Dr. Arpan Kusari has joined UMTRI as an Assistant Research Scientist, a position where he will bring his cutting-edge industry experience. Dr. Kusari has spent five years at Ford Motor Company researching exclusively on making autonomous vehicles safe and viable, working collaboratively with researchers from MIT and University of Michigan to advance the state-of-the-art knowledge in autonomous vehicles. His 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 focused 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. 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.
My research interests are in the areas of brain-inspired machine intelligence and its applications such as mobile robots and autonomous vehicles. To achieve true machine intelligence, I have taken two different approaches: bottom-up data-driven and top-down theory-driven approach. For the bottom-up data-driven approach, I have investigated the neuronal structure of the brain to understand its function. The development of a high-throughput and high-resolution 3D tissue scanner was a keystone of this approach. This tissue scanner has a 3D virtual microscope that allows us to investigate the neuronal structure of a whole mammalian brain in a high resolution. The top-down theory-driven approach is to study what true machine intelligence is and how it can be implemented. True intelligence cannot be investigated without embracing the theory-driven approach such as self-awareness, embodiment, consciousness, and computational modeling. I have studied the internal dynamics of a neural system to investigate the self-awareness of a machine and model neural signal delay compensation. These two meet in the middle where machine intelligence is implemented for mechanical systems such as mobile robots and autonomous vehicles. I have a strong desire to bridge the bottom-up and top-down approaches that lead me to conduct research focusing on mobile robotics and autonomous vehicles to combine the data-driven and theory-driven approaches.
Reza Amini is currently an Assistant Research Scientist at the Department of Naval Architecture and Marine Engineering (NAME), University of Michigan, Ann Arbor. He was a Postdoctoral Research Fellow at the Real-Time Adaptive Control Engineering (RACE) Lab at the University of Michigan from June 2017 to January 2019. He received his Ph.D. in Mechanical Engineering from Michigan Technological University, Houghton, in 2017, where he served as a graduate research and teaching assistant at Energy Mechatronics Lab (2013-2017). Reza is an active member of IEEE Control Systems Society (CSS) and ASME Dynamic Systems and Control (DSC) Division. He is the author of over 20 peer-reviewed journal and conference papers in the broader area of control, optimization, automotive and transportation, and robotic systems. He has served as associate editor, (co-)chair, and (co-)organizer at several international conferences, including the American Control Conference (ACC), IEEE Conference on Decision and Control (CDC), IEEE Conference on Control Technology and Applications (CCTA), and ASME Dynamic Systems and Control Conference (DSCC). He is also an SAE member (since 2014), ASME member (since 2015), and IEEE member (since 2017). See here for more information.