Nicholas Rios

Nicholas Rios is interested in a wide variety of research areas in the field of Statistics. His primary research focuses on experimental design in the presence of real-world constraints. He is also interested in functional data analysis, computational statistics, Gaussian process modeling, and models for compositional data analysis, with applications to chemical engineering and pharmaceutical industries.

Nicholas earned his PhD in Statistics at Penn State University in 2022. His dissertation was focused on designing optimal mixture experiments. In these experiments, multiple reagents and chemicals are mixed to produce a response. The dissertation addressed the practical issue of finding optimal experiments when the order of addition of the components was important. He developed novel algorithms for finding relatively cheap and efficient experiments that allow researchers to estimate the optimal mixture and order settings.

Nicholas has taught applied statistics (STAT554), experimental design (STAT455/517), and Statistical Inference (STAT 652) at GMU. He will also be teaching Foundations and Practice of Machine Learning for Artificial Intelligence (AII 600/STAT 689) in the Spring semester. He is very passionate about teaching students to think like statisticians. He challenges students to apply statistical thinking and methodology to analyze real-world data.  

Degrees

• PhD, Statistics, The Pennsylvania State University
• MS, Statistics, Montclair State University
• BS, Statistics, University of Delaware

Research Interests

• Experimental Design
• Optimal Subsampling
• Functional Data Analysis
• Computational Statistics
• Gaussian Process Modeling
• Models for Compositional Data Analysis