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Alan Schumitzky

Professor Emeritus of Mathematics

Contact Information
E-mail: schum@usc.edu
Phone: (213) 740-2392
Office: KAP 406G

LINKS
Curriculum Vitae
Faculty Profile on Departmental Website
 

Biographical Sketch

Link
 

Education

M.S. , Cornell University
Ph.D. , Cornell University
B.S. , Washington University
 

Description of Research

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.
 

Funded Research

Contracts and Grants Awarded

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

Other Funded Research

NIH Grants, On Sabbatical for the academic year 2007-2008. Was supported by grants listed under "Funded Research". Worked on important projects in "Optimizing Coordinated Drug Therapy" at the USC Medical School. Developed new software for "Analyzing Clinical Trials for Populations of Differing Pharmacogenetic Subjects" at the USC Bioengineering Department. Co-authored three research papers and one book (see "Publications")., $63,000, 2007-2008   
 

Publications

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.
 

New Courses Developed

Math 307 "Statistical Inference and Data Analysis I", Mathematics, Probability, counting, independence, distributions, random variables, simulation, expectation, variance, covariance, transformations, law of large numbers, Central limit theorem, estimation, efficiency, maximum likelihood, Cramer-Rao bound, bootstrap., 2010-2011   
 
 
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