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Caren Marzban

Principal Physicist

Lecturer, Statistics






B.S. Physics, Michigan State University, 1981

Ph.D. Theoretical Physics, University of North Carolina, 1988


2000-present and while at APL-UW

Mixture models for estimating maximum blood flow velocity

Marzban, C., G. Wenxiao, and P.D. Mourad, "Mixture models for estimating maximum blood flow velocity," J. Ultrasound Med., 35, 93-101, doi:10.7863/ultra.14.05069, 2016.

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1 Jan 2016

Objectives—A gaussian mixture model (GMM) was recently developed for estimating the probability density function of blood flow velocity measured with transcranial Doppler ultrasound data. In turn, the quantiles of the probability density function allow one to construct estimators of the “maximum” blood flow velocity. However, GMMs assume gaussianity, a feature that is not omnipresent in observed data. The objective of this work was to develop mixture models that do not invoke the gaussian assumption.

Methods—Here, GMMs were extended to a skewed GMM and a nongaussian kernel mixture model. All models were developed on data from 59 patients with closed head injuries from multiple hospitals in the United States, with ages ranging from 13 to 81 years and Glasgow Coma Scale scores ranging from 3 to 11. The models were assessed in terms of the log likelihood (a goodness-of-fit measure) and via visual comparison with the underlying spectrograms.

Results—Among the models examined, the skewed GMM showed a significantly (P< .05) higher log likelihood for 56 of the 59 patients and produced maximum flow velocity estimates consistent with the observed spectrograms for all patients. Kernel mixture models are generally less “robust” in that their quality is inconsistent across patients.

Conclusions—Among the models examined, it was found that the skewed GMM provided a better model of the data both in terms of the quality of the fit and in terms of visual comparison of the underlying spectrogram and the estimated maximum blood flow velocity. Nongaussian mixture models have potential for even higher-quality assessment of blood flow, but further development is called for.

Model tuning with canonical correlation analysis

Marzban, C., S. Sandgathe, and J.D. Doyle, "Model tuning with canonical correlation analysis," Mon. Wea. Rev., 142, 2018-2027, doi:10.1175/MWR-D-13-00245.1, 2014.

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1 May 2014

Knowledge of the relationship between model parameters and forecast quantities is useful because it can aid in setting the values of the former for the purpose of having a desired effect on the latter. Here it is proposed that a well-established multivariate statistical method known as canonical correlation analysis can be formulated to gauge the strength of that relationship. The method is applied to several model parameters in the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) for the purpose of "controlling" three forecast quantities: 1) convective precipitation, 2) stable precipitation, and 3) snow. It is shown that the model parameters employed here can be set to affect the sum, and the difference between convective and stable precipitation, while keeping snow mostly constant; a different combination of model parameters is shown to mostly affect the difference between stable precipitation and snow, with minimal effect on convective precipitation. In short, the proposed method cannot only capture the complex relationship between model parameters and forecast quantities, it can also be utilized to optimally control certain combinations of the latter.

Variance-based sensitivity analysis: Preliminary results in COAMPS

Marzban, C., S. Sandgathe, J.D. Doyle, and N.C. Lederer, "Variance-based sensitivity analysis: Preliminary results in COAMPS," Mon. Wea. Rev., 142, 2028-2042, doi:10.1175/MWR-D-13-00195.1, 2014.

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1 May 2014

Numerical weather prediction models have a number of parameters whose values are either estimated from empirical data or theoretical calculations. These values are usually then optimized according to some criterion (e.g., minimizing a cost function) in order to obtain superior prediction. To that end, it is useful to know which parameters have an effect on a given forecast quantity, and which do not. Here the authors demonstrate a variance-based sensitivity analysis involving 11 parameters in the Coupled Ocean%u2013Atmosphere Mesoscale Prediction System (COAMPS). Several forecast quantities are examined: 24-h accumulated 1) convective precipitation, 2) stable precipitation, 3) total precipitation, and 4) snow. The analysis is based on 36 days of 24-h forecasts between 1 January and 4 July 2009. Regarding convective precipitation, not surprisingly, the most influential parameter is found to be the fraction of available precipitation in the Kain%u2013Fritsch cumulus parameterization fed back to the grid scale. Stable and total precipitation are most affected by a linear factor that multiplies the surface fluxes; and the parameter that most affects accumulated snow is the microphysics slope intercept parameter for snow. Furthermore, all of the interactions between the parameters are found to be either exceedingly small or have too much variability (across days and/or parameter values) to be of primary concern.

More Publications


System and Methods for Tracking Finger and Hand Movement Using Ultrasound

Record of Invention Number: 47931

John Kucewicz, Brian MacConaghy, Caren Marzban


10 Jan 2017

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center