Manjari Narayan

Surrogate Science , Auditing AIxBio, AI epistemics. Prev
@Dyno_Tx | @StanfordMed | PhD@RiceU_ECE | BS@ECEILLINOIS
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[Manjari Narayan @NeuroStats]

Neurostats 2.0 continues at https://t.co/665TMK6jm3

Surrogate Science is coming soon

[Manjari Narayan @NeuroStats]

@JacobHHilton Are you interested in scientific problems where you can map interpretability of parameters or compositions of parameters to ground truth?

[Manjari Narayan @NeuroStats]

Codecrastinating by bringing old projects to life.

[Amanda Askell @AmandaAskell]

Claude Codecrastination: when you avoid the thing you're supposed to do by cranking out 17 other things you've been wanting to do for a while.

[Manjari Narayan @NeuroStats]

@sebkrier @MLStreetTalk Chirimuuta is great! So nice to see these issues discussed, because "prediction" has been overloaded since Day 1.

[Manjari Narayan @NeuroStats]

Super interesting to see this. I've been testing how this can play out in science and scientific decision making — we really aren't vigilant about how wrong stuff in the scientific literature might create self-fulfilling loops that extend stagnation

[Stella Biderman @BlancheMinerva]

It's so great that there are now multiple orgs doing transparent, rigorous testing of basic premises about how LLMs work and how their behavior can be influenced. So glad that Geodesic exists and excited to work with them on more like this!

[Manjari Narayan @NeuroStats]

I wish more people asked themselves
"What would John Tukey do?"

He sure as hell would have been exciting things, not stuck on old problems. I mean he worked on information retrieval in his retirement in the 90s.

[Manjari Narayan @NeuroStats]

I found myself similarly disoriented. I suspect we haven't found new abstractions that actually make sense for theory to be cool again.

But you have to develop a taste for different research problems.

[Manjari Narayan @NeuroStats]

I found myself similarly disoriented. I suspect we haven't found new abstractions that actually make sense for theory to be cool again.

But you have to develop a taste for different research problems.

[Wenting Zhao @wzhao_nlp]

🌶️ Some (perhaps) spicy thoughts. It’s been a while since my last tweet, but I wanted to write about how disorienting it has been from academia to an LLM lab 😅

The kind of research I was trained to do during my PhD almost doesn’t exist here. The obsession with mathematical elegance and novelty is mostly gone. Everything is about scaling data and compute. For a while, that really got to me. At my lowest point, I felt like I’d lost interest in building LLMs altogether. I didn’t feel intellectually challenged anymore.

What made this even stranger was that, at a technical level, things worked. If there was a capability I wanted to teach a model, scaling the right data and compute always got me there, no exception (so far).

But recently, I found a way to reconcile with myself..

I realized the real competition isn’t in the ML recipe anymore. Most teams do roughly the same thing. What actually matters is how fast you can iterate, test ideas, and recover from mistakes. And that speed is mostly backed by infrastructure 🏗️ Faster loops, fewer bugs, better tooling.

Seeing this made me excited again! Infra is its own deep, hard, and intellectually fun problem space.

In 2026, I want to become an ML researcher who’s really good at infra. And I'll come back to ML problems with that edge, and will be excited to share what I find 😌

[Manjari Narayan @NeuroStats]

@aishdoingthings @pasricha_sehaj @jainhiya_ @rootsofprogress I'd have to do a little digging. But very interested in organizing on these fronts on next trip to India

[Manjari Narayan @NeuroStats]

https://t.co/nXw0wVNqI6

[Manjari Narayan @NeuroStats]

🧵
Personal Hamming Problem #1:
Raise the bar for quantifying risk-benefit tradeoffs in drug development.
By risk-benefit tradeoff I mean instantiation of the therapeutic index at some stage of the pipeline.
#Neurips2025

[Manjari Narayan @NeuroStats]

But modern ML and statistics has a much richer set of solutions I am excited about.

Easier solution: Use generalized probability indices that trade off two outcomes both of which have their own sources of measurement error

Even better: modern multi-multicalibration

More 👇

[Manjari Narayan @NeuroStats]

@ryanayork @GeorgeSandler_ Very cool, would love to chat a bit more about how to test learning the space of causal interactions more deeply!

[Carlos E Alvarez @CarlosEAlvare17]

@NeuroStats 🔥 Exactly! Genetic variation is comparatively clean to interpret, but dynamic biological measurements (transcriptomic, epigenetic, imaging, physiological) are deeply entangled with life-course factors: environment, development, health status, social, & reverse causation...

1/3

[Manjari Narayan @NeuroStats]

One an start with what the mathematics of a proper scoring criterion ought to be for a problem, what kinds of properties a transformation ought to have, etc..
I've seen this problem emerge in many bio/health competitions over 10 years across kaggle, DREAM, etc..

[Mile Sikic @msikic]

@AnthropicAI @Google The recent very welcome @Arcinstute challenge made this painfully clear: defining evaluation metrics is hard. In some cases, trivial data transformations—and even random data—can score astonishingly high.

Great AI performance ≠ biological meaning. 4/6

[Mile Sikic @msikic]

@AnthropicAI @Google The recent very welcome @Arcinstute challenge made this painfully clear: defining evaluation metrics is hard. In some cases, trivial data transformations—and even random data—can score astonishingly high.

Great AI performance ≠ biological meaning. 4/6

[Manjari Narayan @NeuroStats]

OMG yes. Glad to see someone else making this point.

Measurements of dynamic biological processes are subject to more novel kinds of confounding and selection bias than genetic markers. 'omics/imaging in biology ignores these challenges of life-course epidemiology

[Carlos E Alvarez @CarlosEAlvare17]

@anshulkundaje I was just making that point in a 3-tweet thread here. In addition to my closing suggestions there, I would mention the need for life course molecular (omics) epidemiology - high powered.

[Manjari Narayan @NeuroStats]

Being fast has advantages when high quality feedback is really quick. But surely deep thinking / pondering / working from first principles has its place for problems that are either have long-horizon/low quality feedback.

Where is the Jim Simon of pharmaceutical forecasting?

[Andrew Gordon Wilson @andrewgwils]

@ltrd_ You also don’t need to be a gold medalist of the IMO to be a great mathematician. He isn’t drawing a contrast between smart and stupid. It’s a contrast between shallow and deep thinking.

[Andrew Gordon Wilson @andrewgwils]

@ltrd_ You also don’t need to be a gold medalist of the IMO to be a great mathematician. He isn’t drawing a contrast between smart and stupid. It’s a contrast between shallow and deep thinking.

[Manjari Narayan @NeuroStats]

@patrickc I believe @math_rachel made a career switch from (math/AI to immunology) to study stuff like this. https://t.co/oc19dD6mf7