Cofounded & running @ml_collective. Weekly talks at Deep Learning Classics & Trends (DLCT). Research at Google DeepMind. DEI/DIA Chair of ICLR & NeurIPS.
San Francisco, CA
Joined March 2013
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The opportunity gap in AI is more striking than ever. We talk way too much about those receiving $100M or whatever for their jobs, but not enough those asking for <$1k to present their work.
For 3rd year in a row, @ml_collective is raising funds to support @DeepIndaba attendees.
Rosanne Liu retweeted
I’m looking for a Ph.D. research intern in Human-centered NLP with a focus on Multilinguality to come work at Apple MLR (Machine Learning Research team), in the SF Bay Area, CA! ☀️
Application details in thread below 🧵👇
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Rosanne Liu retweeted
I'm hiring a Student Researcher to work on scaling laws at Google DeepMind! Project is for 19 weeks, starting winter/spring '27 (ideally: January '27), in-person in downtown SF. If you're interested, fill out this form: forms.gle/9MKo9ciBmp5wsyiU7
Rosanne Liu retweeted
i was diagnosed with thyroid cancer many years ago purely by chance, because i had to get blood work for my US green card application (thank you, USCIS!). i was in my mid-30’s and was reasonably healthy (or so i believed.) this was shocking and totally unpredictable news to me. thanks to the amazing team of clinicians at NYU Langone, i successfully went through a total thyroidectomy followed by radioactive iodine therapy. but, it was truly the most uncertain and confusing period of my life.
this is when i first realized that no one can opt out of healthcare. being sick isn’t what you choose to or choose not to. it is something that unpredictably happens to all of us at some point for one reason or another. this is why it is a healthcare system that we collectively build and use to support each other at the society level. because it must support everyone, the complexity of creating, maintaining and improving this system is a Herculean effort that calls for all the help in the world.
today, i am announcing @OrtetAI together with my dear co-founders, @keunwoochoi, @HenriDwyer, @elmanmansimov, @causalclaudia and Jeff, to lend our support to improving the health of each and every one of us. Ortet’s goal is to create a technological foundation that will enable health systems to deliver predictably better care to each and every patient. this will only be possible by working closely with all stakeholders in the broader ecosystem, which is enabled in part by Ortet’s close partnership with Thoreau.
Ortet is taking the very first step of a long journey ahead, and at the end of this journey, i strongly believe we would be able to say that Ortet had contributed to building better, more predictable healthcare and thus to improving health of each and every one of us.
see ortet.ai/ for more!
Stop blowing up Looped Transformers — it's not magical, it doesn't add recurrence to the (still and always) feedforward network, it was done years ago (Dehghani et al., 2019). All it does is making your model deeper, the same as if you added more layers to begin with.
However many loops you do, the transformer is still inherently feedforward, the only "recurrence" added here is "depth recurrence", which is the same as making the model deeper (with tied parameters). The only real "recurrence" is if you carry things *forward in time*.
Rosanne Liu retweeted
I'm hiring! 🚀
Our Frontier Health team at @GoogleDeepMind is looking for a Research Scientist to build foundational AI & physiological world models for human biology.
Help us transform healthcare from reactive observation to proactive intervention. 🧬
🔗 Apply: goo.gle/3SPNis2
Rosanne Liu retweeted
For me, a fun entry-point into this paper is just to observe:
The transformer is a silly architecture for token generation. During inference, transformers operate with their hands tied behind their back. They do this because it makes training cheaper.
What if a foundation model could tell us how to modify its architecture to boost inference and reasoning instantaneously—without retraining? What if that tweak incurred near zero latency cost during generation and supported indefinite state tracking? arxiv.org/abs/2608.17981
Rosanne Liu retweeted
Mike’s recirculation paper and Jie’s post about scaling laws coming out on the same day couldn’t have been timed better
Rosanne Liu retweeted
💥I have been able to independently verify @mc_mozer and Google team's work on ♾ Recirculation👀 using Llama 3.2 1B model on a M4 MacBook.
😱 +16.95% improvement on GSM8K-Platnium with zero weight changes.
Reproduction code with optimized mlx prefill kernel. 👇
100%! The title we secretly want to publish it under is "Recirculation. OR: CoT is a cop out."
Rosanne Liu retweeted
What if a foundation model could tell us how to modify its architecture to boost inference and reasoning instantaneously—without retraining? What if that tweak incurred near zero latency cost during generation and supported indefinite state tracking? arxiv.org/abs/2608.17981
18 years into paper publishing and I still sheepishly search for our new paper title to see if it's gotten picked up in the gap between arxiv posted and twitter announced
Rosanne Liu retweeted
This tweet is for all the babies that got to experience The Odyssey in IMAX from inside the womb.
Rosanne Liu retweeted
Classic tech bro being tech bro. Recruits from academia, builds on academia's innovation, outbids academia for every GPU on earth, publishes nothing peer-reviewed, gives back pennies, then declares academia "mainly fraud." That's pulling up the ladder and spitting down it.
On a two week vacation aka full time childcaring with no pay no appreciation and 24h on call where your coworker is kinda a jerk but super duper cute
