Eric A. Suess

Eric A. Suess is a Professor in the Department of Statistics and Biostatistics at California State University East Bay, with a joint appointment in the College of Engineering. He served as Department Chair until Spring 2015, after which he focused on developing and teaching courses related to Data Science, Machine Learning, and AI.

His educational background isn't explicitly stated in the provided text, but he holds a Ph.D. and has extensive experience in academia.

Professor Suess's research interests span a wide range of statistical and computational fields. His primary areas include Bayesian Statistics, Time Series Analysis, Applied Probability, Stochastic Processes, and Simulation. Since 2015, he has expanded his focus to include Data Visualization, Statistical/Machine Learning, Natural Language Processing, and Deep Learning. As of Fall 2023, he has been experimenting with Large Language Models for applied NLP problems, and since Spring 2024, he has been working with open-source small language models (SMLs) from ollama. His work demonstrates a consistent progression from traditional statistical methods to cutting-edge computational approaches.

His scholarly publications show a strong emphasis on computational statistics, Bayesian methods, and practical applications across various domains including meteorology, environmental science, and public health. His most recent work focuses on precipitation forecasting accuracy, analysis of coronavirus events using hierarchical Bayesian models, and air quality monitoring systems.

• JSM 2018 Vancouver, Data Competition Winner for "Accuracy of Precipitation Forecasts"
• JSM 2018 Vancouver, First Prize for poster presentation "Classroom Demonstration: Deep Learning for Classification and Prediction"
• JSM 2011 Miami, Third Prize for "Effect of Oil Spill on Birds: A Graphical Assay of the Deepwater Horizon Oil Spills Impact on Birds"

Professor Suess has been advising Engineering Management MS students on Capstone Projects related to Data Science applications since Fall 2016. These projects cover topics including Time Series Forecasts, Natural Language Processing, Process Control, and other data science applications in engineering management. He has also co-authored the book "Introduction to Probability Simulation and Gibbs Sampling with R" published by Springer-Verlag in 2010, which has become a valuable resource for students and practitioners.

His department offers several degree programs including MS in Statistics (with options in Applied Statistics, Data Science, Mathematical Statistics, and Actuarial Science), MS in Biostatistics, and BS in Statistics (with a concentration in Data Science). He has been instrumental in developing the Data Science curriculum at CSUEB.