Rajesh K. Gupta

Microelectronics, EDA, Intel; Now director @HDSIUCSD and Professor @ucsd_cse @ucsd; UC San Diego. Generally curious, nothing official here.

[Rajesh K. Gupta @GuptaUcsd]

@prfsanjeevarora Interesting philosophical perspective on solving problems, this utilitarian view from philosophy may have value for LLM-generated solutions.

[Sanjeev Arora @prfsanjeevarora]

Avi is not only an amazing scientist but also a wise and great person. Worth watching this interview

[Race @multiplanet1]

Elon Musk cried on national television when his childhood heroes called him a fraud.

Neil Armstrong and Gene Cernan, the first and last men to walk on the moon, publicly testified against SpaceX. They said Musk was reckless. That private spaceflight was dangerous. That he was going to get people killed. They asked Congress to shut him down.

These were the men Musk grew up worshipping. The posters on his wall. The reason he built rockets in the first place. And they went on television and said he was a disgrace to space exploration.

In a 60 Minutes interview shortly after, Musk was asked about it. He started speaking and his voice broke. His eyes filled. He couldn't finish the sentence. The richest man in tech, the guy who argues with regulators and fires engineers mid-meeting, sat on camera and cried because his heroes rejected him.

He didn't stop building. He didn't change direction. He didn't even respond to them publicly. He just kept launching rockets until the rockets proved him right.

Armstrong never lived to see SpaceX land a booster. Cernan never saw Starship. The men who said it couldn't be done died before the man they doubted did it.

Most people need approval from the people they admire before they act. Musk got the opposite of approval and acted anyway. That's the gap. Not talent. Not money. The willingness to keep building while the people you love most tell you to stop.

[Halıcıoğlu Data Science Institute @HDSIUCSD]

UC San Diego Halıcıoğlu School of Data Science and Computing is now welcoming applications from New York and Delaware residents for the Master of Data Science online.

📅 Application Deadline: June 17, 2026
📝 Apply today: https://t.co/YvhWwCnxrr
📩 Questions: [email protected]

[Gabe Wilson MD @Gabe__MD]

PREPRINT REVIEW
—————————
45 domain scientists spent 469 hours rating 2,960 individual criticisms from peer reviews of 82 Nature-portfolio papers, mostly from Nature Communications. The reviews came from both human reviewers and three frontier AI models deployed as tool-equipped reviewing agents: GPT-5.2, Gemini 3.0 Pro, and Claude Opus 4.5.

Averaged across papers, GPT-5.2 had a higher rate of fully positive criticisms, defined as correct, significant, and sufficiently evidenced, than the top-rated human reviewer baseline. 60.0% versus 48.2%. P = 0.009. In holistic expert judgments, it matched or exceeded the top-rated human on about half of papers. All three AI reviewers exceeded the lowest-rated human across every dimension.

The tradeoff matters: AI reviewers were less factually correct than the top-rated humans (86.2% vs 92.3%). The win was on significance and evidence quality, not accuracy. Their accurate criticisms were more often rated as significant and well-evidenced, and surfaced a distinct 26% of issues that no human reviewer raised.

The honest limitations: AI reviewers overlap with each other far more than human reviewers do, 21% versus 3% for cross-reviewer pairs. They exhibit 16 recurring weaknesses including limited subfield knowledge, poor long-context management across multiple files, and a tendency to be overly critical on minor issues. The authors position current AI reviewers as complements, not replacements.

The most actionable finding is not "replace peer reviewers." It is "add one AI reviewer to a human panel." In the paper's simulation, a 2-human-plus-1-AI panel preserved the amount of useful unique feedback while reducing reviewer noise.

This connects directly to the Rodman conversation from earlier this week about evidence infrastructure. Peer review is one of the primary bottlenecks in the scientific evidence pipeline. If AI can surface a quarter of issues that human reviewers miss while producing higher-significance critiques on the issues they share, the implications for the speed and rigor of scientific publication are significant.

Posted to arXiv this week. Preprint. But the signal is strong enough to take seriously.

[Haider. @haider1]

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code

They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists

"their role is to help humans build"

[Demis Hassabis @demishassabis]

I’ve always believed the No.1 application of AI should be to improve human health.

That work started with AlphaFold, and now at @IsomorphicLabs with the mission to reimagine drug discovery and one day solve all disease!

We are turbocharging that goal with $2.1B in new funding.

[Eric Xing @ericxing]

“A university that asks students what they want and gives it to them is not educating – it is catering.”

In my annual MBZUAI commencement address today, I emphasized two principles that shape our university: 1) The core business of university is creating knowledge and teaching knowledge, everything else is secondary; 2) Our university is a forge. Faculty and leaders are not pastoral counselors; they are forge masters. Their job is to make people capable, not comfortable. — Maybe obvious, but hard to say and hard to enact these days.

https://t.co/nq6YjFUP0T

[Rajesh K. Gupta @GuptaUcsd]

@EarlenceF Congratulations, Earlence!

[Rajesh K. Gupta @GuptaUcsd]

So heartwarming to see a strong MLSys group emerge at UC San Diego!

[Google for Developers @googledevs]

Breaking LLM inference’s autoregressive bottleneck 🛠️

We've teamed up with @haozhangml, @YimingBob, and @aaronzhfeng, among others from UCSD to achieve a massive 3.13X speedup for LLM inference on Google Cloud TPUs using Diffusion-Style Speculative Decoding (DFlash).

Read the blog: https://t.co/bIugAUJm8S

[Zhijian Liu @zhijianliu_]

Super happy to see my UCSD colleague help port our DFlash to TPU. Big speedup too!! More to come.

[UC San Diego @UCSanDiego]

A bold investment in the future of AI and computing.
Thanks to a $125M gift from alumnus Taner Halıcıoğlu ’96, UC San Diego launches the Halıcıoğlu School of Data Science and Computing.

🔗 Learn more: https://t.co/y6L91D9jPM

[Manisha Mishra @manishamishra24]

A man spends 50 years teaching at MIT.

He knows his time is running out.

So he records one last lecture — everything he knows, distilled into a single hour.

He died 5 months later.

This is that lecture.

The most important hour you'll watch this week. 👇
Bookmark it for later

[India in Los Angeles @CGILosAngeles]

On April 21, CG @drkjsrini participated in the first ever #USIndiaForum organized by @UCSD in partnership with @IndiasporaForum @USISPF & #MotwaniJadejaFamily Foundation as a keynote speaker, joining leaders from academia/industry/thinktanks/policymaking to strengthen partnerships between #India & #US. CG emphasized that #India looks forward to strengthening India–US relations with a strong focus on collaboration in #AI #technology #education #defence #space #advancedmanufacturing & other emerging sectors. He highlighted that engagement with educational institutions will open new opportunities for knowledge exchange, joint research & capacity building, further deepening strategic partnership.

Event attended by UCSD Chancellor @pkkhosla #UPSC Chairman Ajay Kumar, Hon’ble MP @milinddeora, former Ambassadors @AmbMoKumar @RichRVerma @Tim_Roemer , thinktanks like @orfonline @BrookingsInst , various policy experts, entrepreneurs, specialists, Indian companies / startups, etc for two days of productive discussions
@MEAIndia @IndianEmbassyUS @IndianDiplomacy @samirsaran @ronakdd @tanvi_madan @USIBC @FollowCII @ficci_india @MukeshAghi @DerekJGrossman @mrsandhill @ashajadeja @MattooShashank @ideaforge_tech @ankit_ideaforge @GalaxEye @thesuyashsingh @mitulbid #IndiaUS #SoCal #SanDiego

[Rajesh K. Gupta @GuptaUcsd]

We are squarely into the stage of optimizations for the new stack on perception-cognition-action. New capabilities are bound to follow..

[Zhijian Liu @zhijianliu_]

Reasoning VLAs can think. They just can't think fast. Until now.

Introducing FlashDrive⚡
🚀 716 ms → 159 ms on RTX PRO 6000 (up to 5.7×)
✅ Zero accuracy loss

FlashDrive = streaming inference + DFlash speculative reasoning + ParoQuant W4A8

Real-time reasoning for autonomous driving is here!
https://t.co/zWIBhyJ5QN

[Rajesh K. Gupta @GuptaUcsd]

“the experts will always tell you it can't be done. build it anyway!”

It is true in more ways than one, but it is a “feature” more than a bug: experts are experts because they can see ways things can fail, and are trained to be careful and precise to ensure correctness. Risk

[Elon Musk @elonmusk]

True