Jacqueline Asscher, Industrial Statistician
Chris Gotwalt, Chief Data Scientist at JMP
Moderator: Murat Caner Testik
Date: Dec. 6th, 3pm – 4pm CET
This session will provide a sneak peek into software development and product management of JMP, a long-time industry partner of ENBIS.
Jacqueline Asscher will talk to Chris Gotwalt, Chief Data Scientist at JMP, about how new key features like text analytics, generalized regression, functional data analysis or model screening make it into a new release every 18 months. Chris will explain how hundreds of ideas and requests – both internal and external – are reduced into a much shorter list of new product features. All attendees are invited to discuss how to better close the loops between researchers and educators in statistics and the users and developers of statistical software.
Jacqueline Asscher is a senior lecturer and consultant in industrial statistics, specializing in statistical design of experiments (DOE), statistical process control, sampling and data analysis. She works with big data challenges, e.g. sampling for updating machine learning models. She consults in a wide variety of industries, including semiconductor, pharmaceutical, medical devices, and agricultural equipment, and in academic research. She teaches in industry and at the Technion and Kinneret College, and develops case-based active learning methods. She is an avid JMP user, from Version 3.
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.