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Shaoshi Zhang

@ZShaoshi

neuroscience, computational psychiatry | Computational Brain Imaging Group | Center for Brain Circuit Therapeutics | Huge fan of Edward Hopper
Singapore Joined May 2020
176 Following 219 Followers 188 Posts

Latest posts 5

Shaoshi Zhang @ZShaoshi
For years, we've known that running a standard t-test on cross-validation folds violates sample independence. We wanted to see how widespread this issue actually is.

The result? 97% of the studies used an invalid statistical test. 🧵👇
Thomas Yeo @bttyeo May 21
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests.

Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
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Shaoshi Zhang @ZShaoshi
Thank you @shansiddiqi and @foxmdphd for the kind invitation to share one of the projects I’m most proud of. This work would not have been possible without my amazing co–first authors @Leon_Oo1 and @csabaorban ! Super grateful for their tremendous contributions!
Michael Fox @foxmdphd Mar 3
Welcome @ZShaoshi to @Brain_Circuits @MGBResearchNews @BWHNeurology Very excited to work with you!
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Shaoshi Zhang @ZShaoshi
Thrilled to share our latest work just published in @Nature where we looked into the optimal fMRI scan time for brain-wide association studies (BWAS)! Full thread below 👇
1/11 Excited to share our @Naturestudy led by @Leon_Oo1 @csabaorban @ZShaoshi

doi.org/10.1038/s41586…

It is well-known that AI performance scales with logarithm of sample size (Kaplan, McCandlish 2020), but in many domains, sample size can be # participants or # measurements...
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Shaoshi Zhang @ZShaoshi
Check out our latest preprint led by the amazing @tianchuzeng and @t___fang where we speed up the tedious parameter optimization process for biophysical modelling🔥👇
While the world burns, we cook up a new preprint! doi.org/10.1101/2025.0…

Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike deep neural nets), but the dirty secret ... 1/N
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