Brendan Kochunas

By |

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.

Relationship of concepts for the Digital Model, Digital Shadow, Digital Twin, and the Physical Asset using images and models of the Ford Nuclear Reactor as an example. Large arrows represent automated information exchange and small arrows represent manual data exchange.

Olga Yakusheva

By |

My research interests are in health economics and health services research; specifically econometric methods for causal inference, data architecture, and secondary analyses of big data. My primary focus is the study the work of nurses. I led the development of a new method for outcomes-based clinician performance productivity measurement using the electronic medical records. With this work, I was able to measure, for the first time, the value-added contributions of individual nurses to patient outcomes. This work has won her national recognition earning her the Best of AcademyHealth Research Meeting Award in 2014. I am is currently working to uncover traits and success strategies of highly-effective nurses, including education, experience, and expertise—and most recently smart clinician staffing approaches and innovation in the healthcare setting. I am a team scientist and contributed methodological expertise to many interdisciplinary projects including hospital readmissions, primary care providers, obesity, pregnancy and birth, and peer effects on health behaviors and outcomes. I am the Director of the Healthcare Innovation and Impact Program (HiiP) at the School of Nursing.

Using big data analytics to measure value-added contributions of nurses

Elizabeth F. S. Roberts

By |

“Neighborhood Environments as Socio-Techno-bio Systems: Water Quality, Public Trust, and Health in Mexico City (NESTSMX)” is an NSF-funded multi-year collaborative interdisciplinary project that brings together experts in environmental engineering, anthropology, and environmental health from the University of Michigan and the Instituto Nacional de Salud Pública. The PI is Elizabeth Roberts (anthropology), and the co-PIs are Brisa N. Sánchez (biostatistics), Martha M Téllez-Rojo (public health), Branko Kerkez (environmental engineering), and Krista Rule Wigginton (civil and environmental engineering). Our overarching goal for NESTSMX is to develop methods for understanding neighborhoods as “socio-techno-bio systems” and to understand how these systems relate to people’s trust in (or distrust of) their water. In the process, we will collectively contribute to our respective fields of study while we learn how to merge efforts from different disciplinary backgrounds.
NESTSMX works with families living in Mexico City, that participate in an ongoing longitudinal birth-cohort chemical-exposure study (ELEMENT (Early Life Exposures in Mexico to ENvironmental Toxicants, U-M School of Public Health). Our research involves ethnography and environmental engineering fieldwork which we will combine with biomarker data previously gathered by ELEMENT. Our focus will be on the infrastructures and social structures that move water in and out of neighborhoods, households, and bodies.

Testing Real-Time Domestic Water Sensors in Mexico City

Testing Real-Time Domestic Water Sensors in Mexico City

Fabian Pfeffer

By |

My research investigates social inequality and its maintenance across time and generations. Current projects focus on wealth inequality and its consequences for the next generation, the institutional context of social mobility processes and educational inequality in the United States and other industrialized countries. I also help expand the social science data infrastructure and quantitative methods needed to address questions on inequality and mobility. I serve as Principal Investigator of the Wealth and Mobility (WAM) study as well as Co-Investigator of the Panel Study of Income Dynamics (PSID). As such, my research draws on and helps construct nationally representative survey data as well as full-population administrative data. My methodological work has been focused on causal inference, multiple imputation, and measurement error.

Ivy F. Tso

By |

My lab researches how the human brain processes social and affective information and how these processes are affected in psychiatric disorders, especially schizophrenia and bipolar disorder. We use behavioral, electrophysiological (EEG), neuroimaging (functional MRI), eye tracking, brain stimulation (TMS, tACS), and computational methods in our studies. One main focus of our work is building and validating computational models based on intensive, high-dimensional subject-level behavior and brain data to explain clinical phenomena, parse mechanisms, and predict patient outcome. The goal is to improve diagnostic and prognostic assessment, and to develop personalized treatments.

Brain activation (in parcellated map) during social and face processing.

Meha Jain

By |

​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.

Conducting wheat crop cuts to measure yield in India, which we use to train algorithms that map yield using satellite data

Qing Qu

By |

His research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, he is also interested in understanding deep networks through the lens of low-dimensional modeling.

Trishul Kapoor

By |

Our research is focused on Post ICU pain syndromes (PIPS). PIPS exhibit distinct phenotypic presentations and can be predicted by intra-ICU parameters. Our primary goal is to be able to predict post-ICU opioid use based on intra-ICU parameters. We utilize a data-driven characterization of post-ICU pain syndromes will utilize unsupervised clustering algorithms including DBSCAN and spectral clustering. Prediction of post-discharge pain severity, likelihood of specific pain presentations, and post-discharge opioid use will be achieved using logistic LASSO, random forests, and neural networks. Specifically, these tests will utilize available ICU data to predict changes between pre-
and post-ICU pain severity, incidence of specific pain presentations, and incidence of opioid use.

This is a representation of enhancement of human cognition and clinical intelligence with artificial intelligence.

This is a representation of enhancement of human cognition and clinical intelligence with artificial intelligence.