Leading companies innovate with statistically designed  experiments

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Panel Discussion

Hear from Applied Materials, Lundbeck and Coherent 

 

Trying new things is foundational to innovation. If you work for an organization that is establishing new products or processes, or in an industry characterized by speed, precision or stiff competition, then you may have heard of design of experiments (DOE).

In this panel discussion, you’ll learn from seasoned practitioners and technologists who share real-world examples of how their companies have successfully used the transformative power of DOE to accelerate innovation, achieve faster, more predictable development cycles and problem solving, and save time and money.

You’ll hear from:

  • Melisa Buie, author of Problem Solving for New Engineers, on how she tackles complex engineering and business problems as General Manager at laser manufacturer Coherent.
  • Patti McNeill, on how she leads a team of scientists at Lundbeck to design and evaluate fermentation and cell culture experiments to create innovative medicines for people with brain diseases.
  • Sidharth Bhatia, on his R&D experiences as Director of Data Science and Analytics at Applied Materials, where his advanced analytics team drives high-volume semiconductor manufacturing.
  • Bradley Jones, Distinguished Research Fellow at SAS, on the latest modern experimental methods and how companies can drive innovation with these specialized techniques.

Preview the panel discussion

Case Study

Modern Design of Experiments for Rapid and Active Learning

 

Join Bradley Jones, Distinguished Research Fellow at SAS, as he demonstrates the practical applications of DOE and explains why leading industrial organizations use this methodology.  

You will learn more about:

  • How to experiment more efficiently and effectively. 
  • The different types of experiments and when it’s appropriate to use them.
  • The three fundamental concepts in DOE.
  • The limitations of classical designs and their consequences.
  • The importance of designing your experiment to fit your problem, rather than changing the problem to fit a textbook design.

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