As a dedicated Research Scientist at OpenBCI, I am deeply immersed in exploring the frontier of
neural engineering, computational neuroscience, cognitive science, and human-computer interaction.
My work revolves around advancing brain-computer interfaces (BCIs) to create more intuitive
and accessible technologies for everyone.
I earned my PhD in Computer Science from the University of Washington, where I had the privilege
of working under the guidance of Dr. Rajesh Rao and Dr. Bing Brunton. During my PhD, I was honored
to be an NSF Graduate Research Fellow, which provided invaluable support and resources for my
research. My dissertation, titled
“BCIs for Everyone, Everyday: Generalized Machine Learning Models for Decoding Human Brain Data,”
primarily focused on developing sophisticated machine learning models
to interpret naturalistic ECoG data. This work is a testament to my passion for making
cutting-edge BCI technology more universally applicable.
I also spent six months interning at Meta during my PhD, an experience
that fueled my enthusiasm for blending research with product development,
particularly in creating customer-facing solutions that leverage advanced technologies.
Before my PhD, I worked as an undergraduate research assistant with Dr. Margaret Burnett,
focusing on HCI research related to gender biases in software. This work culminated in my honors
undergraduate thesis on "best practices" and design guidelines for gender-inclusive software.
This experience ignited my fascination with the interplay between technology and human behavior,
as well as the research process itself. During my undergraduate studies, I also participated in
a summer Research Experiences for Undergraduates (REU) program with Dr. Andrea Stocco,
where I explored different cognitive models using fMRI data. This experience sparked my passion
for neural engineering and the brain, ultimately leading me to pursue my PhD work
and my current research position at OpenBCI.
At OpenBCI, I am committed to pushing the boundaries of what is possible with neurotechnology,
striving to develop innovations that enhance human-computer interaction and elevate BCIs
to new heights. Let's connect and explore how we can advance the future of neurotechnology together!
Here are some of my projects, in chronological order from most to least recent.
Dissertation - BCIs for Everyone, Everyday: Generalized Machine Learning Models for Decoding Human Brain Data:
My final PhD dissertation containing 8 chapters covering research ranging from deep learning
models that better generalize across participants to uncovering gender biases that
exist in popular BCI tasks. You can get a copy of the PDF here.
COSYNE 2023 Poster:
To better understand neural activity during naturalistic movements, we explored neural manifolds
of ECOG data from 12 patients with epilepsy. I presented this work at COSYNE 2023, and you can
check out the poster here
and hear a brief summary from me
here.
Brain Bytes Podcast:
In collaboration with Maneeshika Madduri and
NeuroTEC,
we created a new podcast highlighting the
computational neuroscience and neuroengineering research going on at the University of Washington, and also
demystifying the path to graduate school.
You can listen to the podcast here.
Exploring Brain Activity During Movement:
For CSE 512 - Data Visualization, I created a notebook to explore how brain activity from
electrocorticography (ECoG)
signals varies during different movement behaviors.
Through this exploration, I found that brain signals during hand movements with upwards trajectories were
the cleanest (see figure on right).
You can explore the whole
jupyter notebook here.
Transfer Learning with ECoG:
My first project as a graduate student improved brain decoders for ECoG signals through the use of transfer
learning.
We found that models pretrained on a pool of subjects and then fine-tuned to the target subject had
comparable performance to models trained on all the target subject's data.
Slides on our findings can be seen here,
and links to the paper
and github
repo
NSF GRPF Winning Application:
During my first year of grad school (2019-2020), I won a competitive National Science Foundation fellowship.
If you are in the process of applying, or are just curious about what the essay materials are like, feel
free to check out my winning essays. Personal Statement and Research Statement
Deep -Um- Discourse:
I was the team lead for my senior capstone project which developed a machine learning tool to detect filler
words in speech.
Using Mozilla Deepspeech, we were able to adapt the network to a small dataset we collected containing
filler words in speech.
Common Model of Cognition:
I spent a summer with Andrea Stocco at the University of Washington investigating the efficacy of the Common
Model of Cognition (CMC) for human brain data.
Using publicly available fMRI data from the Human Connectome Project, I was able to determine that the CMC
most accurately explains brain activity over other possible models.
See my poster from the summer and my
5 minute talk for more details.
GenderMag:
I investigated possible design solutions for two cognitive styles (information processing and learning),
which was the focus of my undergrad honors thesis.
The video here shows these two styles in action, and how they influence the way users interact with
software.
Visit the GenderMag website for more details.
