About me

Hi, Welcome to my site!

My name is Zeyu Dong, an PhD candidate in Applied Math & Statistics @ Stony Brook University. See bio page for official bio.

My research interests include: autonomous driving, vision-language models, reinforcement learning, training and inference acceleration, and more. Here are the interesting research projects I have been working on:

Research Projects

Generalization of End-to-end Autonomous Driving with LLM

Designed a hybrid architecture combining VLMs with end-to-end driving models, leveraging pre-trained VLMs for generalization, and achieving ~50% failure rate reduction in real-world deployments.

Training Models to Assist Legacy Devices

Developed Learning to Help, a hybrid framework jointly optimizing cloud and edge models, improving image classification by 20% with minimal server interaction.

Sim2Real for End-to-end Autonomous Driving

Developed a training approach to transfer expert driving knowledge from simulation to real-world, addressing data scarcity with transformers and domain-randomized pre-training, achieving ~60% failure rate reduction on unseen real-world tasks.

Intelligent Control for Electron Orbit Feedback System

Applied Deep RL to control high-dimensional system at NSLS-II, developing model-based policy gradient algorithms for adaptive control, deploying on FPGA with <100ns latency, improving electron orbit stability by ~30%.