Dr. Lu brings expertise in machine learning, particularly integrating human knowledge into machine learning and explainable machine learning. He has applied machine learning in a range of domain applications, such as autonomous driving and machine learning for optimized design and control of energy storage systems.
Catherine H. Hausman is an Associate Professor in the School of Public Policy and a Research Associate at the National Bureau of Economic Research. She uses causal inference, related statistical methods, and microeconomic modeling to answer questions at the intersection of energy markets, environmental quality, climate change, and public policy.
Recent projects have looked at inequality and environmental quality, the natural gas sector’s role in methane leaks, the impact of climate change on the electricity grid, and the effects of nuclear power plant closures. Her research has appeared in the American Economic Journal: Applied Economics, the American Economic Journal: Economic Policy, the Brookings Papers on Economic Activity, and the Proceedings of the National Academy of Sciences.
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.
Transportation is the backbone of the urban mobility system and is one of the greatest sources of environmental emissions and pollutions. Making urban transportation efficient, equitable and sustainable is the main focus of my research. My students and I analyze small scale survey data as well as large scale spatiotemporal data to identify travel behavior trends and patterns at a disaggregate level using econometric methods, which we then scale up to the population level through predictive and statistical modeling. We also design our own data collection methods and instruments, be it a network of smart devices or stated preference experiments. Our expertise lies in identifying latent constructs that influence decisions and choices, which in turn dictate demands on the systems and subsystems. We use our expertise to design incentives and policy suggestions that can help promote sustainable and equitable multimodal transportation systems. Our team also uses data analytics, particularly classification and pattern recognition algorithms, to analyze crash context data and develop safety-critical scenarios for automated and connected vehicle (CAV) deployment. We have developed an online game based on such scenarios to promote safe shared mobility among teenagers and young adults and plan to expand research in that area. We are also currently expanding our research to explore the use of NN in context information synthesis.
This is a project where we used classification and Bayesian models to identify scenarios that are risky for pedestrians and bicyclists. We then developed an online game based on those scenarios for middle schoolers so that they are better prepared for shared road conflicts.
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.
Lei Chen’s group focus on applying data science tools to advanced manufacturing. Chen’s research expertise and interests are to integrate the physics-based computational and experimental methods and data-driven approaches, to exploit the fundamental phenomena emerged in advanced manufacturing and to establish the design protocol for optimizing the materials and process parameters of as-fabricated parts for quality control. Current research can be summarized by:
1 One of significant challenges in additive manufacturing (AM) is the presence of heterogeneous sources of uncertainty involved in the complex layer-wise processes under non-equilibrium conditions that lead to variability in the microstructure and properties of as-built components. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production, and current practice usually reverts to trial-and-error techniques that are very time-consuming and costly. This research aims to develop an uncertainty quantification framework by bringing together physical modeling, machine-learning (ML), and experiments.
2 Computational microstructure optimization of piezocomposites involves iterative searches to achieve the desired combination of properties demanded by a selected application. Traditional analytical-based optimization methods suffer from the searching efficiency and result optimality due to high dimensionality of microstructure space, complicated electrical and mechanical coupling and non-uniqueness of solutions. Moreover, AM process inherently poses several manufacturing constraints e.g., the minimum feature size and the porosity in the piezoelectric ceramics as well as at the ceramics-polymer interface. It is challenging to include such manufacturing constraints since they are not explicitly available. This research aims to develop a novel data-driven framework for microstructure optimization of AM piezoelectric composites by leveraging extensive physics-based simulation data as well as limited amount of measurement data from AM process.
3 Lithium (Li) and other alkali metals (e.g., sodium and potassium) are very attractive electrode candidates for the next-generation rechargeable batteries that promise several times higher energy density at lower cost. However, Li-dendrite formation severely limits the commercialization of Li-metal batteries, either because dendrite pieces lose electrical contact with the rest of the Li-electrode or because growing dendrites can penetrate the separator and lead to short circuits. This research aims to develop a computational model to accelerate the design of dendrite-free Li-metal batteries.
Xiuli Chao’s research interests include queueing, scheduling, financial engineering, inventory control, and supply chain management. He is the co-developer of Lekin Scheduling System. He is the co-author of two books, Operations Scheduling with Applications in Manufacturing and Services (Irwin/McGraw-Hill, 1998), and Queueing Networks: Customers, Signals, and Product Form Solutions (John Wiley & Sons, 1999). Chao received the 1998 Erlang Prize from the Applied Probability Society of the Institute for Operations Research and Management Science (INFORMS), and received the 2005 David F. Baker Distinguished Research Award from the Institute of Industrial and Systems Engineers (IISE). He also received the Jon R. and Beverly S. Holt Award for Teaching Excellence from the College of Engineering of the University of Michigan. Chao is a fellow of both IISE and INFORMS.
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.
Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.
Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data. They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.
Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.