Julian Hong, Professor

Closed (1) Informatics for personalized cancer therapy (data science, machine learning, natural language processing, imaging analytics)

Applications for fall 2021 are now closed for this project.

The Hong lab is part of the UCSF Department of Radiation Oncology and Bakar Computational Health Sciences Institute. We focus on combining clinical domain knowledge with data science to generate insights from real world data, develop actionable computational tools, and evaluate the benefit of these advances for personalized cancer care. We have a specific interest and expertise in machine learning, natural language processing, computational data extraction, and imaging analytics. We apply these methods to identify new knowledge regarding clinical practice and patient outcomes, make actionable predictions, and identify new interventions. Our lab works from end-to-end along the development and implementation pipeline to develop tools for clinicians to make a meaningful difference in patient care.

The undergraduate apprentice will perform informatics research to develop computational tools and analyses related to personalized oncology care. Specific research topics can be based on the individual's interests and experience but may include wrangling and analyzing clinical (or other) data, developing predictive machine learning algorithms, natural language processing pipelines, or quantitative imaging data extraction.

They will work closely with other lab members and will be expected to conduct data analyses and prepare for presentations at lab meeting and journal clubs as appropriate. The undergraduate will join a multidisciplinary team of clinicians and scientists in the Department of Radiation Oncology and the Bakar Computational Health Sciences Institute. They will also be encouraged to take advantage of other training and learning opportunities within the UCSF Helen Diller Family Comprehensive Cancer Center, the Bakar Computational Health Sciences Institute, and other related entities at UCSF. We hope this experience will prepare the applicant for future opportunities in medicine and informatics.

Our prior URAP apprentices have been very successful in publishing their work in high impact peer-reviewed journals and presenting at national conferences (please see the lab website).

- Managing and analyzing data from sources of their interest (potentially including but not limited to clinical, imaging, social media/internet data)
- Applying statistical methods, machine learning, natural language processing for analytics.
- Communicating research problems and results to a variety of audiences including clinicians, engineers, entrepreneurs, and general public.
- Submit an abstract to a national conference.
- Author a manuscript to be submitted to a peer-reviewed journal as appropriate.

Qualifications: Currently studying in a quantitative/computational/scientific discipline. Strong programming skills, with a preference for experience in Python, R, SQL (but flexible). Strong problem-solving skills. Most of the work can be done remotely, with possible trips to UCSF. Strong communication and writing skills.

Weekly Hours: to be negotiated

Off-Campus Research Site: UCSF Bakar Computational Health Sciences Institute
480 16th St, Floor 2
San Francisco, CA 94158

Research can be also done remotely and meetings can be done closer to/in Berkeley.

***During COVID-19 pandemic, apprentices will be working remotely.

Related website: http://honglab.ucsf.edu