Daniel W. Apley

Daniel W. Apley
CitizenshipAmerican
Known forStatistical process control
Data mining
Quality engineering
AwardsASA Fellow (2019)
NSF CAREER Award (2001)
Academic background
Alma materUniversity of Michigan
Doctoral advisorJianjun Shi
Jun Ni
Academic work
InstitutionsNorthwestern University
Texas A&M University
WebsiteFaculty Profile

Daniel W. Apley is an American statistician and engineer. He is a professor of industrial engineering and management sciences at Northwestern University. He is known for contributing to the fields of statistical modeling, machine learning, and quality engineering, particularly in the diagnosis and control of complex manufacturing systems.

Apley served as the editor-in-chief of the Journal of Quality Technology from 2009 to 2012 and of Technometrics from 2017 to 2019. He was elected a Fellow of the American Statistical Association in 2019.[1]

Education

Apley attended the University of Michigan, where he earned a Bachelor of Science (B.Sc.) and Master of Science (M.S.) in mechanical engineering in 1990 and 1992 respectively. Then in 1995 he earned a second M.S. in electrical engineering.[2] He completed his Doctor of Philosophy (Ph.D.) in mechanical engineering in 1997 under the supervision of Jianjun Shi and Jun Ni.[3]

Career

Following his doctoral studies, Apley served as an assistant professor at Texas A&M University from 1998 to 2003. He joined the faculty of Northwestern University in 2003 as associate professor and was subsequently promoted to full professor in the Department of Industrial Engineering and Management Sciences. He also served as the director of the Manufacturing and Design Engineering Program at Northwestern from 2004 to 2008.[3]

Apley has held significant leadership roles in major academic journals within the fields of statistics and quality engineering:

Research

Apley's research focuses on the interface of engineering modeling, statistical analysis, and data mining. His work addresses the challenges of data-rich manufacturing environments, specifically in the development of methods for statistical process control, fault diagnosis, and the analysis of simulation models.

He is also known for developing accumulated local effects (ALE) plots, a method for visualizing the effects of predictor variables in supervised learning ("black box") models. This method is considered an improvement over partial dependence plots when predictor variables are correlated.[4]

Awards and honors

Selected Publications

  • Apley, Daniel W.; Shi, Jianjun (1999). "The GLRT for statistical process control of autocorrelated processes". IIE Transactions. 31 (12): 1123–1134. doi:10.1080/07408179908969913.
  • Apley, Daniel W.; Lee, Hyun Cheol (2003). "Design of Exponentially Weighted Moving Average Control Charts for Autocorrelated Processes with Model Uncertainty". Technometrics. 45 (3): 187–198. doi:10.1198/004017003000000014.
  • Apley, Daniel W.; Zhu, Jingyu (2020). "Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models". Journal of the Royal Statistical Society: Series B (Statistical Methodology). 82 (4): 1059–1086. doi:10.1111/rssb.12377.

References

  1. ^ a b "Many Honored at Presidential Address and Awards Ceremony". Amstat News. 2019-10-01. Retrieved 2025-11-23.
  2. ^ a b c d "Faculty Directory: Daniel Apley". Northwestern University McCormick School of Engineering. Retrieved 2025-11-23.
  3. ^ a b c d Apley, Daniel W. "Curriculum Vitae" (PDF). Northwestern University. Retrieved November 27, 2025.
  4. ^ Apley, Daniel W.; Zhu, Jingyu (2020). "Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models". Journal of the Royal Statistical Society, Series B (Statistical Methodology). 82 (4): 1059–1086. doi:10.1111/rssb.12377.
  5. ^ "ASA Fellows". American Statistical Association. Retrieved 2025-11-23.