Daniel Kang (@ddkang) / X

Daniel Kang

550 posts

Daniel Kang

@ddkang

Asst. professor at UIUC CS. Formerly in the Stanford DAWN lab and the Berkeley Sky Lab.

Stanford, CA

Joined November 2010

Daniel Kang
@ddkang
Jan 24, 2022
ML models are being deployed in mission-critical settings, such as autonomous vehicles. Shockingly, the data used to train these models are rarely checked! The Lyft Level 5 dataset has errors in 70% of the validation scenes, see our blog post: dawn.cs.stanford.edu/2022/01/24/loa/ (1/5)
Daniel Kang
@ddkang
Aug 11, 2025
The prevailing wisdom is that compute is the most important factor for frontier AI training. We think this is wrong: data is the most costly and important component of AI training. We collected estimates of revenue for major data labeling companies and compared them with the
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Daniel Kang
@ddkang
Nov 5, 2024
I am recruiting PhD students this cycle! If you're interested in: - AI agents and security - both AI agents for cybersecurity and security of AI agents - Auditing AI - Data and AI (yes I still do this... projects are cooking) Please apply to UIUC CS! 🧵 for recent projects
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Daniel Kang
@ddkang
Oct 10, 2025
I'd like to hear @OpenAI's explanation for why they did this!
Nathan Calvin
@_NathanCalvin
Oct 10, 2025
One Tuesday night, as my wife and I sat down for dinner, a sheriff’s deputy knocked on the door to serve me a subpoena from OpenAI. I held back on talking about it because I didn't want to distract from SB 53, but Newsom just signed the bill so... here's what happened: 🧵
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Daniel Kang
@ddkang
Aug 29, 2022
Some professional news: I will be started as an asst. professor at UIUC in fall 2023! And I'll be spending the upcoming year at UC Berkeley as a postdoc with Ion Stoica 1/4
Daniel Kang
@ddkang
Sep 18, 2023
Verified ML in the form of ZKML has captured significant interest. But it's too slow in practice, taking 6 hours to verify the Twitter recommendation model Enter TensorPlonk, a new ZKML proving system with >1,000x faster proving 📝Blog post: medium.com/@danieldkang/d… 🧵 1/9
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Daniel Kang
@ddkang
Oct 18, 2022
As ML becomes increasingly complex, ML-as-a-service (MLaaS) providers are proliferating (OpenAI, Google, AWS, etc.), which raises an important question: how can we trust MLaaS providers? Today, we show how to trustlessly verify model predictions with zero-knowledge proofs! 1/6
Daniel Kang
@ddkang
Feb 13, 2024
As LLMs have improved in their capabilities, so have their dual-use capabilities. But many researchers think they serve as a glorified Google We show that LLM agents can autonomously hack websites, showing they can produce concrete harm Paper: arxiv.org/abs/2402.06664 1/5
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Daniel Kang
@ddkang
Nov 6, 2025
It's that time of year again! I'm actively recruiting PhD students, please apply to UIUC if you're interested in working with me My research interests are very broad and we've won several awards this last year: 1. I care about rigorous measurement of AI progress via benchmarks
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Daniel Kang
@ddkang
Nov 10, 2023
OpenAI announced GPT-4 fine-tuning this week. Fine-tuning can remove RLHF protections from weak models, but is GPT-4 susceptible? Unfortunately yes: removing RLHF protections from GPT-4 is trivial Paper: arxiv.org/abs/2311.05553 🧵1/6
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Daniel Kang
@ddkang
Apr 3, 2023
I'm excited to announce our library zkml for trustless machine learning! After months of hard work, we've supercharged its performance & expanded its capabilities. Now, zkml achieves 92% accuracy on ImageNet! Blog post: medium.com/@danieldkang/f… GitHub: github.com/ddkang/zkml 1/
medium.com
Open-sourcing zkml: Trustless Machine Learning for All
We’re excited to announce the open-source release of zkml, our framework for producing zero-knowledge proofs of ML model execution. zkml…
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Daniel Kang
@ddkang
Apr 17, 2023
Twitter open-sourced their recommendation algorithm, but the weights remain hidden! How can we trust it? We'll show how to verify the Twitter algorithm with zkml! 📝 Blog post: medium.com/@danieldkang/6… GitHub: github.com/ddkang/zkml 1/6
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Daniel Kang
@ddkang
Apr 12, 2023
Our open-source release of zkml empowers anyone to verify a model executed honestly without seeing the weights for a wide range of models! Let’s dive into zkml’s capabilities Full post: medium.com/@danieldkang/e… GitHub: github.com/ddkang/zkml 1/
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Daniel Kang
@ddkang
Jul 22, 2025
SWE-bench Verified is the gold standard for evaluating coding agents: 500 real-world issues + tests by OpenAI. Sounds bullet-proof? Not quite. We show passing its unit tests != matching ground truth. In our ACL paper, we fixed buggy evals: 24% of agents moved up or down the
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