Bivas Nag

1st year grad student (DSCB) @ucsf || #firstgen #immigrantscientist 🇮🇳🇩🇪🇺🇲 || Alumni @LMU_Muenchen (Thesis student Pattabiraman lab, Sestan Lab, @YaleCSC)

[Bivas Nag @thinking_cortex]

@YuexingHao @Stanford @MIT @Harvard interested!

[ShorterLab @ShorterLab]

The majority of axonal mitochondria in mammalian neurons lack mitochondrial DNA and do not produce ATP https://t.co/0PYl6YDm7X

[Jorge Bravo Abad @bravo_abad]

No scaling laws for single-cell foundation models: when bigger atlases stop teaching the model anything

In language and vision, the recipe has been simple: more data, bigger models, better performance. Single-cell biology borrowed that playbook. Foundation models for transcriptomics jumped from 1 million cells to atlases of over 100 million, on the assumption that scale would unlock the same gains. Alan DenAdel and coauthors put that assumption to the test, and the result is sobering.

Working from a 22.2-million-cell corpus, they pretrained 400 models across five architectures (from PCA and a variational autoencoder up to the Geneformer transformer) and ran 6,400 evaluation experiments. They varied not just dataset size (1% to 75%) but also diversity, using cell-type re-weighting and geometric sketching to deliberately enrich rare cell types and transcriptional states.

The finding: performance saturates almost immediately. On cell-type classification, batch integration, and perturbation prediction, most models hit their ceiling at roughly 1% of the corpus, about 200,000 cells. Beyond that, adding millions more cells changed essentially nothing. More diversity didn't help. Even spiking in genome-scale Perturb-seq data, to give the models perturbed phenotypes rather than just healthy ones, failed to move the needle. Larger models did score better overall, but they too plateaued early on data.

Two points stood out. Simple baselines (PCA, logistic regression) often matched or beat the transformers. And the strongest model, SCimilarity, won not because of size but because its contrastive training objective is aligned with the downstream task. For single-cell data, what you train on and how you frame the objective matters far more than how much you collect.

This reframes a quiet but expensive habit. In drug discovery, biotech, and any pipeline leaning on cell atlases, the instinct to keep scaling pretraining corpora may be burning compute for no return. The real leverage sits elsewhere: curating high-quality, task-relevant data and matching the training objective to the actual question you're trying to answer.

Paper: DenAdel et al., journal license | https://t.co/X7GxoxF5U5

[Bivas Nag @thinking_cortex]

@nimivashi15 Which place is this ?

[Alex Pollen @brainevodevo]

1/ Our new study, led by @ding5066, examines the role of transcription factors during human neurogenesis to identify gene regulatory networks influencing cell fate, maturation, and subtype specification
https://t.co/GDtKk45kFt

[Bivas Nag @thinking_cortex]

@nimivashi15 I do think for someone who is new it feels overwhelming with so much pace going on

[Bivas Nag @thinking_cortex]

@_onlyscott Ronaldinho may be

[UCSF Neurosurgery @NeurosurgUCSF]

Congratulations to Tomasz Nowakowski, PhD, on being named a finalist for the prestigious Blavatnik National Award for Young Scientists! 🎉 He was selected for his groundbreaking research shaping the future of neuroscience and medicine. @BlavatnikAwards https://t.co/KLOkD0em1P

[Bivas Nag @thinking_cortex]

@VarunSuresh007 @UtdKobi @grok 😂

[UC San Francisco @UCSF]

Not only does UCSF's NIH-funded research advance health care and improve patients' lives, it has an estimated $18.7B ripple effect on the economy. The result is more innovative startups, more jobs, and stronger companies that hire workers nationwide. https://t.co/zi7zp0LFlG

[Bivas Nag @thinking_cortex]

@ScienceYael @NatureNeuro @LehtinenLab Congratulations!!!

[UC San Francisco @UCSF]

What gave human brains the edge over apes? UCSF researchers found that tiny DNA changes helped neurons form more connections, driving complex thinking. But this evolution may also impact neurodevelopment. https://t.co/2hHUievtP5

[Bivas Nag @thinking_cortex]

Excited to join @brainevodevo lab for my #PhD thesis!!! Excited to learn and work in the field of brain evolution and development.

#immigrantscientist #gradschool #firstgen

[Bivas Nag @thinking_cortex]

@precigenetic would love to get in touch to know more about!!!

[Ed Jabbari @Ed_Jabbari]

A fantastic afternoon spent talking to the Arsenal Parkinson’s Walking Football squad. Such an honour to have contributed to our club’s rich history of impactful work in the community! @ParkinsonsUK @WhitHealth @uclh @Arsenal

[bioRxiv Neuroscience @biorxiv_neursci]

Mapping early patterning events in human neural development usingan in-vitro microfluidic stem cell model https://t.co/yTtGX8csLB #biorxiv_neursci

[Bivas Nag @thinking_cortex]

@WallaceUcsf so cool!!!

[Bivas Nag @thinking_cortex]

Really enjoying the class!!! Enjoyed making my first XOR gate✨

[Wallace Marshall @WallaceUcsf]

Starting week 2 of the UCSF Cellular Electronics Minicourse - building logic gates with transistors, in order to better understand how logic can be implemented with proteins and DNA.

[Bivas Nag @thinking_cortex]

@ArlottaLab Congratulations!!!

[ksi @KSI]

Real Madrid even closed the roof tonight. Little do they know, Rice cooks better with the lid on