I am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.
Multi-center clinical trials increasingly utilize quantitative diffusion imaging (DWI) to aid in patient management and treatment response assessment for translational oncology applications. A major source of systematic bias in diffusion was discovered originating from platform-dependent gradient hardware. Left uncorrected, these biases confound quantitative diffusion metrics used for characterization of tissue pathology and treatment response leading to inconclusive findings, and increasing the requisite subject numbers and trial cost. We have developed technology to mitigate systematic diffusion mapping bias that exists on MRI scanners and are in process of deploying this technology for multi-center clinical trials. Another major source of variance and bottleneck in high-throughput analysis of quantitative diffusion maps is segmentation of tumor/tissue volume of interest (VOI) based on intensities and patterns on multi-contrast MR image datasets, as well as reliable assessment of longitudinal change with disease progression or response to treatment. Our goal is development/trial/application AI algorithms for robust (semi-) automated VOI definition in analysis of multi-dimensional MR datasets for oncology trials.
Uncertainty quantification and decision making are increasingly demanded with the development of future technology in engineering and transportation systems. Among the uncertainty quantification problems, Dr. Wenbo Sun is particularly interested in statistical modelling of engineering system responses with considering the high dimensionality and complicated correlation structure, as well as quantifying the uncertainty from a variety of sources simultaneously, such as the inexactness of large-scale computer experiments, process variations, and measurement noises. He is also interested in data-driven decision making that is robust to the uncertainty. Specifically, he delivers methodologies for anomaly detection and system design optimization, which can be applied to manufacturing process monitoring, distracted driving detection, out-of-distribution object identification, vehicle safety design optimization, etc.
My research interests include health effects of air pollution, temperature extremes and climate change (mortality, asthma, hospital admissions, birth outcomes and cardiovascular endpoints); environmental exposure assessment; and socio-economic influences on health.
Data science tools and methodologies include geographic information systems and spatio-temporal analysis, epidemiologic study design and data management.
As an environmental epidemiologist and in collaboration with government and community partners, I study how social, economic, health, and built environment characteristics and/or air quality affect vulnerability to extreme heat and extreme precipitation. This research will help cities understand how to adapt to heat, heat waves, higher pollen levels, and heavy rainfall in a changing climate.
I have been involved in the building of data infrastructure in the study of elections, political systems, violence, geospatial units, demographics, and topography. This infrastructure will eventually lead to the integration of data across many domains in the social, health, population, and behavioral sciences. My core research interests are in elections and political organizations.
My research focuses on the development of novel Magnetic Resonance Imaging (MRI) technology for imaging the heart. We focus in particular on quantitative imaging techniques, in which the signal intensity at each pixel in an image represents a measurement of an inherent property of a tissue. Much of our research is based on cardiac Magnetic Resonance Fingerprinting (MRF), which is a class of methods for simultaneously measuring multiple tissue properties from one rapid acquisition.
Our group is exploring novel ways to combine physics-based modeling of MRI scans with deep learning algorithms for several purposes. First, we are exploring the use of deep learning to design quantitative MRI scans with improved accuracy and precision. Second, we are developing deep learning approaches for image reconstruction that will allow us to reduce image noise, improve spatial resolution and volumetric coverage, and enable highly accelerated acquisitions to shorten scan times. Third, we are exploring ways of using artificial intelligence to derive physiological motion signals directly from MRI data to enable continuous scanning that is robust to cardiac and breathing motion. In general, we focus on algorithms that are either self-supervised or use training data generated in computer simulations, since the collection of large amounts of training data from human subjects is often impractical when designing novel imaging methods.
• Computational dynamics focused on nonlinear dynamics and finite elements (e.g., a new approach for forecasting bifurcations/tipping points in aeroelastic and ecological systems, new finite element methods for thin walled beams that leads to novel reduced order models).
• Modeling nonlinear phenomena and mechano-chemical processes in molecular motor dynamics, such as motor proteins, toward early detection of neurodegenerative diseases.
• Computational methods for robotics, manufacturing, modeling multi-body dynamics, developed methods for identifying limit cycle oscillations in large-dimensional (fluid) systems.
• Turbomachinery and aeroelasticity providing a better understanding of fundamental complex fluid dynamics and cutting-edge models for predicting, identifying and characterizing the response of blisks and flade systems through integrated experimental & computational approaches.
• Structural health monitoring & sensing providing increased sensibility / capabilities by the discovery, characterization and exploitation of sensitivity vector fields, smart system interrogation through nonlinear feedback excitation, nonlinear minimal rank perturbation and system augmentation, pattern recognition for attractors, damage detection using bifurcation morphing.
Tayo Fabusuyi is an assistant research scientist in the Human Factors Group at UMTRI. His research interests are in Urban Systems and Operations Research, specifically designing and implementing initiatives that support sustainable and resilient communities with a focus on efficiency and equity issues. Drawing on both quantitative and qualitative data, his research develops and applies hard and soft Operations Research methods to urban systems issues in a manner that emphasizes theory driven solutions with demonstrated value-added. A central theme of his research activities is the use of demand side interventions, via information and pricing strategies in influencing the public’s travel behavior with the objective of achieving more beneficial societal outcomes. Informed by the proliferation of big data and the influence of transportation in the urban sphere, these research activities are categorized broadly into three overlapping and interdependent areas – intelligent transportation systems (ITS), emerging mobility services and urban futures. Before joining the research faculty at UMTRI, Dr. Fabusuyi was a Planning Economist at the African Development Bank and an adjunct Economics faculty member at Carnegie Mellon University, where he received his Ph.D. in Engineering and Public Policy.
My research interest lies in applying data science for actionable transformation of human health from the bench to bedside. Current research focus areas include cutting edge single-cell sequencing informatics and genomics; precision medicine through integration of multi-omics data types; novel modeling and computational methods for biomarker research; public health genomics. I apply my biomedical informatics and analytical expertise to study diseases such as cancers, as well the impact of pregnancy/early life complications on later life diseases.