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The Question
For decades, neuroscientists have used brain imaging to study how the brain represents experience, how a memory looks across thousands of points in the brain, how a face is recognized, how attention rests when the mind is at rest. In the last few years, AI researchers have started asking strikingly similar questions about language models: what is happening inside these systems, and how does it compare to the brain? The two fields are converging on the same question from opposite ends. Taylor's students sit at that convergence.
What Students Build
Over five weeks, students build working versions of three classic brain imaging analyses using public datasets and Python. They start by training a model to figure out what a person was looking at based purely on patterns of brain activity, reproducing one of the foundational results in the field. They move to studying how the brain organizes itself even when nothing is being asked of it. They finish with the analysis that turns out to be the most direct conceptual bridge to AI alignment research: comparing how similar things look across brain regions, the same way researchers now compare how similar things look across layers of a language model.
The capstone, optional but worth aiming for, is a direct comparison between how a brain represents categories and how a small language model represents the same concepts. Students leave with a working notebook, a written report, and the ability to read papers in this field and know what they're looking at.
The Mentor
Taylor Beck first encountered the tools we now call AI twenty years ago, in a neuroscience lab at Princeton. As a college student, he had accidentally stumbled into a new field: decoding brain activity from fMRI using machine learning tools. In labs from Princeton to Kyoto and Washington University in St. Louis, Taylor studied memory, sleep, and aging, charting how the brain maps experiences, images, and dreams. As a writer, teacher, and public speaker, he writes and talks often about mental disorders too, genetics, sleep, and motivation, all features absent from AI.
He is skeptical of what AI claims to be, an intelligence that thinks, but productively so. This summer Taylor's students will work with real fMRI datasets to ask an urgent question in the field today: how much is a human mind like a language model? What does the answer tell us about both?
Who This Is For
Python proficiency is required, comfort with libraries, plotting, and basic data analysis. Students should have a working understanding of how a model can be trained to make predictions and tested for accuracy. No prior neuroscience background is required. Taylor will teach what students need. The right student here gets genuinely curious about what the data shows, notices an unexpected pattern and wants to understand it before moving on. Students looking for clean answers will struggle. This is exploratory work, and the most interesting findings often look like noise at first.
Logistics
Five weeks. July 6 to August 7, 2026. Mondays, Wednesdays, and Fridays, 1:00 PM to 2:15 PM ET. Friday sessions run 1:00 PM to 3:00 PM ET to accommodate Demo Day. Cohorts of 3 to 4 students per mentor. $4,500. Apply by May 25, 2026 at 11:59 PM.
Project Labs require a minimum of two students to run. If your student is the only applicant in a given lab, we will reach out before the program begins with three options: upgrading to a 1-on-1 mentorship, transferring to another active Project Lab, or a full refund.
Beyond the live sessions, students work on their own, and they are not alone when they do. The lab is supported by a 24/7 Slack channel and a team of scholars and practitioners at the Academy. Students also work alongside SeqHub's AI co-teacher, which helps them think through problems on off days without doing the work for them. Plan for 10 to 12 hours per week, with 4.5 hours in live sessions and the rest on independent work.