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.
how can i join the lab?
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 email Irene (iychen at berkeley dot edu).
Please include in your email:
- Your CV
- A brief (1-3 sentence) description of your research background and experience
- The position you’re interested in (e.g., postdoc, research assistant, PhD student)
- Description of why you’re interested in working with me
- 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.
Postdocs: 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.
Existing EECS and CPH PhD students interested in rotating through the lab should email Irene directly. Strong applicants will have background in ML/statistics and ideally some previous research experience and publications.
All students are expected to have a strong foundation in machine learning or statistics, and many students will have previous research experience and publications at top machine learning conferences. 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.
In your PhD application, please explicitly mention your interest in working with Professor Irene Chen.
Undergraduate and masters students: 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.
Other collaborators: We welcome partners on projects with clinicians, payers, and other health organizations. Email Irene with your proposed project idea, available data, and current challenges.