Alan Schumitzky

Summary Statement of Research Interests

Professor Schumitzky’s research interests are focused on estimation and control theory, applied pharmacokinetics, complex analysis,
and software development.

Research Keywords

Estimation and control theory, applied pharmocokinetics, complex analysis, software development.

Contracts and Grants Awarded

• Biomedical Simulations Resource, (NIH/NIBIH), D.Z. D’Argenio, V. Marmarelis, $38,000,000, 09/01/2003 – 08/31/2013

Book

• Tatarinova, T., Schumitzky, A. (2011). Nonlinear Mixture Models: A Bayesian Approach. London: Imperial College Press.
• Tatarinova, T., Schumitzky, A. (2009). Bayesian Analysis of Linear and Nonlinear Mixture Models. VDM Verlag.

Journal Article

• Yamada, W., Leary, R., Schumitzky, A. (2011). The Nonparametric Adaptive Grid Algorithm for Pharmacokinetic Population Modeling.
• Bayard, D. S., Schumitzky, A. (2010). Implicit Dual Control Based on Particle Filtering and Forward Dynamic Programming. International Journal on Adaptive Control and Signal Processing. Vol. 24 (3), pp. 155-177.
• Wang, X., Schumitzky, A., D’Argenio, D. Z. (2009). Population pharmacokinetic mixture models via maximum a posteriori estimation. Computational Statistics & Data Analysis. Vol. 53, pp. 3907-3915.
• Tatarinova, T., Schumitzky, A. (2008). Kullback-Leibler Markov Chain Monte Carlo – a new algorithm for finite mixture analysis and its application to gene expression data. Journal of Bioinformatics and Computational Biology. Vol. 6, pp. 727-735.
• Wang, X., Schumitzky, A., D’Argenio, D. Z. (2007). Nonlinear Random Effects Finite Mixture Models: Maximum Likelihood Estimation via the EM Algorithm. Computational Statistics & Data Analysis/Elsevier. Vol. 51 (12), pp. 6614-6623.

• Math 307 “Statistical Inference and Data Analysis I”, Mathematics, 2010-2011