stephen zhang

850 posts

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stephen zhang

stephen zhang

@stephenz_y

former ∇(student)

Katılım Şubat 2018
610 Takip Edilen601 Takipçiler
stephen zhang retweetledi
smit
smit@itsoksmit·
vibe coding allows "ideas guys" to bring their ideas to reality, revealing that their idea kinda sucked
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stephen zhang
stephen zhang@stephenz_y·
@omaclaren I thought the general consensus was that PINNs don’t really work…
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Oliver Maclaren
Oliver Maclaren@omaclaren·
I see PINNs and Bayesian model selection etc are all still popular in applied math communities. No shade really coz nice enough as normal science but I can’t see these surviving medium to long term. Tho Bayes should have died a long time ago and hasn’t yet so
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Oliver Maclaren
Oliver Maclaren@omaclaren·
Tweeting assuming no math bio people still on here
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Ulugbek S. Kamilov
Ulugbek S. Kamilov@prof_kamilov·
A PhD is a process of gradually lowering your standards for what counts as a successful day.
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Anshul Kundaje
Anshul Kundaje@anshulkundaje·
Some of the accepted bioML papers are truly egregiously terrible. That's been the case at all the major ML conferences over the last many years. There was even a paper that won some kind of award last year at one of the conferences that was just chok full of fatal flaws.
Matt Dean@mattgdean03

a LOT of slop at icml it seems, we are hiring rn and it’s difficult to determine what’s actually substantial from a progress perspective in bio vs what’s just smth someone did over a weekend

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Vincent Guan
Vincent Guan@guanton_soup·
There are many posters at @icmlconf, and one of them is for our spotlight paper on learning population dynamics (done with @lazar_atan and @k_neklyudov). Come by poster #1515 in Hall A today from 5pm-6:45pm for any questions or complaints
Vincent Guan tweet media
Lazar Atanackovic@lazar_atan

A boid in the hand is better than two in the bush.🐦 Fortunately, @guanton_soup and I have plenty more than one boid at our Wasserstein Lagrangian Mechanics poster @icmlconf. (5pm, Hall A, #1515) If you're interested in population dynamics and OT (🕊️🧬🌊), come flock by!

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SIAM Activity Group on Dynamical Systems
Lecture notes: "A Mathematical Introduction to Diffusion Models" (by Jianfeng Lu): arxiv.org/abs/2607.01693 [note: Lecture notes for the John Tukey Summer Graduate School on Mathematics of Generative Models at SLMath (June 22nd, 2026 -- July 2nd, 2026)]
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Ulugbek S. Kamilov
Ulugbek S. Kamilov@prof_kamilov·
I earned my PhD in Switzerland, worked in the US, and did research in France. My family and friends in Tashkent still introduce me as “studying abroad.” Technically, they’re not wrong—I’m still studying. It’s just that now I get paid for it, and nobody grades me.
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stephen zhang@stephenz_y·
Matrix completion ftw!!!
Yuchen Zeng@yzeng58

💻Tired of running so many slow, expensive benchmark evals across every checkpoint? Try ✨BenchPress✨ at microsoft.github.io/benchpress/: provide a few benchmark scores, then get predictions for the remaining ~100 benchmarks, with trust probabilities and calibrated 90% prediction intervals. How does this work? In his original post (x.com/DimitrisPapail…), @DimitrisPapail first tried the idea as a fun question: collect model-by-benchmark scores into a matrix, find its low-rank structure, and use matrix completion to predict missing benchmark scores from a few observed ones. We expanded this into a full system: a fully audited 84-model x 133-benchmark score matrix, an optimized matrix-completion predictor, and a reliability layer for trust probabilities and 90% prediction intervals. Beyond predicting missing scores, we also suggest practical seed benchmark sets. The five-probe set {GPQA-D, HLE, Codeforces, MMLU-Pro, ARC-AGI-1} recovers the rest of a model's public score profile with a MedAE of 3.93 points. A lower-cost set {GPQA-D, MMLU-Pro, Aider Polyglot, MATH-500, AIME 2026} reaches 4.55 points. See more details below 🧵1/7 This work is with @DimitrisPapail at AI Frontiers, a boutique research lab inside @MSFTResearch.

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Yuchen Zeng
Yuchen Zeng@yzeng58·
💻Tired of running so many slow, expensive benchmark evals across every checkpoint? Try ✨BenchPress✨ at microsoft.github.io/benchpress/: provide a few benchmark scores, then get predictions for the remaining ~100 benchmarks, with trust probabilities and calibrated 90% prediction intervals. How does this work? In his original post (x.com/DimitrisPapail…), @DimitrisPapail first tried the idea as a fun question: collect model-by-benchmark scores into a matrix, find its low-rank structure, and use matrix completion to predict missing benchmark scores from a few observed ones. We expanded this into a full system: a fully audited 84-model x 133-benchmark score matrix, an optimized matrix-completion predictor, and a reliability layer for trust probabilities and 90% prediction intervals. Beyond predicting missing scores, we also suggest practical seed benchmark sets. The five-probe set {GPQA-D, HLE, Codeforces, MMLU-Pro, ARC-AGI-1} recovers the rest of a model's public score profile with a MedAE of 3.93 points. A lower-cost set {GPQA-D, MMLU-Pro, Aider Polyglot, MATH-500, AIME 2026} reaches 4.55 points. See more details below 🧵1/7 This work is with @DimitrisPapail at AI Frontiers, a boutique research lab inside @MSFTResearch.
Dimitris Papailiopoulos@DimitrisPapail

x.com/i/article/2026…

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petros
petros@p_boulieris·
@birdpathy ps/2 my beloved
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Anshul Kundaje
Anshul Kundaje@anshulkundaje·
I am really digging this kind of frank presentation of very impressive results from a startup. More of this please. Budding scientists take note. You can do impressive stuff while highlighting important caveats. It creates more trust.
Gabriele Corso@GabriCorso

Don’t get fooled: these designs are still far from zero-shot therapeutic biologics. BoltzProt-1 does not remove the need for downstream optimization, but it can give you a meaningful head start by reducing the time and cost required to reach strong starting points for your therapeutic program.

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Gabriel Peyré
Gabriel Peyré@gabrielpeyre·
The alpha version of my new book "Optimal Transport for Machine Learners" is out, with in particular an online version with interactive figures gpeyre.com/ot4ml/
Gabriel Peyré tweet media
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Tolga Birdal
Tolga Birdal@tolga_birdal·
Excited to finally introduce Topological Neural Operators (TNOs) [arxiv.org/abs/2606.09806], lifting operator learning to topological domains (cell complexes), where pyhsical quantitites live on their natural supports and interact via the language of Discrete Exterior Calculus. 🧵
Tolga Birdal tweet media
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David Bessis
David Bessis@davidbessis·
PS: I didn't have a Turing award, nor a Fields medal, nor anything approaching that, and my accomplishments at 36 can't compare with Knuth's. I'm using my example to document a much broader story, the collapse of academia as a safe haven for uncompromised long-term innovation.
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David Bessis
David Bessis@davidbessis·
It has become impossible for a smart young person to be that longtermist without having to bullshit either investors or grant committees, and lose their focus and sincerity along the way. When I quit academia at 36, the conflict between my intellectual ambition and my basic material needs was still unresolved.
Louis Gleeson@aigleeson

A computer scientist won the Turing Award at 36 and then walked away from almost every other project for the next 50 years to write one book that he has still not finished at age 88, and it may be the most important book in his field. His name is Donald Knuth. He won the Turing Award in 1974, which is the closest thing computer science has to a Nobel Prize. He was 36 years old. He had already written volumes one, two, and three of a book series called The Art of Computer Programming. He was the youngest person ever to receive the award at that point in its history. Almost anyone else would have ridden that moment for the rest of their career. Founded a company. Sat on boards. Gone on speaking tours. Knuth did the opposite. He went back to his desk and kept writing. He started the book in 1962. He was 24 years old. His publisher had asked him to write a short paperback on compilers. He sat down to outline it and discovered that to explain compilers properly he would have to explain the deeper algorithms underneath them first. The short paperback became a draft outline of 12 chapters. The 12 chapters became a planned 7-volume series. The 7-volume series became the project he is still working on 63 years later. Volume 1 came out in 1968. Volume 2 in 1969. Volume 3 in 1973. He was producing books faster than most academics produce papers. Then everything stopped. In 1977 he received the printed proofs of the second edition of Volume 2. He looked at the pages and was so disgusted by how the publisher had typeset his mathematical notation that he could not bring himself to release the book. The equations looked ugly. The fonts looked wrong. The spacing was off. He decided he could not in good conscience publish another volume of TAOCP until the typesetting problem was solved. So he paused the book. He stopped writing TAOCP and spent the next 8 years inventing TeX from scratch. TeX is the typesetting system that every academic paper, every math textbook, every physics journal on earth now uses. Every PhD thesis in the sciences is set in TeX. Every paper on arxiv. Every equation in every paper Anthropic, OpenAI, and DeepMind have ever published. The system that the entire scientific publishing world runs on exists because one man refused to compromise on how the second edition of Volume 2 looked. He gave the entire TeX system away for free. He never tried to commercialize it. He went back to writing TAOCP. In 1992 he retired from Stanford at the age of 54. Most professors retire to slow down. Knuth retired to speed up. He explicitly said he was leaving teaching because he needed every remaining hour of his life to keep writing the book. He stopped using email on January 1, 1990. He answers no calls. He takes paper mail only. He is on a personal mission to finish a multi-volume series that nobody is forcing him to write, on a deadline that only exists in his own head. Volume 4A came out in 2011. Volume 4B in 2022. He is currently working on Volume 4C. Volumes 4D, 4E, 4F, 5, 6, and 7 are still ahead of him. He is 88 years old. He will almost certainly die before he finishes. The thing that should haunt anyone reading this is the math of his choice. Every modern incentive structure tells you to optimize for speed. Ship the imperfect version. Get it out the door. Iterate later. Move on to the next thing. Knuth has spent 63 years doing the exact opposite. He pays a $2.56 reward in hexadecimal dollars to anyone who finds an error in his published books. Real checks, until check fraud made him switch to certificates of deposit. He treats every single error in every single volume as a personal failure. He revises. He rewrites. He goes back to fix issues that nobody else could have spotted. He could have written 30 books in 63 years. He chose to write one. The reason is the one almost nobody understands the first time they hear it. There is a category of work that loses all its value when it is done quickly. A reference book that engineers will rely on for the next 200 years is not the same kind of object as a blog post that has to ship today. The slow project and the fast project look like the same activity from the outside. They are completely different games. Bill Gates once said in an interview that if you can read the whole of TAOCP, you should send him your resume. He meant it. He was not joking. The man who founded Microsoft was telling the world that the rarest skill on earth is being able to finish a book that one man has spent his entire adult life writing for an audience that mostly does not have the patience to read it. The book may never be finished. The man writing it knows this and keeps writing anyway. The work outlives the worker. That is the entire point.

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Grande
Grande@Sixrande·
Grande tweet media
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Susan Zhang
Susan Zhang@suchenzang·
the people most susceptible to ai/llm psychosis seem to be the ones with the least personally-directed execution results to show for in the last N years of their lives the bigger the mismatch in self-perception-of-greatness vs reality (no matter how grand the reality seems from the outside), the harder the psychosis hits these people generally are at the peak of maslows heirarchy, with no other needs to take care of, aside from maybe an impossible thirst for adoration from the masses when they feel misunderstood-in-their-brilliance-by-peers-and-therefore-have-no-peers, chat is only one who will always be there for them, supporting them and their grand theories every single step of the way
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Ben Davison
Ben Davison@Ben_Davison1·
Fosters tried to open a brewery in China Guzman y Gomez tried to open Mexican fast food in the USA The over confidence of barely capable Australian executives is fuelled by our oligopoly domestic markets & a financial press that is more bosses pamphlet than a voice of reason
Mark Di Stefano@MarkDiStef

🤦🏻‍♂️

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