Eugene Demidenko, PhD, a professor of biomedical data science and of community and family medicine at Dartmouth’s Geisel School of Medicine, who also holds an adjunct appointment in the Mathematics Department at Dartmouth, has received the 2022 Ziegel Prize for his book Advanced Statistics with Applications in R.
The prestigious award is given each year by Technometrics, a quarterly journal co-published by the American Society for Quality and the American Statistical Association whose mission is to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences, as well as information sciences and technology.
The award was instituted in 2007 in honor of Eric Ziegel who served as book review editor of Technometrics from 1986-2006. Its criteria include that “ideally, the book will be one that brings together in one volume a body of material previously only available in scattered research articles and having the potential to significantly improve practice in engineering and science.”
“It means the world to me to be recognized in this way. I’ve been in the field of statistics for almost 50 years, and I’ve been a long-time teacher of statistics on various levels, so it is a culminating kind of experience for me,” says Demidenko, who worked on the book for more than 10 years.
Demidenko’s goal in writing Advanced Statistics is to fill the gap that exists between the many “recipe-style” applied statistics books and several theoretical statistics textbooks that have been published. “Basically, I wanted to bring the two main communities within the field together—those that use statistics as a utility and those who develop statistics, the theoreticians,” he says.
“Unfortunately, we are in a bit of a crisis—nowadays, computers have become so versatile and so good at crunching the numbers that people, especially students, are able to take shortcuts when reporting their results. But when they’re asked to interpret or explain what the numbers mean or what the limitations of the methods are, they’re unable to,” explains Demidenko.
“That’s what my book is intended to do—I hope it will be on the shelves of instructors around the world to help them teach statistics.”
Demidenko is a principal investigator of several statistics-driven NIH grants applying advanced statistical and machine learning techniques to solving ill-conditioned inverse problems. These include Laplace partial differential equation for electrical impedance tomography and the classification of bladder cancer cells using cell surface images via atomic force microscopy.
He is the author of the widely read book Mixed Models: Theory and Applications with R, published in 2013, and the paper “The p-Value You Can’t Buy,” published in The American Statistician in 2016, and has authored more than 150 papers in peer-reviewed journals.
In addition to teaching data science and statistics to graduate and PhD students in the Quantitative Biomedical Sciences Master’s Program at Geisel, using his Advanced Statistics as the textbook, Demidenko also teaches statistics to undergraduates in the Mathematics Department at Dartmouth College.
Demidenko earned his PhD in statistics and computer science from the Central Economics and Mathematics Institute of the Academy of Sciences in Moscow, USSR, in 1975. As a Soviet-era mathematician, he is proud to have had two of Russia’s most prominent statisticians, Sergey Aivazian and Valerii Fedorov, as members of the PhD thesis committee. Demidenko completed his MSD at Moscow Pedagogical University in 1971.
Founded in 1797, the Geisel School of Medicine at Dartmouth strives to improve the lives of the communities it serves through excellence in learning, discovery, and healing. The Geisel School of Medicine is renowned for its leadership in medical education, healthcare policy and delivery science, biomedical research, global health, and in creating innovations that improve lives worldwide. As one of America’s leading medical schools, Dartmouth’s Geisel School of Medicine is committed to training new generations of diverse leaders who will help solve our most vexing challenges in healthcare.