I aim pay forward all of their support, optimism, & humor to my future mentees. Which brings me to: my lab at Vanderbilt CCC will be recruiting folks of all levels for Fall 2027! If you'll be at OHBM, come to my talk on 6/17 to get a sense of what we'll be up to — or email me🧠🥳
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
All methods are available as open-source Python tutorials with:
✅ Step-by-step Jupyter notebooks
✅ Simulated datasets
✅ Sample fMRI datasets
✅ Google Colab integration https://t.co/bEFwtYKpIP
Excited to share this new work w/ Nick Turk-Browne & Arielle Baskin-Sommers!!
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: https://t.co/SJhtyHeNUn
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
New preprint! Thrilled to share my latest work with @esfinn -- "Sensory context as a universal principle of language in humans and LLMs"
https://t.co/iL1Op3B4dQ
Today in @Nature, we report human sensory #assembloids—the most complex 3D assembloids to date—comprising four integrated parts to recapitulate aspects of the spinothalamic pathway that processes pain stimuli. We use this model to investigate cellular/early circuit level features of genetic pain syndromes. More details to follow.
Work pioneered in the lab by the brilliant Ji-il Kim and Kent Imaizumi, and in close collaboration with @GregScherrer25!
Link below 👇
This is just the TL;DR of these super cool results — highly suggest perusing the paper for more! & take a peek at the supplement for some fun tidbits from participants’ reports on their experience of the experiment 8/8
That is, if we knew how a participants’ brain activity is naturally structured, we could figure out types of activity that would be easier or harder for them to learn to control. 4/8
Yet, during the session where the perturbation went “outside-manifold”, participants couldn’t learn (within the same 1 hour session of training). Together — we think these results show that manifold geometry is a critical ingredient for more learnable human neurotech! 7/8
Our idea? If we could characterize the geometric structure, or ‘intrinsic manifold’, of the brain activity we were training people to control, we could leverage it to accelerate learning. 3/8
We aimed to tackle a significant challenge for human neurofeedback and brain-computer interfaces: it’s *really* hard to learn to control your own brain! Humans generally show variable BCI learning, really long training times, and high rates of non-response in these tasks. 2/8
New preprint! Excited to share our latest work “Accelerated learning of a noninvasive human brain-computer interface via manifold geometry” ft. outstanding former UG Chandra Fincke,
@g_lajoie_ , @krishnaswamylab, & @wutsaiyale's Nick Turk-Browne 1/8 https://t.co/mZ70ocebRb