Acknowledgements & References

Acknowledgements

A special thanks to the following people who helped to make this a success:

Dr. Ying Xie

Dr. Ying Xie is a Professor in the Colleage of Computing and Software Engineering at Kennesaw State University. His present research interests are primarily on deep learning and big data. Dr. Xie currently serves as the Director of Equifax Data Science Research Lab @KSU. His research was sponsored by multiple US companies and the National Science Foundation (NSF). Dr. Xie holds two US patents on data analytics and several pending US patents. He has been involved in organizing multiple international conferences and workshops on data mining and computational intelligence. Dr. Xie has also served in the editorial board of the Elsevier Journal of Big Data Research..

Bob Vanderheyden, PhD

In his 25 years of professional experience, Vanderheyden has often encountered issues with classifying ordinal data, which requires clean-up work before any analysis can take place. The approach can double or triple the amount of data created, therefore making it costly and time consuming. He is exploring how to utilize a deep neural network – a complex computational model that behaves like neurons in the human brain – to classify data rather than changing the parameters set to get the desired results. In theory, the network could automatically perform tasks statisticians have been doing manually for years, Vanderheyden said.

Jonathan Boardman

Data analyst and machine learning enthusiast familiar with multiple programming languages including Python, R, and SAS (SAS Certified Base Programmer for SAS 9). His goal is to work in predictive analytics, designing and implementing novel machine learning solutions to problems in science and business. Johnathan is currently seeking his Doctor of Philosopy - PhD, Analytics and Data Science at Kennesaw State University.

Shayan Shamskolahi

Shayan is a machine learning developer engaged in researching and implementing techniques that make the outcome of neural networks more explainable. His goal is to empower decision-makers to interpret the results of deep-learning algorithms and understand the basis for their decisions. His efforts address a variety of real-life problems by assisting professionals from healthcare and finance industries. Portfolio: http://myportfolio.ddns.net/