Dmitriy Drusvyatskiy

Prof at @HDSIUCSD. I work in optimization, statistics, machine learning and AI.

[Dmitriy Drusvyatskiy @ddrusvyat]

Sharing here in case someone finds the material useful.

[Dmitriy Drusvyatskiy @ddrusvyat]

This spring I taught a grad course on “All of Convex Quadratic Optimization.”

That may sound narrow, but quadratic problems are simple enough to analyze precisely, yet rich enough to reveal many of the core phenomena that drive modern optimization and learning.

I wrote up the course notes here:
https://t.co/kuLZi4rFRG

[Dmitriy Drusvyatskiy @ddrusvyat]

With basically just linear algebra as background, the notes cover:

gradient descent
Chebyshev acceleration
conjugate gradients / Krylov methods
source conditions and spectral structure
average-case analysis, Marchenko–Pastur, power laws
SGD for least squares
lower bounds
high-dimensional scaling limits of SGD

[Damek @damekdavis]

Spent last month making a tiny harness around codex, which helped me prove a new result on the following question:

How many n×t matrices with ±1 entries have pairwise orthogonal rows?

I learned a lot doing this, which I'll share later. But I want to first to explain the result.

[Edgar Dobriban @EdgarDobriban]

Excited to share our #ICML2026 workshop "AI as a Tool for Mathematics, Computer Science, and Machine Learning" https://t.co/1Po28kOjqO

AI is becoming an indispensable tool in research in math and CS (including in ML). However, due to the "jagged frontier", it is not always clear how to best use AI workflows for each given problem. To address this, our workshop aims to help the community by collecting best practices and workflows for using AI in research.

We have an exciting lineup of speakers, including Sergeu Gukov (Caltech), Remy Degenne (University of Lille · Inria), Damek Davis (@damekdavis, UPenn), Rachel Ward (UT Austin), Mehtaab Sawhney (@mehtaab_sawhney, Columbia University / OpenAI).

We also welcome submissions that highlight workflows using AI for machine learning, math, and computer science research more generally. Your contribution should illustrate—in an accessible way for a non-expert—how a simple workflow has proven to be useful in solving a cognitive research task (e.g., time-saving, energy-saving, result-strengthening, etc.). Deadline: May 13. See our Call for Papers: https://t.co/yLT2VNNBzE.

We aim to collect these workflows, make them available after a workshop, and even organize a challenge where we run the workflows against a test suite of problems, to understand their relaive merits. In this sense, by focusing on general strategies and workflows, our workshop is complementary to other cool related workshops at ICML, such as the AI4math workshop (https://t.co/dHmCwYW3Qi).

I'm glad to be co-organizing this with the amazing @FannyYangETH, Misha Belkin (UCSD), Dmitriy Drusvyatskiy (@ddrusvyat), @SebastienBubeck & Ravi Vakil (Stanford). Also grateful to excellent trainee volunteers Federico Di Gennaro (ETH), Sunay Joshi (UPenn), Tao Wang (UPenn), Qingsong Wang (UCSD).

We are looking for additional volunteers and partners! If you would like to be a partner or sponsor, or contribute by reviewing papers, helping set up the challenge, logistics, advertising, etc., please reach out to us directly or fill out this form: https://t.co/psjPbnL2MM

[Arya Mazumdar @MountainOfMoon]

Very excited about the next @EnCOREInstitut workshop on Replicability and Verifiability. The speaker list is insanely good

https://t.co/2jTTY6vypF

[Daniel Litt @littmath]

See here: https://t.co/M6zNoyGNbE

[Jakub Pachocki @merettm]

Very excited about the "First Proof" challenge. I believe novel frontier research is perhaps the most important way to evaluate capabilities of the next generation of AI models.

We have run our internal model with limited human supervision on the ten proposed problems. The problems require expertise in their respective domains and are not easy to verify; based on feedback from experts, we believe at least six solutions (2, 4, 5, 6, 9, 10) have a high chance of being correct, and some further ones look promising.

We will only publish the solution attempts after midnight (PT), per the authors' guidance - the sha256 hash of the PDF is d74f090af16fc8a19debf4c1fec11c0975be7d612bd5ae43c24ca939cd272b1a .

This was a side-sprint executed in a week mostly by querying one of the models we're currently training; as such, the methodology we employed leaves a lot to be desired. We didn't provide proof ideas or mathematical suggestions to the model during this evaluation; for some solutions, we asked the model to expand upon some proofs, per expert feedback. We also manually facilitated a back-and-forth between this model and ChatGPT for verification, formatting and style. For some problems, we present the best of a few attempts according to human judgement.

We are looking forward to more controlled evaluations in the next round!

https://t.co/jtLCOhJftv #1stProof

[Dmitriy Drusvyatskiy @ddrusvyat]

I'll be in the Boston area next week: Wednesday (Feb 18) visiting @ShamKakade6 at Harvard and on Friday (Feb 20) speaking in the Statistics and DS seminar at MIT. Happy to meet and chat with those in the area. DM to set something up.

[Dmitriy Drusvyatskiy @ddrusvyat]

@normisdeath @damekdavis Thanks!

[Dmitriy Drusvyatskiy @ddrusvyat]

I'm speaking on Wednesday (Feb 11) at ITA 2026 https://t.co/6i7vitgzik in a plenary session on AI Theory together with Ilias Diakonikolas and Jerry Li.

[Dmitriy Drusvyatskiy @ddrusvyat]

@normisdeath @damekdavis Unfortunately it wasn’t videotaped

[Dmitriy Drusvyatskiy @ddrusvyat]

Long overdue, but finally decided to become active on Twitter. Interested in conversations around machine learning/AI and related areas.