Hamid Ghanbari

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My research focuses on using digital health solutions, signal processing, machine learning and ecological momentary assessment to understand the physiological and psychological determinants of symptoms in patients with atrial fibrillation. I am building a research framework for rich data collection using smartphone apps, medical records and wearable sensors. I believe that creating a multidimensional dataset to study atrial fibrillation will yield important insights and serve as model for studying all chronic medical conditions.

Zheng Song

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I received my second PhD in Computer Science (with a focus on distributed systems and software engineering) from Virginia Tech USA in 2020, and the first PhD (with a focus on wireless networking and mobile computing) from Beijing University of Posts and Telecommunications China in 2015. I received the Best Paper Award from IEEE International Conference on Edge Computing in 2019. My ongoing research projects include measuring the data quality of web services and using federated learning to improve indoor localization accuracy.

Matias del Campo

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The goal of this project is the creation of a crucial building block of the research on AI and Architecture – a database of 3D models necessary to successfully run Artificial Neural Networks in 3D. This database is part of the first stepping-stones for the research at the AR2IL (Architecture and Artificial Intelligence Laboratory), an interdisciplinary Laboratory between Architecture (represented by Taubman College of Architecture of Urban Planning), Michigan Robotics, and the CS Department of the University of Michigan. A Laboratory dedicated to research specializing in the development of applications of Artificial Intelligence in the field of Architecture and Urban Planning. This area of inquiry has experienced an explosive growth in recent years (triggered in part by research conducted at UoM), as evidenced for example by the growth in papers dedicated to AI applications in architecture, as well as in the investment of the industry in this area. The research funded by this proposal would secure the leading position of Taubman College and the University of Michigan in the field of AI and Architecture. This proposal would also address the current lack of 3D databases that are specifically designed for Architecture applications.

The project “Generali Center’ presents itself as an experiment in the combination of Machine Learning processes capable of learning the salient features of a specific architecture style – in this case, Brutalism- in order to generatively perform interpolations between the data points of the provided dataset. These images serve as the basis of a pixel projection approach that results in a 3D model.

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.

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.

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.