About:
I’m a PhD Candidate in
Biomedical Informatics at Stanford University
and on a leave of absence from the MD program at the University of New Mexico
School of Medicine. I completed an interdisciplinary BA in Mathematics,
Statistics, and Physical, Natural & Social Sciences with minors in Navajo
Language & Linguistics and Chicana & Chicano Studies in 2015. I was a
Visiting Student
in the Dept. of Economics at Harvard 2015-2017. In 2018, I was a
fellow
at the Washington University School of Medicine Mallinckrodt Institute of
Radiology.
Research:
Variation in human sex physiology is essential to consider in all biomedical
research, but there are no variables or methods to construct variables that
faithfully represent sex physiology. Most researchers use assigned sex at
birth (ASAB), which is a binary, legal record of the gross morphology of a
neonate’s genitalia and it is assigned despite any
differences of sexual development (DSD)
that alter morphology or physiology. ASAB
does not capture inherent
variation in the gene regulatory networks, acquired pathology, environmental
exposures, and medical interventions that alter sex physiology over the course
of trans*, cisgender, and people with DSD’s lives alike.
I study machine learning methods to construct a representation of human sex
physiology that can be used in place of ASAB in clinical informatics research.
This variable is constructed using only objective, observable, routine
laboratory tests and biomarkers present in existing electronic health records.
In particular, I research auto-encoder algorithms in multi-task settings,
probabilistic graph models, variational inference, and meta-learning networks.
Previously, I worked on
CV methods
to isolate and featurize autofluorescent signals in megapixel resolution,
multiplexed immunofluorescence experiments, multi-class segmentation of
intercranial tumors, characterization of provider-patient emails with
NLP methods
, and disparity in provision of health services and insurance.
Publications: