Had the delight of contributing during my internship at #GoogleDeepMind, collaborating with incredible collaborators from Berkeley, U. Toronto, Cambridge, Stanford and @deepmind.google.web.brid.gy.

Learn more here: playground.mujoco.org

It was a delight to contribute during my internship at
#GoogleDeepMind, collaborating with incredible folks from
from Berkeley, U. Toronto, Cambridge, Stanford and
@deepmind.google.web.brid.gy.

Dive in here:
playground.mujoco.org

We’re also excited to share MuJoCo Playground itself—a broader open-source sim-to-real pipeline harnessing GPU-accelerated reinforcement learning and environment simulation with JAX, tackling complex real-world tasks with training in as little as 10 minutes.

Huge thanks to my collaborators: @MihaelaVDS @_chris_lu_ @RobertTLange @jfoerst.bsky.social @tennisonliu @QianZhaozhi @AlexJChan @claudio @weatheralljim75 🙌

DM me to connect at NeurIPS!

LLM agent automated scientific discovery for equation discovery/symbolic regression and active feature acquisition—towards an LLM scientist.
“Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models”
Main conference, Thu 12 Dec 4:30pm-7:30pm, East Exhibit Hall A-C #3202

LLM multi-agent automated neural architecture search/discovery through code representations consisting of equations and neural network components.
“Automatically Learning Hybrid Digital Twins of Dynamical Systems”
Main conference, Wed 11 Dec 11:00am-2:00pm, East Exhibit Hall A-C #3500

LLM automated objective discovery to discover a better offline preference optimization algorithm for LLM post-training (RLHF) to preferences.
“Discovering Preference Optimization Algorithms with and for Large Language Models”
Main conference, Thu 12 Dec 11:00am-2:00pm, East Exhibit Hall A-C #3304