Arpan Kusari

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Dr. Arpan Kusari has joined UMTRI as an Assistant Research Scientist, a position where he will bring his cutting-edge industry experience. Dr. Kusari has spent five years at Ford Motor Company researching exclusively on making autonomous vehicles safe and viable, working collaboratively with researchers from MIT and University of Michigan to advance the state-of-the-art knowledge in autonomous vehicles. His research interest spans through the spheres of sensing and perception; and decision-making and control, in the domain of autonomous vehicles. In the sensing and perception realm, his interests lie in uncertainty quantification and fault tolerance of a generic sensor suite. Dr. Kusari is also interested in utilizing noise reduction methods for designing cost-effective low SNR (signal-to-noise ratio) LiDARS. In decision making and control, he is focused on creating a robust framework capable of handling the uncertainty stemming from other road users’ behavior. In that regard, Dr. Kusari is pursuing development of methods for increasing the efficiency and robustness of probabilistic formalisms such as reinforcement learning and evolutionary algorithms to safely navigate the dynamic environment. His doctoral research was in LiDAR mapping in the areas of sensor calibration, precise estimation of earthquake displacement and uncertainty quantification in the point cloud.

Xu Shi

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My methodological research focus on developing statistical methods for routinely collected healthcare databases such as electronic health records (EHR) and claims data. I aim to tackle the unique challenges that arise from the secondary use of real-world data for research purposes. Specifically, I develop novel causal inference methods and semiparametric efficiency theory that harness the full potential of EHR data to address comparative effectiveness and safety questions. I develop scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems and coding systems.

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.

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.