Hello,

I am Prof. Eric A. Suess.  I am a faculty member in the Department of Statistics and Biostatistics.  I am also jointly appointed in the School of Engineering.

I was Department Chair until the Spring of 2015.  Since ending my 3 terms as Chair I have focused on the development and offering of courses related to Data Science.

I have designed and taught courses in R Programming, Data Visualization, Statistical/Machine Learning, Natural Language Programming (NLP), and Deep Learning, at both the graduate and undergraduate level.  As part of our transition to the Semester System, Fall 2018, I have designed three additional courses, R for Data Science, Applied Natural Language Processing (NLP), and Applied Deep Learning.

I have also continued to teach core Statistics courses such as Advanced Probability, Survey Sampling, SAS Programming, and Statistical Inference.

My current research interests continue to include Bayesian Statistics, Time Series Analysis, Applied Probability, Stochastic Processes, and Simulation.  Since ending my terms as Chair I have expanded my efforts into Data Visualization and Statistical/Machine Learning.  Other areas of interest include Natural Language Processing and Deep Learning.

My computing interests continue to be related to open source software.  I use Linux and BSD primarily, but still maintain my Windows skills.  I use R, Python, Julia, and SQL for data analysis.  I use RStudio every day.  I have been converting my data analysis efforts to using R Notebooks and Jupiter Notebooks for reproducible research.  For Data Visualization I use R with ggplot2 and other packages.  For Machine Learning I use R and Python.  For Deep Learning I have been using Tensorflow and Keras, and have been using an Nvidia 1070 and 1080 GPU.  I am interested in parallel processing and GPU computing.  I am also interested in distributed data storage, such as Hadoop and CrateDB.  I still use and teach using traditional software such as MS Excel, Minitab, SPSS, and SAS.

Since Fall 2016 I have been advising Engineering Management MS students on their Capstone Projects.  These projects relate to applications of Data Science in Engineering Management.  Topics include Time Series Forecasts, Natural Language Processing, Process Control, and other topics.

We offer the following degrees in our Department:

MS degree in Statistics, with options in Applied Statistics, Data Science, Mathematical Statistics, and Actuarial Science

MS degree in Biostatistics

BS degree in Statistics, with a concentration in Data Science, a Calcu-less degree, only one Calculus course is required, which is comparable to other science majors offered in the College of Science today

BS in Statistics

Minor in Statistics

Post-bac Certificates in Applied Statistics and Theoretical Statistics