About Me

I am a Junior Fellow of the Harvard Society of Fellows, an affiliate of the Harvard Center for Brain Science, and a recipient of a 2024 NIH Director’s Early Independence Award. My research interests lie in some neighborhood of the intersection between theoretical neuroscience, statistical physics, and machine learning. For more information about me and my work, see some of my recent publications and lecture notes, or my CV.

News from the past year

• [January 2026] Our note on disordered kinetic Ising models as a toy model for sequence data with tunable correlations has now been published in JPA.

• [December 2025] I’m headed to NeurIPS! If you’re in San Diego, check out our UniReps workshop paper with Farhad Pashakhanloo on how data symmetries affect representational similarity analysis, our UniReps perspective based on our long-form piece on summary statistics of learning, and our NeurReps proceedings paper on how training shapes the Riemannian geometry of deep network representations.

• [September 2025] I’m headed to the ROC(K)IN’ AI 2.0 workshop, where I’ll speak about our work on generalized cross-validation in high-dimensional ridge regression with correlated samples.

• [August 2025] Our perspective on summary statistics of learning—and what lessons the statistical physics of learning might have for the analysis of neural data—has just been published.

• [August 2025] I’m speaking at the 2025 Brains, Minds, and Machines Summer Course at the Woods Hole MBL.

• [May 2025] Our paper with Alex Atanasov on high-dimensional ridge regression with correlated training data was accepted to ICML 2025. M Ganesh Kumar’s paper on reorganization of place field representations was also accepted.

• [March 2025] Five posters on our work were presented at Cosyne 2025!

See older news here.

I can also (rarely) be found on Bluesky or Mastodon. In official contexts I use my full name, so you’ll find me listed as Jacob Andreas Zavatone-Veth.