Introducing the first NeRF-based 3D style transfer method that generalizes across scenes and styles! Our approach eliminates the time-consuming optimization for each scene or style, making it more efficient than previous NeRF-based methods. Check it out! #GCPR2024

Caglar Gulcehre and I are excited to share our lecture slides for EE-628 Training Large Language Models course: epfl.ch/labs/lions/tea… If you have any feedback, please reach out to us. I am also at #ICLR25.

We are presenting our work on backprop alternatives, "Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism," co-authored with Mete Erdoğan & Cengiz Pehlevan, today at #Neurips2025! 📍 Poster S. 1, Exhibit Hall C,D,E - #2006 (Wed, 11am–2pm PST)

Check out Mete Erdoğan 's poster at NeurIPS. He just started his PhD at Stanford with one oral, one spotlight, and one poster presentation. Go talk to him at #909 today!

For anyone interested in understanding orthogonalization/spectral based methods here’s the slides from our #neurips25 oral that I tried to make more broadly about the topic.

1/ Wonderful student projects from CS329H (Fall ’25) ML from Human Preferences at Stanford University! 🚀 Sang Truong Andreas Haupt and I introduced students to preference learning + alignment, culminating in final projects. Out of ~50, here are 5 standouts 👇