Arpan Kusari

Dr. Kusari’s research interest spans through the spheres of sensing and perception; and decision-making and control, in the domain of  autonomous vehicles. In the sensing and perception realm, his interests lie in uncertainty quantification and fault tolerance of a generic sensor suite. Dr. Kusari is also interested in utilizing noise reduction methods for designing cost-effective low SNR (signal-to-noise ratio) LiDARS. In decision making and control, he is focussed on creating a robust framework capable of handling the uncertainty stemming from other road users’ behavior. In that regard, Dr. Kusari is pursuing development of methods for increasing the efficiency and robustness of probabilistic formalisms such as reinforcement learning and evolutionary algorithms to safely navigate the dynamic environment. Dr. Kusari has spent five years at Ford Motor Company working collaboratively with researchers from MIT and University of Michigan; formulating research problems in autonomous vehicles. His doctoral research was in LiDAR mapping in the areas of sensor calibration, precise estimation of earthquake displacement and uncertainty quantification in the point cloud. 

Expertise: Autonomous Vehicle Validation, AI for Transportation, Multi-sensor Calibration Fault Tolerance, Sensor Fusion, Object Detection, Out-of-Distribution (OOD) Detection, Statistical Bounds in Machine Learning, Perception Systems, Decision-Making in AVs, Deep reinforcement Learning (DRL) Signal Optimization