Dennis K.J. Lin Delivers Prestigious Deming Lecture at JSM - Department of Statistics - Purdue University Skip to main content

Dennis K.J. Lin Delivers Prestigious Deming Lecture at JSM


Professor Dennis K.J. Lin, the new head for the Department of Statistics, delivered this year’s prestigious Deming Lecture at the Joint Statistical Meetings (JSM) this summer.

The Deming Lecturer Award was established in 1995 by the American Statistical Association (ASA) to honor the accomplishments of Dr. W. Edwards Deming, to enhance awareness among the statistical community of the scope and importance of Deming’s contributions, and to recognize the accomplishments of the awardee.

As announced by ASA, Professor Lin won the award “for significant contributions to major policy decisions of diverse industries around the globe and contributions to the American Society for Quality. Like Deming, Professor Dennis Lin has been a true ambassador of statistics to business and industry and has inspired many.” Congratulations to Professor Lin for this prestigious honor!

Since JSM was held virtually this year for the first time in its 160 year history, Lin’s lecture, “If Deming Were Born Today -- Quality, Statistics in Modern Data Science”, was also presented virtually. The abstract and link to his recorded lecture can be found below.

Link to the lecture recording:

Abstract:  If Francis Bacon were born today, I could well imagine that he might have said “Data is power” instead of his original saying, “Knowledge is power.” Data is everywhere. When data speaks, do you understand what data is trying to tell you? In memory of Dr. Deming (a guru in quality), this talk attempts to address the fundamental quality issue in modern data science. Specifically, I will attempt to explain what exactly Data Science really means, what the impact of Data Science is to the real world, and the data quality (the so-called “Bad Data”) issue. Statisticians have much more to contribute to both the intellectual vitality and the practical utility of Data Science. At the same time, Data Science challenges statisticians to move out of some familiar habits to engage less structured problems, to become more comfortable with ambiguity, and to engage more scientists in a fruitful discussion on what various parties can bring to this new mode of investigation. Some potential avenues for future research along this direction will be proposed.

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