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Eric A. Suess, Ph.D. Faculty Profile

Photo of Eric Suess

Eric  A.  Suess, Ph.D.

Professor

Department of Statistics and Biostatistics

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 Data Visualization and Statistical Learning, at both the graduate and undergraduate level.  As part of our transition to the Semester System Fall 2018, I have designed three more courses, R for Data Science, Applied Natural Language Processing 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 and Python 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 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, this program has been recognized as a Profession Science Master (PSM)

BS degree in Statistics

Minor in Statistics

Post-bac Certificates in Applied Statistics and Theoretical Statistics

My professional interests in Statistics include:

Data Science, Data Visualization, Machine Learning, Natural Language Processing, Deep Learning, Bayesian Statistics, Time Series Analysis, Applied Probability, Statistics Education

Open source software interests include:

Linux (Majaro and Debian and OpenSUSE), BSD (TrueOS and OpnSense), OpenWRT, R, Python, markdown, TensorFlow, Keras, CrateDB

  • PhD Statistics, UC Davis, 1998
  • MS Statistics, CSU Hayward, 1993
  • BA Statistics and Economics, UC Berkeley, 1991
Spring Semester 2019
Course #SecCourse TitleDaysFromToLocationCampusTextbook Info
ENGR 69001Independent StudyARRARRHayward Campus View Books
STAT 45201Intro Stat LearningMW6:30PM7:45PMSC-S146Hayward Campus View Books
STAT 49002Independent StudyARRARRHayward Campus View Books
STAT 65201Statistical LearningMW12:00PM1:40PMSC-S146Hayward Campus View Books
STAT 65202Statistical LearningMW8:00PM9:40PMSC-S146Hayward Campus View Books
STAT 65301Stat. Natural Language Proc.MW8:00PM9:40PMSC-S146Hayward Campus View Books
STAT 65401Intro to Applied Deep LearningMW12:00PM1:40PMSC-S146Hayward Campus View Books
STAT 69002Independent StudyARRARRHayward Campus View Books
STAT 69802InternshipARRARRHayward Campus View Books
STAT 69803InternshipARRARRHayward Campus View Books

In 2010, my collegue, Bruce Trumbo and I published our book,

Introduction to Probability Simulation and Gibbs Sampling with R (Use R)

Here is a link to our webpage for the book.  http://www.sci.csueastbay.edu/~esuess/psgs/

Here is a link to our book on amazon.  psgs on amazon

For a further list of papers and presentations, please see the Research page on my university website.

I have served on many committees in the university during my time here at CSUEB.  I severed on the Semester Conversion College of Science Curriculum Committee. I am on all Departmental committees as a faculty member of the Statistics Department.  I have served on the College Computer Advisory Committee.  I have served on CAPR, the Library Advisory Committee, COBRA, and UPABC.

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