Meha Jain

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‚ÄčI 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

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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

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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.

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.

Thomas L. Chenevert

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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.

Representative apparent diffusion coefficient (ADC) histograms and map overlays are shown for oncology trials to be supported by this Academic Industrial Partnership (AIP). ADC is used to characterize tumor malignancy of breast cancer, therapeutic effect in head and neck (H&N) and cellular proliferation in bone marrow of myelofibrosis (MF) patients. Relevant clinical outcome metrics are illustrated under histograms for detection sensitivity threshold (to reduce unnecessary breast biopsies (13)), Kaplan-Meier analysis of therapy response (stratified by median SD of H&N metastatic node (23)), and histopathologic proliferation stage (MF cellular infiltration classification).

Deena Costa

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Dr. Costa’s goal is to maximize survival and minimize morbidity for mechanically ventilated adults. She accomplishes this through her research on the organization and management of critical care. Specifically, her work identifies key structural and functional characteristics of ICU interprofessional teams that can be leveraged to improve the delivery of high quality, complex care to mechanically ventilated patients. She is a trained health services researcher with clinical expertise in adult critical care nursing. Her work care has been published in leading journals such as JAMA, Chest, and Critical Care Medicine. Her current research examines ICU teamwork and patient outcomes, linking individual clinicians to individual patients using the Electronic Health Record, and using qualitative approaches to understand how to improve ICU teams. Her research has focused on ICU clinician staffing, well-being and psychological outcomes of ICU clinicians as a way to improve care and outcomes of ICU patients.

Amie Gordon

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My research focuses on understanding the social cognitive, affective, and biological factors that shape our closest relationships. I am particularly interested in identifying factors that help romantic couples and families maintain high quality relationships. My work draws upon a variety of methods, including experimental, observational, naturalistic (e.g., daily experience), and physiological, to capture people at multiple levels in a variety of social situations. I frequently gather dyadic longitudinal data in order to understand how relationship partners influence each other in the moment and over time.

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.