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.
Larson’s research has been in the area of “Complex Fluids,” which include polymers, colloids, surfactant-containing fluids, liquid crystals, and biological macromolecules such as DNA, proteins, and lipid membranes. He has also contributed extensively to fluid mechanics, including microfluidics, and transport modeling. He has also has carried out research over the past 16 years in the area of molecular simulations for biomedical applications. The work has involved determining the structure and dynamics of lipid membranes, trans-membrane peptides, anti-microbial peptides, the conformation and functioning of ion channels, interactions of excipients with drugs for drug delivery, interactions of peptides with proteins including MHC molecules, resulting in more than 50 publications in these areas, and in the training of several Ph.D. students and postdocs. Many of these studies involve heavy use of computer simulations and methods of statistical analysis of simulations, including umbrella sampling, forward flux sampling, and metadynamics, which involve statistical weighting of results. He also has been engaged in analysis of percolation processes on lattices, including application to disease propagation.
Alpha helical peptide bridging lipid bilayer in molecular dynamics simulations of “hydrophobic mismatch.”
Energy Transportation related topics: data and simulations of various cleaner and ultimately cost-effective options for transit. exploring techno-economic and environmental issues in electric ride-sharing/hailing vehicles to create clean and convenient alternatives to single-occupancy vehicles. investigation of the location and integration of chargers with energy storage and bi-directional services, along with the connection to distributed renewable power generation such as solar arrays as well as the centralized electric grid.
Powertrain related topics: measurements, models and management of batteries, fuel cells, and engines in automotive and stationary applications.
Age- and sensory-related deficits in balance function drastically impact quality of life and present long-term care challenges. Successful fall prevention programs include balance exercise regimes, designed to recover, retrain, or develop new sensorimotor strategies to facilitate functional mobility. Effective balance-training programs require frequent visits to the clinic and/or the supervision of a physical therapist; however, one-on-one guided training with a physical therapist is not scalable for long-term balance training preventative and therapeutic programs. To enable preventative and therapeutic at-home balance training, we aim to develop models for automatically 1) evaluating balance and, 2) delivering personalized training guidance for community dwelling OA and people with sensory disabilities.
Prof. Huan’s research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. He focuses on methods to bridge together models and data: e.g., optimal experimental design, Bayesian statistical inference, uncertainty propagation in high-dimensional settings, and algorithms that are robust to model misspecification. He seeks to develop efficient numerical methods that integrate computationally-intensive models with big data, and combine uncertainty quantification with machine learning to enable robust and reliable prediction, design, and decision-making.
Jingwen Hu, PhD, is Associate Research Scientist in the University of Michigan Transportation Research Institute (UMTRI) with a secondary appointment as Associate Research Scientist in Mechanical Engineering in the College of Engineering at the University of Michigan, Ann Arbor.
The primary goal of Prof. Hu’s research is to reduce the incidence of injuries and fatalities in motor-vehicle crashes and other injurious events using a multidisciplinary approach. It involves 1) collecting and analyzing large-scale injury data to identify the injury problems and assess the performance of safety designs, 2) performing physical tests and computational simulations to investigate human impact responses, injury mechanisms, and injury tolerances for various body regions under field-relevant loading conditions, and 3) developing tools for large-scale computational simulations to explore the best solutions for reducing impact-induced injuries.