Stefanus Jasin

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My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.


Negar Farzaneh

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Dr. Farzaneh’s research interest centers on the application of computer science, in particular, machine learning, signal processing, and computer vision, to develop clinical decision support systems and solve medical problems.

Brendan Kochunas

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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

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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

Ivy F. Tso

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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

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​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

Lubomir Hadjiyski

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Dr. Hadjiyski research interests include computer-aided diagnosis, artificial intelligence (AI), machine learning, predictive models, image processing and analysis, medical imaging, and control systems. His current research involves design of decision support systems for detection and diagnosis of cancer in different organs and quantitative analysis of integrated multimodality radiomics, histopathology and molecular biomarkers for treatment response monitoring using AI and machine learning techniques. He also studies the effect of the decision support systems on the physicians’ clinical performance.

Gen Li

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Dr. Gen Li is an Assistant Professor in the Department of Biostatistics. He is devoted to developing new statistical methods for analyzing complex biomedical data, including multi-way tensor array data, multi-view data, and compositional data. His methodological research interests include dimension reduction, predictive modeling, association analysis, and functional data analysis. He also has research interests in scientific domains including microbiome and genomics.

Novel tree-guided regularization methods can identify important microbial features at different taxonomic ranks that are predictive of the clinical outcome.

Wenbo Sun

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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.

J.J. Prescott

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Broadly, I study legal decision making, including decisions related to crime and employment. I typically use large social science data bases, but also collect my own data using technology or surveys.