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Accessing Grid Computing from Your Desktop for NLME

On-Demand Webinar

The performance and scalability of software and hardware always constrains a PK/PD modeler’s productivity. To improve performance of computationally intensive algorithms, Phoenix® NLME™ 7.0 jobs utilize parallel computing to take full advantage of computing resources. In this webinar, we will illustrate how parallel computing reduces analysis time and demonstrate how easy it is to submit jobs to Torque/SGE grids running on physical Linux and Amazon EC2 cloud. We will also share some tips and tricks for non-linear mixed effects modeling.

About Our Speakers

Webinar-3speaker-Soltanshahi-Thomashevskiy-GuzyFred Soltanshahi is a scientific software developer with Certara’s research group. He has been involved with algorithm design and scientific software development at Certara since 1990. His professional interests included PK/PD modeling, NLME, Trial Simulation, Parallel Computing, Molecular Modeling, Statistics, QSAR and hi-D visualization. He earned his Bachelors of Science in Computer Science from Southern Illinois University.

Dr. Michael Tomashevskiy is the member of the scientific group in the software business unit at Certara. He joined Certara in 2015 after working for 3 years as a research associate at a pharmaceutical company. His interests include using PK/PD models, parallel computing, linear models/bioequivalence and clinical trial simulation to aid decision-making. He earned his MD at the Russian Medical Academy of Postgraduate Education.

Serge Guzy, PhD, is currently a Pharmacometrics Professor Affiliate at the University of Maryland, Adjunct Professor at the University of Minnesota, Adjunct Professor at the University of Colorado, Adjunct Professor at UC Denver, President and CEO of POP_PHARM, and a Senior Consultant at Certara. Serge is the co-developer of the MCPEM algorithm, a popular algorithm that has been expanded into the QRPEM algorithm, a robust algorithm implemented in Phoenix for population analysis. Serge has written two book chapters on Pharmacometrics and more than 30 peer reviewed papers.

The performance and scalability of software and hardware always constrains a PK/PD modeler’s productivity. To improve performance of computationally intensive algorithms, Phoenix® NLME™ 7.0 jobs utilize parallel computing to take full advantage of computing resources.

In this webinar, we illustrated how parallel computing reduces analysis time and demonstrate how easy it is to submit jobs to Torque/SGE grids running on physical Linux and Amazon EC2 cloud. We also shared some tips and tricks for non-linear mixed effects modeling.

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