Dear Colleagues,The ASA Section on Statistics in Defense and National Security is pleased to announce the January webinar, presented by Dr. Chris Gotwalt from JMP on January 25th 2023.
For more information about the SDNS webinar series, please visit the ASA SDNS website or reach out to Elise Roberts (SDNS.AmStat@gmail.com).
Speaker: Dr. Chris Gotwalt, Chief Data Scientist at JMP
Title: Modeling Spectral Data using JMP Pro 17
Date: Wednesday, January 25th
Time: 2:00 – 3:30 PM Eastern / 11:00 AM – 12:30 PM Pacific
Registration (Zoom, free): SDNS Webinar Registration
Abstract: Curves and spectra are fundamental to understanding many scientific and engineering applications. As a result, curves or spectral data are created by many types of test and manufacturing equipment. When these data are used as part of a designed experiment or a machine learning application, most software requires the practitioner to extract features from the data prior to modeling. This leads to models that are more difficult to interpret and are less accurate than models that treat spectral/curve data as first-class citizens.
Chris Gotwalt, JMP Chief Data Scientist, will present an overview of functional data analysis in JMP Pro. His talk will focus on new capabilities in JMP Pro 17, such was wavelet analysis, designed to help statisticians of all levels analyze spectral data from NMR, mass spectroscopy, chromatography, and many other types of analysis common in the chemical, pharmaceutical, and biotech industries. He will explain how and why JMP Pro handles these data. The session also includes time for Q&A.
About the Speaker: Chris Gotwalt leads the statistical software development and testing teams for JMP Statistical Discovery. His passion is developing new technologies that accelerate innovation in industry and science. Since joining the company as a PhD student intern in 2001, Gotwalt has contributed many numerical algorithms and new statistical techniques. He has authored algorithms in JMP for fitting neural networks, linear mixed models, optimal design of experiments, analytical procedures for text analysis, and the algorithms for fitting structural equation models. Gotwalt is a principal investigator for Self-Validating Ensemble Models (SVEM), a procedure that makes machine learning possible for the small data sets often encountered in industry. He holds adjunct professorial positions at North Carolina State University, University of Nebraska and University of New Hampshire, and was the 2020 Chair of the Quality and Productivity Section of the American Statistical Association.