Omid Dehzangi

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Wearable health technology is drawing significant attention for good reasons. The pervasive nature of such systems providing ubiquitous access to the continuous personalized data will transform the way people interact with each other and their environment. The resulting information extracted from these systems will enable emerging applications in healthcare, wellness, emergency response, fitness monitoring, elderly care support, long-term preventive chronic care, assistive care, smart environments, sports, gaming, and entertainment which create many new research opportunities and transform researches from various disciplines into data science which is the methodological terminology for data collection, data management, data analysis, and data visualization. Despite the ground-breaking potentials, there are a number of interesting challenges in order to design and develop wearable medical embedded systems. Due to limited available resources in wearable processing architectures, power-efficiency is demanded to allow unobtrusive and long-term operation of the hardware. Also, the data-intensive nature of continuous health monitoring requires efficient signal processing and data analytic algorithms for real-time, scalable, reliable, accurate, and secure extraction of relevant information from an overwhelmingly large amount of data. Therefore, extensive research in their design, development, and assessment is necessary. Embedded Processing Platform Design The majority of my work concentrates on designing wearable embedded processing platforms in order to shift the conventional paradigms from hospital-centric healthcare with episodic and reactive focus on diseases to patient-centric and home-based healthcare as an alternative segment which demands outstanding specialized design in terms of hardware design, software development, signal processing and uncertainty reduction, data analysis, predictive modeling and information extraction. The objective is to reduce the costs and improve the effectiveness of healthcare by proactive early monitoring, diagnosis, and treatment of diseases (i.e. preventive) as shown in Figure 1.

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Embedded processing platform in healthcare

Cancer Center, April Harris

Jeremy M G Taylor

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I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

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Naisyin Wang

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My main research interests involve developing models and methodologies for complex biomedical data. I have developed approaches in information extraction from imperfect data due to measurement errors and incompleteness. My other methodology developments include model-based mixture modeling, non- and semiparametric modeling of longitudinal, dynamic and high dimensional data. I developed approaches that first gauge the effects of measurement errors on non-linear mixed effects models and provided statistical methods to analyze such data. Most methods I have developed are so called semi-parametric based. One strength of such approaches is that one does not need to make certain structure assumptions about part of the model. This modeling strategy enables data integration from measurements collected from sources that might not be completely homogeneous. My recently developed statistical methods focus on regularized approach and model building, selection and evaluation for high dimensional, dynamic or functional data.

Regularized time-varying ODE coefficients of SEI dynamic equation for the Canadian measles incidence data (Li, Zhu, Wang, 2015). Left panel: time-varying ODE coefficient curve that reflects both yearly and seasonal effects with the regularized yearly effect (red curve) embedded; right panel: regularized (red curve), non-regularized (blue) and two-year local constant (circles) estimates of yearly effects. The new regularized method shows that the yearly effect is relatively large in the early years and deceases gradually to a constant after 1958.

Regularized time-varying ODE coefficients of SEI dynamic equation for the Canadian measles incidence data (Li, Zhu, Wang, 2015). Left panel: time-varying ODE coefficient curve that reflects both yearly and seasonal effects with the regularized yearly effect (red curve) embedded; right panel: regularized (red curve), non-regularized (blue) and two-year local constant (circles) estimates of yearly effects. The new regularized method shows that the yearly effect is relatively large in the early years and deceases gradually to a constant after 1958.