Joining the UCSF TECH Lab

Thanks for your interest in joining the UCSF TECH Lab (Technologies Empowering Consumer Health)! This is an exciting time make a tangible and ideally significant impact in consumer digital health informatics. There are a plethora of challenges and corresponding opportunities in this burgeoning field, and I am excited to train the next generation of consumer digital health researchers. Students from underrepresented and disadvantaged backgrounds and life experiences are especially encouraged to apply!

Below, I list how to work with me in each capacity. Note that availabilities under each category below are continuously changing. If there is not currently an opportunity, please check back in 6 months. I am actively pursuing additional grant opportunities to expand our team and research capabilities.

​Please note that I will be on paternity leave midway through 2025. During this time, my advising bandwidth will be very limited, and I will not be able to take on new trainees. I encourage prospective collaborators or trainees to reach out well in advance if they are interested in working together.

Clinical Informatics Fellows: If you have completed medical school and have been accepted to UCSF's Clinical Informatics Fellowship (CIF), I would be happy to be your primary mentor if you have an interest in expanding your research portfolio into consumer digital health informatics. The Clinical Informatics Fellowship at UCSF is a two year program for physicians who have successfully completed any ACGME/ABMS residency and who wish to develop and advance a professional and primary interest in informatics. The UCSF CIF seeks to train physicians who will go on to become leaders in their specialty as well as in the specialty of informatics, advancing their own work while also becoming invaluable resources and collaborators for their peers. Formal education or significant experience in computer science is helpful but not required. Fellows will either arrive or leave with the necessary CS expertise. I am happy to work with promising Fellows at all levels of prior experience; I have many possible projects spanning various skillsets.​

Postdoctoral Researchers: I do not currently have a direct postdoc position available at this time, though I will in the future. However, if you apply for a postdoctoral position with the Division of Clinical Informatics and Digital Transformation (my home division) at UCSF within the Department of Medicine and pass the division-wide screening process, I can be your primary research mentor. Please email me your CV and a paragraph describing your PhD research and future research interests if you apply through this route.

PhD Students: I am not currently accepting new PhD students, though I will in the future. I am currently supporting and co-advising 5 PhD students at University of Hawaii and 1 PhD student at Stanford. Once these students graduate, I will have new openings. When I start accepting new PhD students again, I will be able to advise students through the UC Berkeley - UCSF Joint PhD Program in Computational Precision Health or co-advise students through University of Hawaii's PhD Programs in Computer Science or Communication and Information Sciences. I am in the process of formalizing collaborations with additional PhD programs at UCSF.​ To de-risk the admissions process for both of us, significant preference for admission will be given to students who have successfully worked with me prior to their PhD application through one of the avenues listed below:

University of Hawaii Master's Students: For University of Hawaii MS students in the Computer Science program, I can co-advise your Plan A thesis in collaboration with a professor in the department, many of whom I am actively collaborating with.

Stanford Master's Students: Over the years, I have worked with many Master's students at Stanford for course credit through the ICME Xplore program (CME 291) and Applied Data Science (CME 218), which I am a regular official mentor for. I have also provided direct mentorship and guidance on the final projects for project-based machine learning and deep learning courses such as CS229, CS230, CS224N, CS231N, and many others. Many of these cases have led to first author publications and successful graduate school applications in the past.

UC Berkeley Undergraduate Students: For UC Berkeley undergraduate students in particular, I can advise your undergraduate research for course credit via the UC Berkeley URAP program, which is closely partnered with UCSF to provide health-related research opportunities to UC Berkeley students.

Master's, Undergraduate, and High School Students at Other Schools: I am happy to host paid undergraduate and high school summer research interns through the UCSF Summer Student Research Program, which includes a stipend. However, note that I am not accepting students for summer 2025 due to my paternity leave. Even outside of this context and outside of the summer, I am happy to work with any students who are admitted to any university. I have a history of mentoring Master's, undergraduate, and high school students at a wide range of institutions, getting them a first-author research publication, and subsequently placing them to top PhD programs (for undergraduate and Master's students) and colleges (for high school students). To apply, please email me your CV and a short paragraph summarizing your prior experience and interest in the field.

Volunteers: I am also happy to work with volunteers, such as full-time engineers who are interested in contributing to cutting-edge research and building a competitive PhD application portfolio during their spare time.​​​

Industry Collaboration: I am always open to industry collaboration and have a track record of consulting and scientific advisory engagements.

Academic Collaboration: I am happy to discuss collaboration opportunities. I am happy to be a Co-Investigator on grant applications. My team and I can provide expertise in: health website development, health mobile app development, data analysis for wearable data, machine learning model development, and human-centered AI including fairness, explainability, and trust.