Area of research
Automotive Engineering · Artificial Intelligence
Research interest
Research interests include Computer science, Artificial intelligence, Robustness (evolution), Artificial neural network, End-to-end principle, and Robot.
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
BarrierNet: Differentiable Control Barrier Functions for Learning of Safe Robot Control
Robust flight navigation out of distribution with liquid neural networks
Closed-form continuous-time neural networks
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Identifying and Mitigating Potential Biases in Predicting Drug Approvals
Liquid Time-constant Networks
Evidential Deep Learning for Guided Molecular Property Prediction and Discovery
Neural circuit policies enabling auditable autonomy
Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation
Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing