Director of High Performance Computing and Machine Learning in the Financial Risk division, SAS
Henry Bequet is Director of High Performance Computing and Machine Learning in the Financial Risk division of SAS. In that capacity, he leads the development of a high-performance solution that can run SAS code on thousands of CPU and GPU cores for advanced models that use techniques like Black-Scholes, Binomial Evaluation, and Monte-Carlo simulations. Henry has more than 35 years of industry experience and 15 years of high-performance analytics practice. He has published two books and several papers on server development and machine learning.
By This Author
Deep Learning for Numerical Applications with SAS®
By Henry G. Bequet
Dive into deep learning! Written by an expert in high-performance analytics, Deep Learning for Numerical Applications with SAS® introduces a new field: Deep Learning for Numerical Applications (DL4NA). It introduces deep learning concepts in SAS along with step-by-step techniques that allow you to easily reproduce the examples on your high-performance analytics systems. It also discusses the latest hardware innovations that can power your SAS programs from many-core CPUs to GPUs to FPGAs to ASICs.
- Kim Chantala is a Programmer Analyst in the Research Computing Division at RTI International with over 25 years of experience in managing and analyzing research data. Before joining RTI International, she was a data analyst at the University of North Carolina at Chapel Hill.
- Ron Cody, EdD, a retired professor from the Robert Wood Johnson Medical School now works as a private consultant and a national instructor for SAS Institute.
- Michele M. Burlew designed and programmed SAS applications for data management, data analysis, report writing, and graphics for academic and corporate clients for over 40 years. She has expertise in many SAS products and operating systems.