Mixture Experiments Using Machine Learning: A How-To Approach
The focus of this course is on designing and analyzing mixture experiments that enable you to predict and optimize system characteristics, which are critical to product and process design and quality.
Build Better and Faster with Small Experimental Designs and Machine Learning
This webinar centers on Self-Validating Ensemble Modeling (SVEM), an easy-to-use, new modeling technique that results in a greater ability to accurately predict complex systems that conventional methods smooth over.
Design of Experiments for Mixtures Using Machine Learning
This course presents a revolutionary yet proven approach to the design of experiments for mixtures based on modern machine learning methods. It reduces sample size requirements, streamlines the process of analyzing and modeling & improves accuracy.
For over 25 years Predictum has been privileged to partner with JMP. We have collaborated on course development and delivery across industries. This partnership continues with these online courses.
Phil is a Senior Data Scientist and Statistical Consultant at Predictum. He provides consulting services in data science, statistics, and machine learning for integrated analytical systems, custom projects, and training. He specializes in modern experimental design and analysis strategies and the use of statistics, data science, and machine learning in engineering and science. In addition, Phil is a Professor in the Department of Mathematics and Statistics at the University of New Hampshire (UNH) where he teaches courses at the undergraduate and graduate levels in design of experiments, machine learning, and statistical methods for quality improvement. He has held the following relevant industrial positions: Senior Engineer for Materials and Processes Development, McDonnell Douglas, St. Louis, MO; Staff Scientist/Statistician, Alcoa Technical Center, Pittsburgh, PA; and Statistician/Senior Engineer, Rohm & Haas Electronic Materials (now Dow), Marlboro, MA. Phil holds a Ph.D. in statistics from Virginia Polytechnic Institute and State University.Email
Marie is a Senior Data Scientist at Predictum. She specializes in predictive modeling, design of experiments, statistics, and machine learning. She has extensive experience in consulting and statistical training in a wide variety of industries. Marie is Professor Emerita of Statistics at the University of New Hampshire (UNH), where she has worked extensively with students and companies on the practical application of statistics. She is also a co-author of two books, one of which is about the use of JMP software and statistical methods to improve quality and the other is about the partial least squares technique. She was also a statistical writer for several years as a member of the JMP documentation team at SAS Institute. Marie holds a Ph.D. in Statistics from the University of Massachusetts at Amherst.Email
Cy Wegman is a Senior Analyst at Predictum. He specializes in data analytics and empirical modeling and optimization. Before joining Predictum, he worked for 38 years at Procter & Gamble, where his last assignment was the Empirical Modeling leader for the company. Cy is a member of the P&G Prism Society, which represents the top 1% of P&G engineers, for profitably applying his technical mastery. His contributions have resulted in hundreds of millions of dollars in annual savings. His cross-industry work in manufacturing, engineering, and R&D has spanned molecular-scale to full-scale technologies, as well as material, formulation, process, packaging, and consumer models. Cy holds a B.S. in Civil Engineering from Rose-Hulman Institute of Technology.Email
Asit Rairkar is a Senior Analyst at Predictum. He specializes in Design of Experiments, process and product development, and operations scale-up. He has over 18 years of experience in companies ranging from small startups such as MiaSolé to technology giants such as Apple, Applied Materials, and Lam Research. At Apple, he led two large projects in battery engineering, including building up its largest, on-site, battery-testing lab. At MiaSolé, a former thin-film solar start-up, he led efforts in process development, and the commissioning and running of its 24/7 pilot plant. In addition, Asit is also an adjunct faculty member in Electrical Engineering at California Polytechnic University and has been a board-member and vice-chair for a non-profit organization. Asit holds a PhD in Materials Science and Engineering from Arizona State University, a BS in Engineering from University of Pune, India and several specialization certificates in data science, machine learning, and deep learning.Email
Bernd is a Senior Data Scientist at Predictum, specializing in predictive modeling, design of experiments, and machine learning. Prior to joining Predictum, Bernd worked as a JMP Systems Engineer at SAS Institute with various responsibilities in global sales and technical enablement for over 10 years. For many years, he had an active IT consultancy business, providing business process design, system development and project management services to his customers. He began his career as a biometrician, consulting with applied scientists on their research and development work in the fields of medicine and agriculture. Bernd has also evaluated and supported registrations for new commercial and consumer products in the chemical industry. In corporate training, he has developed and delivered on-site and online statistical courses, tailored to the needs of engineers and scientists and their relevant application areas, for public and private clients. He is delighted to hear occasionally from participants who say they never before could have imagined that "applying statistics could be fun." Bernd holds a M.Sc. in Statistics from TU Dortmund University, Germany.Email
Wayne Levin is the President and CEO at Predictum. He has provided executive leadership to the Predictum team for over 30 years. He is passionate about delivering robust and reliable analytical solutions to improve productivity and accelerate operational performance and innovation in engineering, science companies, and research to companies in various industries. Wayne also leads statistical training workshops and presents at speaking engagements. Wayne has forged a strong and longstanding partnership with JMP, a business unit of SAS Institute, as a major JMP customization and training partner. Wayne earned a B.A.Sc. in Industrial Engineering from the University of Toronto, and an M.A.Sc. in Engineering with a focus on research methods from the University of Waterloo.Email