Samantha H. Sun

Hello! I am a Bioengineering PhD candidate at the University of Washington. My research bridges between experimental and computational neuroscience: I design and run experiments to learn about the brain, and build computational models from that data to explain and learn about neural mechanisms. If interested in learning more about the specific projects I’m working on, please check out the “projects” section.

One of the things I hope to accomplish in my career is to improve trust and communication in science. I’m a firm believer that science should be fun and accessible for everyone. Communicating science in both an informative and engaging way is essential to rebuild trust and equip the public with critical thinking skills needed for science, policy, and beyond. I’m currently involved in scientific outreach and am a content adviser with the Pacific Science Center.

In my free time, I run a small business as a ceramicist, and I enjoy spending time in the outdoors- in 2021 I reached my goal of hiking 100 miles and I’ve through-hiked the Enchantments twice. I’m also a photographer, and I have a cat named Anya.

news

Mar 17, 2023	Happy Brain Awareness Week! Read more about my classroom visit here!
Oct 13, 2022	Attended the IEEE SMC conference in Prague and presented my paper, “Human intracortical responses to varying electrical stimulation conditions are separable in low-dimensional subspaces.” I was awarded Best Student Paper from IEEE Brain and was a finalist for Best Paper from IEEE SMC! Looking forward to attending their conference next year in Maui 🎉🎉🎉
Jun 7, 2022	Relocated to SoCal for my 6-month internship at Boston Scientific ☀️
May 20, 2022	Completed my General Exam and am now a PhD Candidate 👩🏻‍🏫
Feb 9, 2022	I’ve accepted an on-site internship position at Boston Scientific in their Neuromodulation Research team 🧠⚡️

selected publications

IEEE

Human intracortical responses to varying electrical stimulation conditions are separable in low-dimensional subspaces

Sun, Samantha, Levinson, Lila H., Paschall, Courtnie J., Herron, Jeffrey, Weaver, Kurt, Hauptman, Jason, Ko, Andrew, Ojemann, Jeffrey, and Rao, Rajesh P. N.

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2022

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IEEE

Unsupervised Sleep and Wake State Identification in Long-Term Electrocorticography Recordings

Sun, Samantha, Jiang, Linxing Preston, Peterson, Steven M, Herron, Jeffrey, Weaver, Kurt, Ko, Andrew, Ojemann, Jeffrey, and Rao, Rajesh PN

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2020

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Studying the neural correlates of sleep can lead to revelations in our understanding of sleep and its interplay with different neurological disorders. Sleep research relies on manual annotation of sleep stages based on rules developed for healthy adults. Automating sleep stage annotation can expedite sleep research and enable us to better understand atypical sleep patterns. Our goal was to create a fully unsupervised approach to label sleep and wake states in human electro-corticography (ECoG) data from epilepsy patients. Here, we demonstrate that with continuous data from a single ECoG electrode, hidden semi-Markov models (HSMM) perform best in classifying sleep/wake states without excessive transitions, with a mean accuracy (n=4) of 85.2% compared to using K-means clustering (72.2%) and hidden Markov models (81.5%). Our results confirm that HSMMs produce meaningful labels for ECoG data and establish the groundwork to apply this model to cluster sleep stages and potentially other behavioral states.