Thomas Serre

@BrownUniversity @CarneyInstitute @ANITI_Toulouse @ELLISforEurope
Computational neuroscientist. Machine and biological vision.

[Jack Langerman (in sf) @jacklangerman]

Last keynote: "Scaling Laws vs. Neural Laws: Toward More Natural Artificial Vision" kicking off now with @tserre

[Thomas Serre @tserre]

Thank you Anand! You are too kind!

[Anand Bhattad @anand_bhattad]

Easily one of the best keynotes I've attended in a long time.

Incredibly thought-provoking and very timely for folks in Computer Vision. Thanks for the great talk! @tserre #CVPR2026

[Thomas Serre @tserre]

@HildeKuehne 🙌 thank you, Hilde. This means a lot to me! And I am sorry we ended up not meeting for coffee after my talk but I guess you will have to come visit at Brown for us to catch up!

[Thomas Serre @tserre]

@Parskatt @juliencolin_ @nuriaoliver @L_Goetschalckx @ELLIS_Alicante @serre_lab @BrownUniversity @ELLISforEurope Except that most vision scientists expect SSL/foundation models to be much closer to the way we learn than image classification… if I had to put my money on a model i would have put it ok dinov3 😉

[Julien Colin @juliencolin_]

Foundation models dominate vision benchmarks. But how interpretable are their internal features to humans?

We ran a large behavioral study across 6 vision transformers, and found that every foundation model tested falls below the supervised baselines that came before. 🧵

[Thomas Serre @tserre]

Apply here: https://t.co/EQNx3KWSdK

More on the Zimmerman Center for Computational Brain Science: https://t.co/N4e3hcjh0t

[Thomas Serre @tserre]

Postdoc opening in APMA @BrownUniversity — specifically looking for candidates bridging APMA + brain science or CS. Review starts April 1! Great fit for anyone interested in collaborating with faculty at the @CarneyInstitute Nancy G. Zimmerman Center for Comput. Brain Science. 🧵

[Trends in Cognitive Sciences @TrendsCognSci]

Better artificial intelligence does not mean better models of biology

Opinion by Drew Linsley (@DrewLinsley), Pinyuan Feng (@Pinyuan3), & Thomas Serre (@tserre)

Free access before Feb 11: https://t.co/qQamT7cE3b

[Thomas Serre @tserre]

Published @TrendsCognSci with @DrewLinsley & @Pinyuan3: As vision models scale to human/superhuman accuracy, they’re becoming worse models of primate vision—benchmark engineering isn’t neuroscience. @CarneyInstitute @BrownCoPsy https://t.co/INZqLygFCS

[Thomas Serre @tserre]

🚨 New preprint alert!
Our latest review/opinion, led by @DrewLinsley, examines how deep neural networks (DNNs) optimized for image categorization align with primate vision, using neural and behavioral benchmarks.

[Corey Welch @CoreyWelch]

A solemn moment here at the Brown University memorial tonight… a little while ago, Kento Suzuki walked up by himself and began playing the Duduk, an ancient Armenian instrument. A family soon joining, overtaken by emotion. @wpri12

[Victor Boutin @VictorBoutin]

New #neurips2025 paper🚀: “Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models” with @LouisBAlgue @du_yilun @tserre @RufinVanrullen

We show how to turn an EBM into a Riemannian metric so that geodesics follow the data manifold.

[Josh McDermott @JoshHMcDermott]

New pre-print from our lab, by Lakshmi Govindarajan @lakshming92 with help from Sagarika Alavilli, introducing a new type of model for studying sensory uncertainty. https://t.co/TMKEDbmbCm
Here is a summary. (1/n)

[Thomas Serre @tserre]

Last call for applications! Join us in advancing AI and the science of mind at Brown. Apply by Nov 8, 2025 👉 https://t.co/15j5mMY3zQ

[Thomas Serre @tserre]

Brown’s Department of Cognitive & Psychological Sciences is hiring a tenure-track Assistant Professor, working in the area of AI and the Mind (start July 1, 2026). Apply by Nov 8, 2025 👉 https://t.co/clod1iz6xu

#AI #CognitiveScience #AcademicJobs #BrownUniversity

[Thomas Serre @tserre]

📚 Full ref: https://t.co/9lOZOjEJIr @serre_lab @BrownCSDept @BrownCLPS @CarneyInstitute

[Thomas Serre @tserre]

🧠 Thrilled to share our NeuroView with @Brown_NLP! Key question: AI foundation models are coming to neuroscience—if scaling laws hold, their predictive power will be unprecedented. But is that enough to understand how the brain works? A thread on prediction vs understanding🧵

[Neuron @NeuroCellPress]

Online now: From prediction to understanding: Will AI foundation models transform brain science? https://t.co/5WUuSsZKKH

[Thomas Serre @tserre]

Personal take: Current XAI tools can't yet discover novel mechanisms—they test hypotheses more than reveal the unexpected. We need better methods NOW, before digital twins become so convincing we stop asking how they work.

[Michael Black @Michael_J_Black]

Many people are in the middle of the @CVPR deadline. So I'm sharing my guide to writing a CVPR paper (or any paper). My students have had this for years but I haven't shared it publicly before. I hope you find it useful and write a great paper. #CVPR2025 https://t.co/RAvnQFnuLQ