MS Students

All Trainees | Clinical Informatics Fellows | MS Students | PhD Students | Postdoctoral Trainees


Lauren Anderson

MS Student

Faculty Advisor

Neil Abernethy


Katelyn Banschbach, MD

MS Student

Faculty Advisor

Peter Tarczy-Hornoch


Biography:

Katelyn Banschbach, MD is board certified in pediatrics and is completing a fellowship in pediatric rheumatology at Seattle Children’s Hospital as well as a Masters of Biomedical Informatics. Dr. Banschbach grew up in Indiana and obtained a BS at Purdue University and an MD at Indiana University prior to moving to Seattle in 2018 for pediatric residency. Currently, her research centers around improving race and ethnicity data in a patient database to better understand disparities. Ultimately she hopes to use informatics to improve care for patients with rheumatic diseases.


Kevin Chen

MS Student

Faculty Advisor

John Gennari


Biography:

Kevin Chen received BS degrees in Computer Science and Neurobiology from the University of California, Irvine. He has previously worked on tractography tools using diffusion MRI data. He is interested in imaging analysis with novel and different methods. His recent projects include generating annotations for biomedical models and mapping neuropsychological conditions to anatomical regions of the brain


Wei Fan

MS Student

Faculty Advisor

Annie Chen
John Gennari


Tianran Li

MS Student

LinkedIn

Faculty Advisor

Sean Mooney


Sharon Wong

MS Student


Biography:

Sharon Wong received a B.A. in Neuroscience with additional minors in Computer Science and Psychology from Northwestern University. She is broadly interested in improving accessibility to healthcare and is passionate about identifying and addressing barriers that prevent people from having their healthcare needs met. Her recent research endeavors include conducting user-centered design research to develop a participant insight dashboard for an online problem-solving intervention, analyzing narrative data to better understand stigma related to certain health conditions, and computational neuroscience research investigating speech perception and auditory processing in infants using electroencephalography (EEG) and machine learning methods.