Lecturers: Chris Gotwalt and Phil Kay
Duration: 4 hours
In product and process design there are many situations where you want to optimize something that is best thought of as a curve. There are many examples: stability/degradation curves, the many varieties of spectral data, shear/viscosity curves, and force/distance curves, to name a few. When this data is 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 modelling. Using metrics like the mean, peak height, or a threshold crossing point leads to models that are more difficult to interpret and are less accurate than models that treat spectral/curve data as first-class citizens in their own right.
JMP Pro makes it easy to directly model your curve or spectral data in designed experiments and machine learning applications. This hands-on workshop with JMP Chief Data Scientist, Chris Gotwalt, will be a unique opportunity to learn about functional data analysis in JMP Pro. All participants will get free access to pre-install JMP Pro 17 and are invited to bring their Windows or Mac computers to try the latest capabilities, such was wavelet analysis, that make it easier than ever to analyze spectral data from NMR, mass spectroscopy, chromatography, and many other types of analysis common in the chemical, pharmaceutical, and biotech industries. Pre-knowledge in statistics, functional data or JMP are not required.
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.
As a learning manager for JMP, Phil Kay loves showing people how data analytics enables better science. His passion for this started with some big successes using statistical design of experiments as a development chemist at FujiFilm. Phil has a master’s degree in applied statistics and a master’s and PhD in chemistry. He is a chartered chemist and chair of the RSC process chemistry and technology interest group.