This is my third year as a PhD student at UC Berkeley, advised by
The PhD journey is made possible with support from wonderful
people at Berkeley and beyond, as well as from the
Berkeley Graduate Fellowship, the
LLNL Secure Biosystems Design SFA, and the Microsoft
Research PhD Fellowship.
My research contributes to the machine learning foundation for
the design, engineering, and interpretation of proteins. More
recently, I have been focusing on immunology and immune repertoire
One topic I'm curious about is the intersection between immunology
and women's health. For most autoimmune diseases, there is a
higher prevalence amongst women. Many autoimmune disorders tend
to affect women during periods of extensive stress, such as
pregnancy, or during a great hormonal change.
Additionally, I'm also curious about the immune system's role in
the healthy gut and in digestive disorders.
Prior work experience as machine learning engineer at Google
Brain and Google Health (2018-2019) helped shape my
interests in human health.
Deep gratitude also goes towards
Chris Umans and
for their kind and inspiring mentorship during
my time at Caltech (BS 2018).
In 2021, I had a fun internship with
Lerer and the Protein team (led by
Tom Sercu) at Facebook AI Research.
Google Scholar /
Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer*, Alexander Rives* (Equal contribution*)
ICML (long oral presentation), 2022
| colab notebook
Inverse folding aims to designs sequences to fold into desired structure. We
augment training data by nearly three orders of magnitude by predicting
structures for 12M protein sequences using AlphaFold2. Trained with this
additional data, our new inverse folding model more accurately designs sequences
to fold into desired structure, while also generalizing to a variety of more
complex tasks including design of protein complexes, partially masked
structures, binding interfaces, and multiple states.
Learning protein fitness models from evolutionary and assay-labeled data
Chloe Hsu, Hunter Nisonoff, Clara Fannjiang, and Jennifer Listgarten
Nature Biotechnology, 2022
A simple combination approach to protein fitness prediction, learning from both (unlabeled) evolutionarily related protein sequences and variant protein sequences with experimentally measured labels.
Also an analysis that highlights the importance of systematic evaluations and sufficient baselines.