A Professor @ucsd_cse. Scholar at Code Metal. Helps people write programs they can trust.
San Diego, CA
- Tweets 9,992
- Following 777
- Followers 9,397
- Likes 5,807
Reviewers should be allowed to give a new type of score that indicates "the paper in the current form is a waste of everyone's time".
After N of these papers, an author should not be allowed to submit for a period of time.
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we're hiring
Check out our Assistant Professor position @ucsd_cse!
apol-recruit.ucsd.edu/JPF046…
Since it is 2026, it is worth quoting explicitly from the ad that this not AI-specific: “The department is looking for exceptional candidates in all areas of Computer Science and Engineering.”
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Loris D'Antoni retweeted
girl I got a PhD in computer science and bought myself everything I own including my ring, don't wait for a man to get you shit
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The reason I've been quiet recently.
Over the last two months I worked hard, using AI, on solving two open problems that my UIUC research lab was not able to solve over the last 7 years.
Finally, we proved them. Viva @AnthropicAI and @OpenAI!
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New funding opportunity in verification/PL research lnkd.in/p/gcDbkmKy
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Loris D'Antoni retweeted
I finally infiltrated the elite program synthesis department at UCSD. 😎
Been a pleasure getting to know @lorisdanto and find ways to collab. Excited to learn more about the PL group’s work.
On the pod: "Constrained Adaptive Rejection Sampling" with @ucsd_cse professor @lorisdanto.
Hear how symbolic AI experts have navigated the LLM era and why the future of AI code generation depends on program synthesis and formal methods.
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Loris D'Antoni retweeted
If you are at #FloC26, come to the workshop on AI for Math and Computer Science (AIMACS) on July 25!
* Tutorial by @KaiyuYang4 on AI-aided theorem-proving
* Tutorial by Clark Barrett and Sorrachai Yingchareonthawornchai on #CSLib, the Lean computer science library
* Talks by @lorisdanto, @gtsoukal, and Moa Johansson
* A panel on how computer scientists should respond to AI progress, with @vardi, @ShriramKMurthi, Pavithra Prabhakar, and Armando Solar-Lezama. I will moderate.
* Many very interesting posters.
program.floc26.org/AIMACS-20…
sites.google.com/view/aimacs…
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This year's FLOC will include RajeevFest, a special workshop to celebrate the career of my PhD advisor Rajeev Alur!
lorisdanto.github.io/rajeevf…
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Watch the full interview:
piped.video/BwcuNBfHj2w
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On the pod: "Constrained Adaptive Rejection Sampling" with @ucsd_cse professor @lorisdanto.
Hear how symbolic AI experts have navigated the LLM era and why the future of AI code generation depends on program synthesis and formal methods.
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Here are my ten new followers. I understand bot detection is hard, but these ones seem not that hard to catch...
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Getting LLMs to generate samples that satisfy constraints is a fundamental problem in machine learning. The standard approach is rejection sampling: generate a sample, check whether it satisfies the constraint, and if not, try again. 1/
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This work shows that the structure of constraint checkers can be exploited to accelerate sampling without sacrificing correctness; reminiscent of how conflict-driven clause learning transformed SAT solving by extracting and reusing information from failed search attempts. 6/
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Paper: Constrained Adaptive Rejection Sampling arxiv.org/abs/2510.01902
w/ Paweł Parys, Sairam Vaidya, @BergKirkpatrick
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At ICML, Jinwoo Kim will present the first approach for enforcing formal structural constraints like regexes, grammars, and schemas on the output of continuous diffusion language models, entirely without retraining. 1/
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The result is a training-free constrained generation framework for continuous diffusion language models that can exactly enforce formal syntax constraints while provably preserving generation quality. 4/
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Paper: Continuous Diffusion Models Can Obey Formal Syntax
arxiv.org/abs/2602.12468
@BergKirkpatrick /the end
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