Shaoshi Zhang
124 posts
@ZShaoshi
neuroscience, computational models | Computational Brain Imaging Group | Huge fan of Metroidvania and Edward Hopper.
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...
Congratulations to the #ADPD2026 Junior Faculty Award winners! ✨ These talented early-career researchers are making meaningful contributions to Alzheimer’s and Parkinson’s research, bringing fresh ideas and dedication to the field. Join us in Copenhagen to celebrate their achievements and learn more about their work. Here’s to supporting and recognizing the next generation of neuroscientists making a real impact. 🧠🌍 Find out more: adpd.kenes.com/junior-faculty… @MugeAkinci @anlijuncn @BaaylaB @wagnersbrum @AnnaDewenter @NielsOkkels @cathrine_sant @Laura_Sofia_S @georgietos #alzheimers #parkinsons #dementia #endalz
Welcome @ZShaoshi to @Brain_Circuits @MGBResearchNews @BWHNeurology Very excited to work with you!
And applause for our HAI travel scholarships and the AAIC/ISTAART fellowship winners!
🚨 Predicting Alzheimer's Progression 🚨 A thread 🧵 1/ Accurate prediction of Alzheimer’s progression is critical for early intervention. How can we make predictions more precise and generalizable? 🧠✨ 📝 Read the preprint led by @ChenZhang_NUS : doi.org/10.1101/2024.1…
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...
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...
Nature research paper: Longer scans boost prediction and cut costs in brain-wide association studies go.nature.com/46fWRFe
3/11 ... model. Tom's model explains empirical accuracies well across 76 phenotypes from 9 resting-fMRI & task-fMRI datasets (R2 = 0.89), spanning many scanners, acquisitions, racial groups, disorders & ages. Does this mean that we should collect large datasets & short scans?
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...
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...
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...
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...
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...
