Dr. Kochunas’s research focus is on the next generation of numerical methods and parallel algorithms for high fidelity computational reactor physics and how to leverage these capabilities to develop digital twins. His group’s areas of expertise include neutron transport, nuclide transmutation, multi-physics, parallel programming, and HPC architectures. The Nuclear Reactor Analysis and Methods (NURAM) group is also developing techniques that integrate data-driven methods with conventional approaches in numerical analysis to produce “hybrid models” for accurate, real-time modeling applications. This is embodied by his recent efforts to combine high-fidelity simulation results simulation models in virtual reality through the Virtual Ford Nuclear Reactor.
My reserach group–theNeurobionics Lab–has two chief goals. Firstly, we seek to answer fundamental questions about human locomotion through a deeper understanding of how limb mechanics are felt and regulated by the nervous system. These properties are important because they govern how people respond to disturbances during gait, such as unexpectedly stepping on an obstacle, or carefully walking over uneven terrain. Moreover, the ability to regulate these mechanics is drastically impaired following neurological injury. As a result, impaired individuals fall more frequently, fatigue faster, and have abnormal gait patterns that inhibit daily life. The more we understand about how the brain controls the body during locomotion, the better we can assess, track, and treat the changes that occur following neurological injury.
The second mission of the group is to develop technologies that address the deficits that arise from neuropathologies and amputation. We leverage biomimetic design and control approaches to develop novel wearable robotic systems. Our intent is to not only address the locomotor deficits of these individuals, but also enable them to exceed the performance of their able-bodied counterparts. Our approach is unique: the biomechanical science that we discover is used to develop a new class of assistive technology. Through interdisciplinary, bidirectional feedback between science and engineering, the Neurobionics Lab conducts innovative work that will eventually impact the lives of the disabled.
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.
Alex Gorodetsky’s research is at the intersection of applied mathematics, data science, and computational science, and is focused on enabling autonomous decision making under uncertainty. He is especially interested in controlling, designing, and analyzing autonomous systems that must act in complex environments where observational data and expensive computational simulations must work together to ensure objectives are achieved. Toward this goal, he pursues research in wide-ranging areas including uncertainty quantification, statistical inference, machine learning, control, and numerical analysis. His methodology is to increase scalability of probabilistic modeling and analysis techniques such as Bayesian inference and uncertainty quantification. His current strategies to achieving scalability revolve around leveraging computational optimal transport, developing tensor network learning algorithms, and creating new multi-fidelity information fusion approaches.
Sample workflow for enabling autonomous decision making under uncertainty for a drone operating in a complex environment. We develop algorithms to compress simulation data by exploiting problem structure. We then embed the compressed representations onto onboard computational resources. Finally, we develop approaches to enable the drone to adapt, learn, and refine knowledge by interacting with, and collecting data from, the environment.
Gabor Orosz is an Associate Professor of Mechanical Engineering and Civil and Environmental Engineering. His theoretical research include dynamical systems, control, and reinforcement learning with particular interests in the roles of nonlinearities and time delays in such systems. In terms of applications he focuses on connected and automated vehicles, traffic flow, and biological networks. His research has been supported by the National Science Foundation and industrial funds. His recent work appeared in journals like IEEE Transactions on Automated Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Intelligent Transportation Systems, and Transportation Research Part C. For the latter journal he has also be serving as an Editor. WIRED magazine reported on his experimental results when his team built a connected automated vehicle and evaluated it in real traffic. He served as the program chair for the 12th IFAC Workshop on Time Delay Systems and served as the general chair for 3rd IAVSD Workshop on Dynamics of Road Vehicles, Connected and Automated Vehicles.
My areas of interest are control, estimation, and optimization, with applications to energy systems in transportation, automotive, and marine domains. My group develops model-based and data-driven tools to explore underlying system dynamics and understand the operational environments. We develop computational frameworks and numerical algorithms to achieve real-time optimization and explore connectivity and data analytics to reduce uncertainties and improve performance through predictive control and planning.
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.
David works on computer vision and machine learning with the end goal of developing autonomous systems that can learn to build representations of the underlying state and dynamics of the world through observation (and potentially interaction).
Towards this end, he is particularly interested in understanding physical and functional properties from images. His research interest in physical properties aims to address how we can recover a rich 3D world from a 2D image. He is especially interested in representations — the answers that are obvious are also obviously defective — as well as how we should reconcile our strong prior knowledge about this structure of the problem with data-driven techniques. In recent work, he has become interested in applying this more broadly in the hope that we can develop AI systems that can learn how the physical world works from observation, including work on solar physics. In functional properties, he is interested in inferring and understanding opportunities for interaction with the environment by both robots and humans, both in terms of how one would learn this and what this implies for a physical understanding of the world.
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.
Current research includes a project funded by Toyota that uses Markov Models and Machine Learning to predict heart arrhythmia, an NSF-funded project to detect Acute Respiratory Distress Syndrome (ARDS) from x-ray images and projects using tensor analysis on health care data (funded by the Department of Defense and National Science Foundation).