Worked at the intersection of computer vision and robotics to develop a robot action policy that generalizes to unseen tasks and environments. Using the DROID dataset — a large-scale collection of diverse robot task demonstrations — we modified the policy's diffusion architecture to improve generalization.
Key contributions included creating visualizations for gripper heuristics to extract meaningful waypoints from noisy human-operated trajectories, training and evaluating baseline models through the full experimental pipeline (configuring training runs, monitoring loss, and conducting physical robot experiments), and investigating distributed data parallelism across multiple GPUs to accelerate training.
Developing a system to detect and prevent malicious network attacks using artificial data generated through diffusion models. The work focuses on building ML-powered security systems that operate reliably in real-world networks where data distributions shift and adversaries adapt.