Computational cog neuro | PhD candidate @Yale | @NSFGRFP | @Dartmouth ‘20 Find me @ https://xitter.gq/t.co/3kozLc0Irp
Joined October 2020
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Erica Busch retweeted
🚀 We're hiring @VanderbiltU! Multiple open rank positions, College of Connected Computing.
Come join us to build the Computational Mind & Behavior (CMB) research area, where discoveries about minds and behavior inspire new computation—and computation advances human science. 👇
Our new paper is out this week in @NatureNeuro! We built a BCI that works with the brain's natural geometry — and we found that people could learn to play a video game with their brains in <1 hr of training. This efficiency is groundbreaking & here's why: nature.com/articles/s41593-0…
For motor/communication disorders, manifold-aligned interfaces could help extend accessibility. For mental health, incremental manifold-aware approaches may outperform blunter interventions. Broadly: understanding the natural structure of our minds may be key to improving them.
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This project started in 2020 & shaped my PhD, so it's especially exciting to see it out. Massive thanks to my coauthors @g_lajoie_ & @KrishnaswamyLab & everyone who helped collect this massive dataset. news.yale.edu/2026/06/09/bra…
I'm thrilled and humbled to share some major updates! (A 🧵 but TLDR: graduated from Yale, joining Sungkyunkwan University in South Korea as an IBS Young Scientist Fellow this summer, and starting as an assistant professor in Vanderbilt's College of Connected Computing in F2027)
I feel incredibly privileged to have a brilliant and generous set of mentors behind me - from my advisor Nick Turk-Browne @WuTsaiYale to my many mentors:
@KrishnaswamyLab, Arielle Baskin-Sommers, BJ Casey, and @haxbylab. I cannot express enough gratitude to them & so many others
New Perspective out in @NatMentHealth! We consider why neuroimaging analyses struggles to predict adolescent mental health, discuss approaches for improving prediction, and provide open-source implementations and tutorials: nature.com/articles/s44220-0…
Identifying robust biomarkers requires methods that capture the complexity of the brain and behavior. Two approaches we explore:
🔧 Functional alignment - aligns brain function not just anatomy
🔧 Manifold learning - captures nonlinear brain-behavior relationships
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All methods are available as open-source Python tutorials with:
✅ Step-by-step Jupyter notebooks
✅ Simulated datasets
✅ Sample fMRI datasets
✅ Google Colab integration github.com/ericabusch/RepBio…
Excited to share this new work w/ Nick Turk-Browne & Arielle Baskin-Sommers!!
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