I am a NSF Graduate Research Fellow and PhD student at the University of Washington in the department of Computer
Science and Engineering, currently co-advised by Dr. Bingni Brunton and Dr. Rajesh Rao. After completing my Honors
Bachelors of Science in Computer Science at Oregon State University, I came to Seattle to learn more about the
fascinating intersection between Neuroscience and Machine Learning. I first gained interest in the brain, and
intelligence in general, during an undergraduate research experience at the University of Washington with Dr.
Andrea Stocco, focusing on Cognitive Neuroscience. Currently, my research aims to improve generalizability of
Brain Computer Interfaces (BCIs) to new patients by leveraging transfer learning techniques from machine learning
on naturalistic electrocorticography (ECoG) data.
While at OSU I was involved with research in Human Computer Interaction with Dr. Margaret Burnett, specifically
investigating gender biases in user interfaces. My Honors undergraduate thesis investigated "best practices" and
design guidelines for gender inclusive software.
Here are some of my projects, in chronological order from most to least recent.
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.
CONTACT
HAVE QUESTIONS?
University of Washington,
Seattle, US
Email: zsteineh@uw.edu
