FTC 2018 Short Courses

Short courses will be held on October 3, 2018.

Bayesian Statistics for Better Process Understanding & Prediction

Katherine Giacoletti, SynoloStats LLC

This course will provide an introduction to Bayesian statistical methodology for the applied statistician and, via examples and case studies, demonstrate how the use of such methods provides more precise process modeling and prediction than traditional analyses. The purpose of the course is not to provide instruction in programming, software use, or detailed computation, though it will include an overview of Bayesian methodology and some discussion of programming options. The learning objective is instead to provide the practitioner with a better understanding of the applicability of Bayesian statistics to manufacturing processes. Specifically, attendees will gain understanding to enable them to:

– Identify situations in which Bayesian methods may be of particular benefit

– Interpret the results (know how interpretation is different using Bayesian statistics as opposed to frequentist methods)

– Understand the implications of Bayesian methods for decision making.

The content is applicable to any process, but focus will be on applications in the pharmaceutical industry with examples from all three stages of the Process Lifecycle: Design, Qualification, and Ongoing Monitoring.

KATHERINE GIACOLETTI has worked as a statistician in the Pharmaceutical industry for many years and has expertise across the product lifecycle, from product and process development through validation and commercial supply, as well as all stages of clinical development from First in Human through licensure and beyond. She holds a Master of Statistics degree from North Carolina State University, with a focus in biostatistics, and before starting in Pharma she worked in survey research at the Research Triangle Institute. Katherine lives in the Philadelphia suburbs with her husband and daughter and 2 cats, and in her spare time is a dancer and a teacher of Scottish dancing.

21st-Century Design of Screening Experiments

Peter Goos, KU Leuven, Belgium

Many industrial experiments are screening experiments. In the past few decades, much innovative work has been done on screening experiments involving two levels per factor as well as screening experiments involving three levels per factor. As a result of all that research, many attractive two-level orthogonal arrays are now available as alternatives to the traditional two-level fractional factorial designs, and definitive screening designs, involving three levels per factor, have become available as well.

In this course, these new screening designs will be discussed in detail, and compared to the well-known classical alternatives. Attention will be paid to the existence of non-isomorphic orthogonal arrays and definitive screening designs. Also, the various criteria that can be used to evaluate and to compare the modern screening designs, such as generalized resolution and generalized aberration, will be introduced in an intuitive, graphical way. Therefore, a major goal of the course is to provide an accessible overview of the recent developments concerning nonregular orthogonal screening experiments. Finally, the course will also discuss the usefulness of optimal screening designs as alternatives to orthogonal screening designs.

PETER GOOS is a full professor at the Faculty of Bio-Science Engineering of the University of Leuven and at the Faculty of Applied Economics of the University of Antwerp, where he teaches various introductory and advanced courses on statistics and probability.His main research area is the statistical design and analysis of experiments. Besides numerous influential articles in various kinds of scientific journals, he published the books The Optimal Design of Blocked and Split-Plot Experiments, Optimal Experimental Design: A Case-Study Approach, Statistics with JMP: Graphs, Descriptive Statistics and Probability and Statistics with JMP: Hypothesis Tests, ANOVA and Regression. For his work, Peter Goos has received the Shewell Award and the Lloyd S. Nelson Award of the American Society for Quality, the Ziegel Award and the Statistics in Chemistry Award from the American Statistical Association, and the Young Statistician Award of the European Network for Business and Industrial Statistics (ENBIS).

Strategies for Formulations Development

      

Ronald D. Snee, Snee Associates LLC and Roger W. Hoerl, Department of Mathematics, Union College

This course will help scientists and engineers develop formulation recipes more quickly and efficiently. Good strategies are needed to get the right data in the right amount at the right time. Ron Snee and Roger Hoerl have worked on this problem for decades and have executed many different types of formulation and mixture experiments in a variety of industries. This experience has enabled them to identify what is essential for successful experimental designs, rather than what is just nice to know. Participants will be able to immediately apply what they have learned.

Key takeaways from the course include learning how to:

  • Approach formulation development from a strategic viewpoint, where the resulting experiments provide a roadmap for developing a successful formulation.
  • Focus on developing understanding about how components blend together.
  • Use designs and models that focus on finding dominant components with large effects.
  • Use screening experiments to identify components that are most critical for formulation performance. This strategy ensures that important components are not overlooked.
  • Analyze both screening and optimization experiments using graphical and numerical methods. The right graphics can extract additional information from the data.
  • Integrate formulation components with process variables in designs and models, using recently developed methods that reduce the required experimentation by up to 50%.

How to reduce mistakes, better meet deadlines, avoid wasted experimentation and reduce the cost of experimentation in terms of time, personnel and funds.

RONALD D. SNEE is president of Snee Associates LLC in Newark, DE. He has a doctorate in applied and mathematical statistics from Rutgers University in New Brunswick, NJ. Snee is an Honorary Member of ASQ and has received ASQ’s Shewhart, Grant and Distinguished Service Medals. He is an ASQ fellow and an academician in the International Academy for Quality.Ron worked at DuPont for 24 years in a variety of assignments including pharmaceuticals, statistical studies, manager of statistical, software and engineering consultants and process improvement prior to entering the consulting field.

ROGER W. HOERL is a Brate-Peschel assistant professor of statistics at Union College in Schenectady, NY. He has a doctorate in applied statistics from the University of Delaware in Newark. Hoerl is an ASQ fellow, a recipient of the ASQ’s Shewhart Medal and Brumbaugh Award and an academician in the International Academy for Quality.Roger worked at Hercules, Inc., Scott Paper Company, and at General Electric (GE) Plastics prior to joining the Union College faculty

Bridging Statistics and Data Science

          

Ming Li, Amazon and Hui Lin, DowDuPont

With recent big data revolution, enterprises ranging from FORTUNE 100 to startups across the US are using Data Science and Deep Learning to bring actionable business insight from all the valuable data collected. Statisticians are great data scientist candidates, but there are relatively few data scientists with statistics education background. This course aims to bridge the gap between Statistics and Data Science with the focus on big data platform and deep learning methods. Data science is a combination of science and art with data as the foundation. We will cover both the science part and the art part (such as data science project flow, general pitfalls in data science projects, and soft skills to effectively communicate with business stakeholders). The course will be hands-on and we will use the Databricks community edition cloud platform to illustrate how to apply big data (through Spark) and deep learning (using deep neural networks and convolutional neural networks) to real problems.

DR. MING LI  is currently a Senior Data Scientist at Amazon and an Adjunct Faculty of Department of Marketing and Business Analytics at Texas A&M University-Commerce. He was the Chair of Quality & Productivity Section of ASA for 2017. He was a Data Scientist at Walmart and a Statistical Leader at General Electric Global Research Center. He obtained his Ph.D. in Statistics from Iowa State University in 2010. With in-depth statistics background and a few years’ experience in data science, he has trained and mentored numerous junior data scientist with a different background such as statistician, programmer, software developer, database administrator and business analyst. He is also an Instructor of Amazon’s internal Machine Learning University and was one of the key founding members of Walmart’s Analytics Rotational Program which bridges the skill gaps between new hires and productive data scientists.

DR. HUI LIN is currently a Data Scientist at Netlify. She was a Data Scientist at DowDuPont where she had provided statistical leadership for a broad range of predictive analytics and market research analysis between 2013 and 2018. She is a co-founder of Central Iowa R User Group, blogger of scientistcafe.com and 2018 Program Chair of ASA Statistics in Marketing Section. She enjoys making analytics accessible to a broad audience and teaches tutorials and workshops for practitioners on data science. She holds MS and Ph.D. in statistics from Iowa State University, BS in mathematical statistics from Beijing Normal University.