is always open to other ideas as well.