I graduated from Pennsylvania State University in Statistics in 2022 with a Doctorate in Statistics. I was advised by Dr. Lingzhou Xue. I am a member of both the Statistical Learning and Data Mining (SLDM) Lab and Microbiome Data Science (MDS) Lab.

Previously, I attended The University of Chicago for a Master’s in Statistics and The University of Wisconsin at Madison for undergraduate degrees in Statistics, Computer Sciences, and Mathematics.

You can find my LinkedIn profile here.

My research interests primarily focus on:

  1. High-dimensional Statistics – With modern advancements in data processing and data collection, we often have data with many variables measured for a small number of samples. Classical statistical methods are often poorly suited for this data and require the development of novel methods.
  2. Human Microbiome Analysis – The human microbiome is the collection of all microbes that live in and on the human body. These microbes have been linked to changes in human health and the development of public health diseases. My research involves identifying the links between disease and these microbes.
  3. Statistical Reproducibility – A critical issue in the scientific community is the increased lack of study reproducibility. Common causes for poor reproducibility are the undue effect of outliers and false positive results. My work focuses on developing robust methods that guard against non-reproducible results.

Outside of the lab I’m an amateur improv comedian and cat aficionado. In my free time, I use my knowledge of statistics to figure out the most efficient way to annoy my players as a D&D Dungeon Master.

github gs