Nhung Hoang

pronouns: she/her/hers

PhD Candidate in Computer Science

Rubinov Lab, Vanderbilt University

nhung.hoang@vanderbilt.edu


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About

Hello, my name is Nhung ("ny-oom") 😊 I enjoy working on interdisciplinary research questions. I am currently pursuing a PhD in computer science under the guidance of Dr. Mika Rubinov at Vanderbilt University. My research focuses on the genetic underpinnings of neuroimaging and clinical traits linked to human brain organization. At the moment, I am developing computational methods for integrating biobank-scale neural and genetic data from varying modalities.

I earned my B.A. in computer science from Swarthmore College in 2019. I am a proud first-generation college graduate, and one of my lifelong goals is to work with educators on expanding access to both STEM education and higher education. In addition to research, I love playing ultimate frisbee and visiting local coffee shops!

Brain Genomics

The goal of this research is to understand gene regulation of brain structure and activity, ultimately to identify genes that may play a role in human brain disorders. We develop and employ computational methods that leverage existing neural and genetic data from human populations.

News: Our paper has been accepted to PLOS Biology!
News: I earned a Trainee Highlight Award from the NIH BRAIN Initiative for this work and got to present it at a nanosymposium during the 2023 SfN Conference!

PRAGMA

PRAGMA is an interactive visual analytics tool for deriving individualized brain parcellations from population-based atlases, as a means for analyzing individual variability in functional brain organization.

News: PRAGMA has received Honorable Mention for Best Short Paper at the IEEE VIS 2020 conference!

Natural Selection Inference

We proposed a Hidden Markov Model to Convolutional Neural Network method that predicts regions of natural selection in a population of genetic sequences.

This work was motivated by the historical lack of representation and diversity in human genetics studies. Our hope was to develop a data-driven approach that does not rely on prior domain knowledge, such that it can be applied to data from understudied human populations.