Avery Pong
B.S., Biochemistry, University of Washington, 2019
2022-24 Trainee on NIH Training Grant in Bioinformatics
Lia Gale

2022-24 Trainee on NIH Training Grant in Bioinformatics
Anna Qi

B.S., Biology: Bioinformatics, University of California San Diego, 2022
2022-24 Trainee on NIH Training Grant in Bioinformatics
Jenny Dong

2022-23 Trainee on NIH Training Grant in Bioinformatics
Improving women’s health outcomes
We have shown repeatedly in humans and animal models that females are as tractable with statistics as males (actually, often more than). Yet female physiology remains inappropriately understudied. Help us refine algorithms, map changes like pregnancy and menopause, and explore diversity within as well as across traditional sex categories.
Diversity within physiological data
Algorithms tend to be one size fits all, where as people are similar or dissimilar in complex and unmapped ways. Help map differences in normal routines, as well as in illness and recovery trajectories. These might arise from known demographic information, co-morbid conditions (diabetes, pregnancy, etc.), or be represent different patterns in illness associated with unknown or latent variables.
Ko-Han Lee

M.D., National Taiwan University, 2019
Felipe Vasquez Castro

B.S., Chemistry, Universidad de Costa Rica, 2020