Gao Lili

My research focuses on controllable generative modeling for autonomous driving, particularly diffusion-based scene generation to synthesize realistic and rare driving scenarios. I previously worked on synthetic data augmentation approaches to enrich training distributions for downstream tasks such as object detection, as well as diffusion-based scene synthesis with structured conditioning mechanisms to enable controllable scenario generation. I aim to advance conditioned data generation methods that improve robustness and generalization of downstream perception systems.