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stephen zhang

@stephenz_y

former ∇student at @flatironinst and @nyudatascience
machine learning ∧ complex systems
syz.id.au Joined February 2018
650 Following 659 Followers 947 Posts

Latest posts 5

stephen zhang @stephenz_y
Join us at @FlatironInst CCM! 🤓
Mark Goldstein Verified account @marikgoldstein Nov 5
just 10 days left to apply: Flatiron Research Fellowship in the Center for Computational Mathematics! 2-3 years postdoctoral support for independently-led research at the Simons Foundation Flatiron Institute in NYC- join us!
apply.interfolio.com/193799
simonsfoundation.org/flatiron/
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stephen zhang @stephenz_y
“The fraction of desk rejected papers at TMLR used to be about 6% in 2023 but is now at about 53%”

Insane…
TMLR has faced a deluge of submissions, necessitating stricter desk rejection policies due to limited reviewer capacity

Co-EiC Nihar Shah reached out to authors of 10 papers slated for desk reject. Could they answer questions about their *own* submission?
medium.com/@TmlrOrg/askin…
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stephen zhang @stephenz_y
Super excited to be here in NYC to continue working on all the things I love most (... #flows, #diffusions, #biophysics, oh my!) 😃
Looking forward to interacting with the community at @NYUDataScience and @FlatironInst
Welcome to Stephen Zhang, an incoming CDS Faculty Fellow.

He’s an applied mathematician working on inverse problems, starting from noisy, messy data and working backwards to reconstruct the systems behind it, across #biology and #physics.

nyudatascience.medium.com/meet-the-fello…
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stephen zhang @stephenz_y
Matrix completion ftw!!!
Yuchen Zeng Verified account @yzeng58 Jun 24
💻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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stephen zhang @stephenz_y
Also pleased to have been recognised🥰 but would likewise be interested in transferring the free reg to underrepresented student if possible as I won’t be attending @icmlconf
Qingcheng Zeng Verified account @SteveZeng7 May 13
Just recognized as a gold reviewer at #ICML2026! However, I will not attend ICML this year. If doable, I really would like to transfer this free registration to a student from an underrepresented group. Is that possible? @icmlconf
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