I build data-centric methods that scale across foundation models — from LLM agents and world models to end-to-end driving. I care about principled data selection (e.g., optimal transport), closed-loop evaluation, and how these push the frontier of post-training and generative world models.
Advised by Prof. Alexandre Alahi. Joining NVIDIA Research as a research intern in summer 2026.
I am a PhD candidate at EPFL, Switzerland, working with Prof. Alexandre Alahi at the VITA Lab. Before EPFL I received my M.Sc. in Robotics, Systems and Control from ETH Zurich and B.E. in Navigation Engineering from Wuhan University.
During my master's I worked on human-to-robot handover at the AIT Lab at ETH Zurich with Prof. Otmar Hilliges, in collaboration with NVIDIA. Earlier I worked on generative traffic simulation with Prof. Bolei Zhou at UCLA.
My current research sits at the intersection of data-centric ML, LLM agents & post-training, world models & video generation, and autonomous driving & robotics. Recent work includes RAP (scalable 3D rasterization for end-to-end driving, powering the 1st-place Waymo 2025 entry), TAROT (optimal-transport data selection, ICML 2025), and Weak-for-Strong (training a small meta-agent to orchestrate frontier LLMs).
I am actively seeking research roles and collaborations on data-centric post-training, agent evaluation, and world models for robotics/AD. Please reach out if our interests overlap.
First-author or core contributor unless otherwise noted. Full list on Google Scholar.
The best way to reach me is by email at lan.feng@epfl.ch. I am based in Lausanne, Switzerland. I also use X/Twitter and LinkedIn.
I am especially interested in collaborating on: (i) data curation for LLM post-training, (ii) long-horizon agent evaluation, and (iii) controllable world models for robotics and autonomous driving. If any of these resonate, drop me a line.