Meet our Editor: Hoang Pham
Springer Handbook of Engineering Statistics is a definite reference work for engineers, analysts, and scientists from all fields. The new and updated edition of the book is a comprehensive place to look for methods and solutions to practical problems within - but not limited to - data science, quality assurance in design and production engineering.
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The first edition of the Springer Handbook of Engineering Statistics was published in 2006. In your opinion, why is this an important topic and in which fields within this area were updates and revisions particularly important?
There have been significant changes and developments over the past 15 years in the field of statistics including a big growth of machine learning aspect and the development of new tools for dealing with big data. These updates and revisions in the second edition are important for ensuring that researchers and professionals have the latest tools and methods to handle large and complex data sets in various applications such as healthcare, finance and marketing as well as to make informed decisions and draw conclusions based on their data observations.
What, in your opinion, has been the most significant advancement of the recent (last 15) years in this field and why? In how far is this reflected in the updates and revisions for the second edition?
One of the most significant advancements in engineering statistics field over the last 15 years has been the development of new tools and techniques include machine learning algorithms and methods for data representation, dimensionality reduction, and clustering for dealing with complex and high-dimensional data which can contain several hundreds of millions observations and thousands of features. Machine learning algorithms can learn patterns in data and make predictions based on those patterns. These techniques have been used in a wide range of applications from manufacturing and aerospace engineering to healthcare, and from finance to marketing.
This (2nd edition) Handbook of Engineering Statistics would be expected to reflect just that (the significant advancements in the field over the last 15 years), with new chapters on machine learning algorithms, Bayesian methods, optimization, model selection, statistical inference, life testing, predictive maintenance, and dimensionality reduction, as well as updates on the existing chapters from the first edition. These will provide researchers, students and professionals with the latest tools, methods and techniques in statistics for dealing complex large data and solving complex problems in various disciplines including engineering.
Which applications of engineering statistics do you, personally, find most exciting?
There are many exciting applications of engineering statistics. In the field of biomedical engineering for example, statistical methods include machine learning algorithms can be used to analyze medical imaging data, identifying patterns and features that can be used to diagnose diseases and predict treatment outcomes. The use of engineering statistics in various fields is to continue to grow greatly and evolve in the coming years as new tools and techniques are developed and applied in a wide range of applications especially in AI.
What are the biggest challenges for the field still ahead?
As data collection technology advances, one of the biggest challenges is how to effectively handle and analyze such big data sets. This requires developing new algorithms, techniques, and tools to process and extract meaningful information from these data sets.
Another challenge is about the model selection. Engineering and business applications often involve complex systems that require the use of sophisticated statistical models to make accurate predictions. The challenge is to identify the most appropriate model for a given application, while taking into account the trade-offs between model complexity and accuracy and decision-making.
These challenges will require continued innovation and collaboration among analysts and statisticians to address them and advance the field of engineering statistics.
Why did you decide to edit a handbook in Engineering Statistics?
Engineering Statistics is a field that combines statistical methods and engineering principles to solve real-world problems in a wide range of industries, from manufacturing to healthcare, and finance and business marketing.
Engineering statistics is an important field as the increasing demand for professionals with skills in data analysis and machine learning. As more and more industries are collecting large amounts of data, there is a growing need for professionals who can extract insights from these data sets and use them to drive decision-making.
What is the most exciting part of your current research?
My research interests lie in the intersection of reliability engineering and engineering statistics. Some exciting areas of research in engineering statistics today include machine learning, statistical inference and Bayesian statistics, and predictive analytics, among others. These areas are being applied to a wide range of applications in the fields of manufacturing, transportation, energy, and healthcare, with the potential to drive significant improvements in efficiency, quality, reliability, resilience and safety.
How would you describe the experience of editing the book?
Despite the challenging and time-consuming insurmountable task of editing this profound handbook especially during the COVID-19 pandemic, it has been indeed a rewarding experience knowing that the handbook is making a valuable contribution to the field in engineering statistics. Working together with a team of knowledgeable authors who are experts in their respective fields and gaining a deeper understanding of the field along the way and a team of Springer's editors to produce a comprehensive and significant reference for researchers, practitioners, professionals, and students have been my personal highlights during the process.
For whom is this book a “must-read”?
This handbook can be a valuable resource for a wide range of individuals from students to researchers to professionals who are interested in applying statistical methods and tools to solve various problems in healthcare, manufacturing, finance, business as well as to improve quality, reliability, resilience, efficiency, and safety.
Anything else you would like to add?
As data continues to become more abundant and accessible, the ability to analyze and interpret these data using statistical methods and tools will be increasingly important, making the field of engineering statistics including machine learning, AI, data science, and predictive analytics, a critical area of study for the future.
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