Assistant Professor, Statistics
Grand Valley State University
Biography
My name is Andrew DiLernia and I am an assistant professor in the Statistics Department at Grand Valley State University (GVSU). I grew up in Potterville, Michigan and spent my undergraduate years at GVSU. I attended the University of Minnesota - Twin Cities for my graduate studies and thoroughly enjoyed all the wonderful activities that the Twin Cities have to offer. In my free time I enjoy casually playing the piano, running, playing basketball, watching football, and most of all spending time with my cat Lei and significant other Lauren. Fall is by far my favorite season since it has the best weather and scenery of the year.
- Machine learning methods for neuroimaging data
- Predictive modeling
- Computing and data visualization
- Teaching
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Ph.D., Biostatistics, 2021
University of Minnesota - Twin Cities
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B.S., Mathematics and Statistics, 2016
Grand Valley State University
Experience
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(1) Research assistant:
• Developed and implemented novel machine learning methods for the analysis of high- dimensional neuroimaging data.
• Used remote Linux servers for parallel computing
• Analyzed time series data for physical activity classification using machine learning
• Produced analysis reports using RMarkdown for reproducible research.
• Created R packages for implementing novel statistical methods available via GitHub in R and C++
(2) Teaching assistant:
• Graded assignments, created assignment solutions, held office hours, and developed materials for the following courses: Advanced Longitudinal Data Analysis, Biostatistics Modeling and Methods, Exploring and Visualizing Data in R, Advanced Programming
and Data Analysis in R.
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Full instructor for the following courses:
(1) MAT 213: Data Visualization and Statistical Computing (Spring 2021):
• Use of R for data wrangling and manipulation, data visualization using ggplot2,
R Markdown, importing and combining data sets, web scraping, basics of natural
language processing, SQL, interactive visualizations, and dashboards.
(2) MAT 273: Statistical Models (Fall 2019 & 2020):
• Predictive modeling using R covering linear regression, logistic regression, and
time series analysis.
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• Automated the cleaning and analysis of data for a manure application survey using SAS
macros saving hours of weekly work
• Created report summarizing data from employee satisfaction surveys using SAS macros
for reproducibility.
• Updated the USDA website by performing maintenance of HTML links and uploading
weekly crop weather reports to the website.
• Used SAS, ArcGIS, and other programs to analyze and process data from weekly, monthly,
and annual surveys
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• Assisted students with courses covering SAS programming, SPSS, probability and
distribution theory, hypothesis testing, and descriptive
Publications
Certificates
- Data visualization using the ggplot2 package in R, and producing templates for use with multiple data sets
- Interactive graphics for rapid exploration of Big Data and also how to create simple web GUIs for managing complex summaries of biological data using R Shiny
- Use of R and R Markdown for producing reproducible documents
- Techniques for making large-scale data analyses reproducible
- Creating ’tidy data’, data retrieval, manipulation, and formatting
- Reproducible research using R Markdown and collaborative code sharing using GitHub