Robin Jia

@robinomial

Assistant Professor @CSatUSC | Previously Visiting Researcher @facebookai | Stanford CS PhD @StanfordNLP

Los Angeles, CA robinjia.github.io Joined June 2018

969 Following 5K Followers 390 Posts

Latest posts 5

Join me at 2:10pm today in ASEM Ballroom 201 to hear my reflections on my Adversarial Evaluation PhD work and my thoughts on how the ChatGPT Era represents a new Golden Age for the Adversarial Mindset!

Jordan Boyd-Graber @boydgraber Jul 10

Come see @sewon__min @mrinmayasachan @robinomial (and Jenny Ni / Naman Goyal, whose handles I can't find).
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Robin Jia @robinomial Jul 6

Heading to ICML where I’ll be presenting 3 papers on differentially private synthetic data, mech interp/ML theory for TFs solving graph problems, & mech interp for LLM negation processing, and giving a talk on the legacy of adversarial evaluation at the EMM-QA workshop!

Robin Jia @robinomial May 29

Being Johnny’s PhD advisor has not only been a great privilege, but it has forever changed my research vision. His work combining AI, law, and statistics opened my eyes to how technical research can guide policy and promote AI accountability. Excited for his next work as Dr. Wei!

Johnny Tian-Zheng Wei @johntzwei May 28

Hi all, I defended my PhD thesis. My thesis in two sentences:

Current AI measurement takes LLMs as fixed objects, which constrains us to observational measurement. *Spiking* the training data (inserting certain data at known rates), enables statistically principled measurement.

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Robin Jia @robinomial Apr 20

Excited to announce that Hubble, our new language model suite for studying LLM memorization, was recently featured in @ScienceMagazine ! Hubble has also received an oral presentation slot at ICLR; if you're there, check out @johntzwei and @Aflah02101 's presentation on Saturday!

Robin Jia @robinomial Oct 24, 2025

Hubble is finally out! We used 200k GPU hours from @NSF NAIRR and @nvidia to build a comprehensive resource for the scientific study of LLM memorization. Fully open-source models & data up to 8B params + 500B tokens with controlled data insertion to study memorization risks 🔭✨

Johnny Tian-Zheng Wei @johntzwei Oct 24, 2025

Announcing 🔭✨Hubble, a suite of open-source LLMs to advance the study of memorization!

Pretrained models up to 8B params, with controlled insertion of texts (e.g., book passages, biographies, test sets, and more!) designed to emulate key memorization risks 🧵

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