Principal Architect, @Microsoft | ex researcher at MSR, CMU PhD | AI for Kernel Optimization, MAIA 200

Kenmore, WA
Joined July 2011
1️⃣We are excited to open-source syftr: a powerful tool for automatically finding Pareto-optimal generative AI flows! syftr searches a large search space of agentic and non-agentic flows to surface optimal tradeoffs between accuracy, cost and latency. 🧵
1
7
3
41
7,275
My salute to the Indian pilot Captain Smit Machchhar for his extraordinary bravery. Despite being stabbed and seriously injured, he fought back, resisted, opened the cockpit door, and enabled passengers and crew to overpower the attacker — preventing a catastrophic mid-air disaster. He saved the lives of 174 people, including Israeli citizens and other nationals. I wish Captain Machchhar a speedy and full recovery. He is a true hero.
8,031
79,815
3,432
410,658
13,993,258
Debadeepta Dey retweeted
🥳 Excited to share that @lossfunk got 4 papers into NeurIPS main conference. Please join me in congratulating the authors! Aman @arcaman07, ⁠Sushrut @martisamuser, ⁠Abhinav @MajorTimbWlf21, Pranav - @Avg_sapient First authors are all undergrads :)
59
70
10
1,484
74,960
Debadeepta Dey retweeted
I would like to clarify a few things: 1) The screenshot is my reaching out to Levent to coordinate our releases. I hope it’s clear from the message that we came in with the best possible intentions. 2) I never ever asked for Levent to be removed from authorship of his own work (as indicated by my text). I was surprised to learn during the call with Tristan that they had only solved Euler and not Navier-Stokes; after learning this we brainstormed possible paths forward. One option we discussed was that Tristan could be the lead author on a rewrite of OpenAI’s Navier-Stokes proof. It is in that context that I said “it would be simpler if Levent was not an Anthropic employee” because I felt it would be inappropriate for an Anthropic employee to author OpenAI’s work. Importantly it was admitted that internal Anthropic models had been used in their proof of Euler blowup; I therefore felt I could not consider Levent to be an independent academic. Another option I wanted to propose (but got cut short) is to offer access to our internal model so that they could try to finish their proof and bridge the gap between Euler and NS. Again I did not know how to navigate giving access to internal OpenAI IP to an Anthropic employee. 3) To reiterate it plainly: as my text clearly indicates, and as I said during our call, OpenAI's intention was to do everything possible to celebrate their mathematical achievements and the heroic efforts that they made on Euler. In the call I was immediately met with a litany of slander, including direct threats that if we were to announce Navier-Stokes he would immediately go to the press with a barrage of unfounded accusations. I refuted all these accusations but he replied “there is nothing you can do, I simply do not trust you”. I was confused why one would turn an incredible source for celebration (of their achievements!) into such bickering, which is when I said that I did not understand why one would risk their career [over unfounded accusations]. Genuinely, at that moment, I was trying to care for him and do a last ditch attempt to get a chance to give them all the credits that they deserve. I deeply apologize for this extremely poor choice of words, it is the opposite of what I was trying to convey. (I should say that I retracted them on the spot by the way.) 4) Overall, on a personal level, it was incredibly difficult to have these conversations. Levent refused to attend any of the meetings despite my repeated asking. As Sholto Douglas said, there will need to be coordination between Anthropic and OpenAI in the future; I felt I was doing a proxy negotiation with Anthropic while the Anthropic employee refused to directly participate.
459
491
311
5,688
3,873,191
Debadeepta Dey retweeted
Many startup employees do not recognize the sheer litany of ways that a founder can screw you over without you even knowing. Founder trust is one of the most important things to look at when you’re joining a startup. From cutting you out of M&A, screwing your retention pool, overdiluting your equity, firing you before your cliff, not having attractive options exercise plans, poor 409a price management, not telling you about QSBS / early exercise, blocking you from participating in secondary, obscuring company performance and many more. Many many decisions that are made in rooms you are not in as an employee where the only thing that matters is: “does the founder have your back?” Great startups with untrustworthy founders lead to poor outcomes and often good startups with trustworthy founders lead to great outcomes. Pick wisely.
114
199
55
3,170
507,411
The key part is the semantic equivalence checking of AI-generated kernel and the equivalent numpy/pytorch.
"openai engineer explains /why/ he didn't need to understand the kernel line by line" 🙃 we're doing compilers 2.0
1
2
900
Debadeepta Dey retweeted
We’ve seen an explosion of new ML chips with unique architectural features, but software support remains the critical bottleneck Achieving peak performance increasingly relies on hardware-specific optimizations in the kernels, but we observe that coding agents are particularly weak at this Introducing Hawkeye, a framework that brings hardware-awareness to coding agents by grounding them in a minimal and comprehensive taxonomy of optimization strategies For new GPU or ML accelerator architectures, you only need to write 10 unit tests and solution kernels (one per optimization strategy), and we show that coding agents can effectively scale test-time compute with this minimal supervision to write hardware-aware kernels Hawkeye can port kernels across architectures (Ampere, Hopper, Blackwell), vendors (NVIDIA, AMD), and precisions (FP8, NVFP4, MXFP4) while consistently leveraging hardware features and approaching expert kernel performance Work co-led with @keramakr and done in collaboration with Alexander Ingare @simonguozirui @18jeffreyma @ZishenW @simran_s_arora @Azaliamirh @profvjreddi
19
55
10
339
54,994
MAI-Image-2.5 is now #1 on the Artificial Analysis leaderboard for image editing! Amazing hillclimbing... compounding the gains!
Microsoft's MAI-Image-2.5-Pro debuts at #1 on the Artificial Analysis Image Editing Leaderboard, and takes the #7 spot in Text to Image MAI-Image-2.5-Pro is Microsoft AI's quality-focused image model, launched July 23 in preview on Microsoft Foundry. It joins MAI-Image-2.5 and MAI-Image-2.5-Flash in a family Microsoft positions as covering the quality-speed-cost curve, so builders can pick the point that fits their job. Pro sits at the quality end: Microsoft describes it as its highest-fidelity image model to date, aimed at hero imagery, detailed editing, and precise in-image text rendering. In the Artificial Analysis Image Arena, MAI-Image-2.5-Pro debuts at #1 on the Image Editing Leaderboard, surpassing Reve 2.1, GPT Image 2, and Microsoft's own MAI-Image-2.5. In Text to Image it lands at #7. On Microsoft Foundry, MAI-Image-2.5-Pro is priced per token: $5 per 1M text input tokens, $8 per 1M image input tokens, and $106 per 1M image output tokens, which works out to roughly $108.5 per 1k 1024x1024 images. That compares to $48 per 1k images for MAI-Image-2.5 and $20 per 1k for MAI-Image-2.5-Flash. MAI-Image-2.5-Pro is available in preview on Microsoft Foundry across seven global-standard regions, and can be tried out in the MAI Playground. Congratulations to @MicrosoftAI on the release! See below for comparisons between MAI-Image-2.5-Pro and other leading models in the Artificial Analysis Image Arena 🧵
26
38
4
340
57,083
Debadeepta Dey retweeted
TLDR: we integrate many computational and statistical improvements for muon-style updates here: github.com/microsoft/dion . Comp: Gram Newton Schulz, Row Selection, Megabatching, Cudagraph Capture, Symmetric Kernels Stat: Joint learning rate+fraction, row norm, fused rounding
Newton-Schulz is what makes Muon work, but it's also a massive pain compared to Adam. *Dion3* makes it ~6x faster and more parallelizable too. Combines our work on row-filtered gradients, Gram-NS alg, and GPU kernels in one package. Try pip install dion! arxiv.org/abs/2608.11612
1
25
152
11,991
Debadeepta Dey retweeted
Grok 4.6 was the first model trained on internal model-development tasks. My team built the training and evaluation stack that enabled Grok to learn from work that accelerates model development itself, including production inference and kernel optimization. Grok 4.6 now leads our internal MTS Eval and InferenceEval. In one experiment, we gave an earlier checkpoint our production inference codebase in a fully automated environment. It explored 297 optimization ideas and shipped 3 changes on top of an already human-optimized stack, improving prefill throughput by 3.1% and decode throughput by 1.5%. Our work is featured in the R&D Enablement section of the Grok 4.6 model card.
66
107
32
1,945
331,280
Our latest code model is 25% more efficient, higher quality, and a quarter of the cost than our model launched in June. Now live in GitHub Copilot - try it out.
26
57
7
438
44,721
Debadeepta Dey retweeted
This is a fun project: Modifying transformer training to support recursive state turns out to be both easy and desirable by all measures.
1/ Sharing a new, interesting project I did during my internship at Microsoft AI Frontiers w/ @JohnCLangford TL;DR: At decoding time, we feed **previous hidden state** into the input together with token embedding, and it boosts performance for free. arxiv.org/abs/2608.08888
1
7
1
78
11,842
Debadeepta Dey retweeted
Nextlat is lots of fun. This new version shows more scale, bootstrapping state tracking, and strong speculative execution.
Next-token prediction is myopic. What if transformers learn to predict their own next latent state? 🌠 We present 𝗡𝗲𝘅𝘁-𝗟𝗮𝘁𝗲𝗻𝘁 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 (𝗡𝗲𝘅𝘁𝗟𝗮𝘁): a self-supervised learning method that teaches transformers to form compact world models for reasoning and planning. It also unlocks up to 3.3x faster inference via self-speculative decoding! 🚀
1
4
24
3,064
Debadeepta Dey retweeted
we published autokernel on arxiv inspired by @karpathy 's autoresearch, we applied the same keep/revert agent loop to GPU kernel optimization you give it any pytorch model, it profiles it, ranks bottlenecks by amdahl's law, writes triton or CUDA C++ replacements, and runs 300+ experiments overnight with no human in the loop - 5.29x over pytorch eager on rmsnorm - 2.82x on softmax - beats torch.compile by 3.44x on softmax and 2.94x on cross entropy - #1 on the vectorsum_v2 B200 leaderboard - single prompt triton FP4 matmul that beats CUTLASS by up to 2.15x every candidate passes a 5-stage correctness harness before any speedup counts, and the whole thing runs at ~40 experiments/hour so you wake up to a faster model arxiv: arxiv.org/abs/2603.21331 github: github.com/RightNow-AI/autok…
18
76
9
652
89,291
Debadeepta Dey retweeted
Introducing Critique, a new multi-model deep research system in M365 Copilot. You can use multiple models together to generate optimal responses and reports.
411
497
179
4,106
1,394,165
Vibe-coding wars.
Breaking News: The U.S. was responsible for a missile strike on an Iranian school, an ongoing military investigation found. The inquiry said the strike — which Iranian officials said killed at least 175 people — was the result of a targeting mistake. nyti.ms/47G2uw2
1
145
AI is writing a growing share of the world's software. No one is formally verifying any of it. New essay: "When AI Writes the World's Software, Who Verifies It?" leodemoura.github.io/blog/20…
41
239
44
1,576
424,765
Debadeepta Dey retweeted
RL on LLMs inefficiently uses one scalar per rollout. But users regularly give much richer feedback: "make it formal," "step 3 is wrong." Can we train LLMs on this human-AI interaction? We introduce RL from Text Feedback, with 1) Self-Distillation; 2) Feedback Modeling (1/n) 🧵
14
101
14
598
110,364