About the Reviewers
Richard Iannone is an R enthusiast and a very simple person. Those who know him (and know him well) know that this is indeed true. He has authored many R packages that have achieved great success. Those who have reviewed the code know that it possesses a je ne sais quoi essence to it. In any case, the code coverage is quite adequate (thanks to the many "test parties" he held), and he often offers builds that pass muster according to Travis CI.
Although he has a tendency toward modesty, others have remarked that he's just a straight shooter with upper management written all over him. You know what, we couldn't agree more. We bet you'll hear a lot more about him in the near future.
Hasan Kurban is a PhD candidate from the School of Informatics and Computing at Indiana University, Bloomington. He is majoring in Computer Science and minoring in Statistics. His main fields of interest are Data Mining, Machine Learning, Data Science, and Statistics. He also received his master's degree in Computer Science from Indiana University, Bloomington, in 2012. You can contact him at <hakurban@indiana.edu>.
Mahbubul Majumder is an assistant professor of statistics in the Department of Mathematics, the University of Nebraska at Omaha (UNO). He earned his PhD in statistics with specialization in data visualization and visual statistical inference from Iowa State University. He had the opportunity to work with some industries dealing with data and creating data products. His research interests include exploratory data analysis, data visualization, and statistical modeling. He teaches data science and he is currently developing a data science program for UNO.
Haichuan Wang holds a PhD degree in computer science from the University of Illinois at Urbana-Champaign. He has worked extensively in the field of programming languages and on runtime systems, and he worked in the R language and GNU-R system for a few years. He has also worked in the machine learning and pattern recognition fields. He is passionate about bringing R into parallel and distributed computing domains to handle massive data processing.
