Nicholas Douville

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Dr. Douville is a critical care anesthesiologist with an investigative background in bioinformatics and perioperative outcomes research. He studies techniques for utilizing health care data, including genotype, to deliver personalized medicine in the perioperative period and intensive care unit. His research background has focused on ways technology can assist health care delivery to improve patient outcomes. This began designing microfluidic chips capable of recreating fluid mechanics of atelectatic alveoli and monitoring the resulting barrier breakdown real-time. His interest in bioinformatics was sparked when he observed how methodology designed for tissue engineering could be modified to the nano-scale to enable genomic analysis. Additionally, his engineering training provided the framework to apply data-driven modeling techniques, such as finite element analysis, to complex biological systems.

Jing Sun

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My areas of interest are control, estimation, and optimization, with applications to energy systems in transportation, automotive, and marine domains. My group develops model-based and data-driven tools to explore underlying system dynamics and understand the operational environments. We develop computational frameworks and numerical algorithms to achieve real-time optimization and explore connectivity and data analytics to reduce uncertainties and improve performance through predictive control and planning.

Nicole Seiberlich

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My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.

We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.

Kathleen Sienko

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Age- and sensory-related deficits in balance function drastically impact quality of life and present long-term care challenges. Successful fall prevention programs include balance exercise regimes, designed to recover, retrain, or develop new sensorimotor strategies to facilitate functional mobility. Effective balance-training programs require frequent visits to the clinic and/or the supervision of a physical therapist; however, one-on-one guided training with a physical therapist is not scalable for long-term balance training preventative and therapeutic programs. To enable preventative and therapeutic at-home balance training, we aim to develop models for automatically 1) evaluating balance and, 2) delivering personalized training guidance for community dwelling OA and people with sensory disabilities.

Smart Phone Balance Trainer

Reza Soroushmehr

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Dr. Soroushmehr’s research interests include the design and development of image processing methods applicable to computer-assisted clinical decision support systems, algorithm design and optimization.

Yulin Pan

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My research is mainly concerned with theoretical and computational hydrodynamics, with applications in nonlinear ocean wave prediction and dynamics, wave-body interactions, and wave turbulence theory. I have incorporated the data science tools in my research, especially in the following two projects:

1. Quantification of statistics of extreme ship motions in irregular wave fields: In this project, we propose a new computational framework that directly resolves the statistics (and causal factors) of extreme ship responses in a nonlinear wave field. The development leverages a range of physics and learning based approaches, including nonlinear wave simulations (potential flow), ship response simulations (e.g., CFD), dimension-reduction techniques, sequential sampling, Gaussian process regression (Kriging) and multi-fidelity methods. The key features of the new approach include (i) description of the stochastic wave field by a low-dimensional probabilistic parameter space, and (ii) use of minimum number of CFD simulations to provide most information for converged statistics of extreme motions.

2. Real-time wave prediction with data assimilation from radar measurements: In this project, we develop the real-time data assimilation algorithm adapted to the CPU-GPU hardware architecture, to reduce the uncertainties associated with radar measurement errors and environmental factors such as wind and current in the realistic ocean environment. Upon integration with advanced in-situ or remote wave sensing technology, the developed computational framework can provide heretofore unavailable real-time forecast capability for ocean waves.

Christopher E. Gillies

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I am Research Faculty with the Michigan Center for Integrative Research in Critical Care (MCIRCC). Our team builds predictive algorithms, analyzes signals, and implements statistical models to advance Critical Care Medicine. We use electronic healthcare record data to build predictive algorithms. One example of this is Predicting Intensive Care Transfers and other Unforeseen Events (PICTURE), which uses commonly collected vital signs and labs to predict patient deterioration on the general hospital floor. Additionally, our team collects waveforms from the University Hospital, and we store this data utilizing Amazon Web Services. We use these signals to build predictive algorithms to advance precision medicine. Our flagship algorithm called Analytic for Hemodynamic Instability (AHI), predicts patient deterioration using a single-lead electrocardiogram signal. We use Bayesian methods to analyze metabolomic biomarker data from blood and exhaled breath to understand Sepsis and Acute Respiratory Distress Syndrome. I also have an interest in statistical genetics.

Harm Derksen

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Current research includes a project funded by Toyota that uses Markov Models and Machine Learning to predict heart arrhythmia, an NSF-funded project to detect Acute Respiratory Distress Syndrome (ARDS) from x-ray images and projects using tensor analysis on health care data (funded by the Department of Defense and National Science Foundation).

Neda Masoud

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The future of transportation lies at the intersection of two emerging trends, namely, the sharing economy and connected and automated vehicle technology. Our research group investigates the impact of these two major trends on the future of mobility, quantifying the benefits and identifying the challenges of integrating these technologies into our current systems.

Our research on shared-use mobility systems focuses on peer-to-peer (P2P) ridesharing and multi-modal transportation. We provide: (i) operational tools and decision support systems for shared-use mobility in legacy as well as connected and automated transportation systems. This line of research focuses on system design as well as routing, scheduling, and pricing mechanisms to serve on-demand transportation requests; (ii) insights for regulators and policy makers on mobility benefits of multi-modal transportation; (ii) planning tools that would allow for informed regulations of sharing economy.

In another line of research we investigate challenges faced by the connected automated vehicle technology before mass adoption of this technology can occur. Our research mainly focuses on (i) transition of control authority between the human driver and the autonomous entity in semi-autonomous (level 3 SAE autonomy) vehicles; (ii) incorporating network-level information supplied by connected vehicle technology into traditional trajectory planning; (iii) improving vehicle localization by taking advantage of opportunities provided by connected vehicles; and (iv) cybersecurity challenges in connected and automated systems. We seek to quantify the mobility and safety implications of this disruptive technology, and provide insights that can allow for informed regulations.

Kevin Dombkowski

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Kevin J. Dombkowski, DrPH., MS, is Research Professor with the Child Health Evaluation and Research (CHEAR) Center within the University of Michigan Department of Pediatrics.   He is a health services researcher working extensively with public health information systems and large administrative claims databases.  

Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions.  Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems.  Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.  Kevin is currently collaborating with Michigan State University on the design and development of the Flint Registry information architecture.

Kevin’s research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems.  He has led numerous collaborations with the Michigan Department of Health and Human Services (MDHHS), including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR).  Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic. Kevin is currently collaborating with MDHHS to evaluate MCIR data quality as immunization providers across Michigan adopt real-time, bi-directional messaging between electronic health records (EHRs) and MCIR.  As PI of a CDC-funded project, Kevin is evaluating the costs and benefits of electronic interoperability between EHRs and MCIR. He is also conducting a statewide evaluation of blood lead testing result data reported by electronic laboratory systems to MDHHS.