asst prof at @gatech_scs FODSI Postdoctoral Fellow @MIT PhD from @Berkeley_EECS; Ex: Microsoft Research, Apple Apple AI/ML Research Fellow 2023
Cambridge, MA
Joined February 2020
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Abhishek Shetty retweeted
How the Elizabeth Holmes Doc 'You Can See Everything' Is Deceiving You, by @ProfileRead open.substack.com/pub/thepro… // sharing because I just love the analogy to photography.
Abhishek Shetty retweeted
Grant Sanderson (@3blue1brown) is the most influential mathematician of the 21st century.
Abhishek Shetty retweeted
Language models are getting much, much harder to fully understand with scale, not easier! (Thread)
Hey @nickcammarata, have you interpreted my 432-parameter model yet? Do you think you'll manage it by year end? That's 4 hours per parameter
Abhishek Shetty retweeted
Morgenstern’s lower bound (and Ailon’s) is only for linear algorithms, so that leaves open the possibility that nonlinear algorithms that are numerically stable could beat n log n. Morgenstern says so himself.
OAI doesn’t resolve this, but it’s incorrect to say the problem is settled.
Abhishek Shetty retweeted
If the frontier labs ran their internal models to break cryptography (of the most important kind, like AES, SHA, RSA, ECDL, LWE and Module-LWE) with N tokens for D days and it did *not* work, that would be an extremely useful piece of information to know at this point. To calm the markets, if you will. (On the other hand, if it did work, that's ... interesting ... too, but it's an entirely different story.) Of course, this is all with the understanding that these bits might change in M months.
Abhishek Shetty retweeted
Thoughts: Given the recent advance in math, many "standard" (e.g., number-theoretic / lattice) assumptions are threatened. We need public-key encryption (and other crypto) with *universal* security --- if *any* secure PKE exists, our PKE is secure. (1/2)
Abhishek Shetty retweeted
Such PKEs exist in theory (see ia.cr/2024/1931), but are currently highly impractical. Are there community efforts toward building such PKE schemes in practice? I feel this is urgent so please RT! #cryptography #security
Abhishek Shetty retweeted
2/n Inference serving has been central to making LLMs faster and more efficient. But when compute is scarce, deciding whose request gets served first still largely comes down to a handful of priority tiers --- a pretty coarse and blunt tool that doesn't really let users express how sensitive their requests are to latency. A coding agent waiting for its next response, someone interacting with an LLM in real time, and my long-running overnight research queries certainly don't have the same latency needs!
Abhishek Shetty retweeted
1/n What should the economic layer on top of LLM inference serving platforms look like? In our new paper, Inference Auctions, we show how to bring economic considerations into the design of inference systems, rather than treating resource allocation and systems efficiency as separate problems. This is a joint work with
@keegan_w_harris @sidpra5ad @ashertrockman
and Mike Jordan.
🤖 Made with AI
Abhishek Shetty retweeted
🚨 Thrilled to be co-organizing a Simons Institute semester-long program on diffusions and flows with @joanbruna, Sinho Chewi, @KonstDaskalakis, @ArnaudDoucet1 in Fall '27!
The call for Simons postdoctoral research fellowships for the program is out now, more info below ⬇️ 1/3
Abhishek Shetty retweeted
Come join us at simons. Best place for theorists!
🚨 Thrilled to be co-organizing a Simons Institute semester-long program on diffusions and flows with @joanbruna, Sinho Chewi, @KonstDaskalakis, @ArnaudDoucet1 in Fall '27!
The call for Simons postdoctoral research fellowships for the program is out now, more info below ⬇️ 1/3
Abhishek Shetty retweeted
From my friend and guide for a long time, a timely essay: terrytao.wordpress.com/2026/…
Abhishek Shetty retweeted
why have these questions been studied in "hiding"? till this day, alignment conferences are "secret" and by invitation only.
Abhishek Shetty retweeted
In 1985, Spencer showed that given a₁,…,aₙ ∈ {−1,1}ⁿ, there exist signs x ∈ {−1,1}ⁿ with |⟨aᵢ, x⟩| ≤ 6√n for all i. We improve this to 3√n and construct a lower bound so that |⟨aᵢ, x⟩| > 1.0000002√n for all choices of signs. [1/3]
A lot of the debate now seems to be summarized as being about how seriously should we take the universality of our universal abstractions
Abhishek Shetty retweeted
The Alon-Yuster-Zwick color coding trick can be used to get 2^{O(sqrt(n))} size depth-3 AC circuits for every symmetric Boolean function!! arxiv.org/pdf/2609.34029
Just the latest exhibit that "circuit lower bounds are hard to prove because they are false"
Abhishek Shetty retweeted
The interesting question a decade ago about representing output distributions as next token distributions was computational: maybe this representation would introduce computational difficulties that would make it hard to learn useful behaviors. It turned out it didn't!
Abhishek Shetty retweeted
In this sense you, or any other system that can output text can be written as a "next token predictor". This isn't making any statement about whether brains function similarly to LLMs or not at an internal level, its just a boring syntactic fact about probability distributions.
Abhishek Shetty retweeted
An annoying part of the twitter/bluesky discussion of whether LLMs are "only" "next token predictor"/stochastic parrots is that "next token predictor" is contentless - all mappings from inputs to output strings can be factored into a sequence of "next token" distributions.
Abhishek Shetty retweeted
Nonlinear Komlós conjecture:
If the images of all edges of a cube have Euclidean length at most 1, then the image of some diagonal has ℓ∞-length at most 10.
Instead of digesting AI’s proof of the Komlós conjecture, I spent time understanding what lies behind the problem, and inspired by the Ribe program (with some help from AI) here is a neat nonlinear variant I would like to offer. There is also a Banach space analogue, but let us save some tokens for now.
