I am pursuing a Master's degree in Applied Statistics at the University of Michigan, starting in Fall 2024.
I have a strong passion for multivariate analysis and graphical models, which I developed during my undergraduate studies in Statistics.

Here, I have summarized the projects I have worked on and am currently working on.

Here’s a collection of several projects I worked on during my undergraduate studies and now as a master's student. The projects are in R, Python, and SAS files. They are also linked to my personal website.

• R programming

  • Statistical ERGM analysis for consulting company network data: Examined the factors influencing advice-giving relationships within a consulting company's workplace network using the Exponential Random Graph Model (ERGM) to provide insights for improving workplace network efficiency and communication. Code
  • Analyzing HR Data using Machine Learning: Analyzed HR data from 14,999 employees to identify the key factors influencing turnover using various machine learning algorithms such as Logistic Regression, Decision Trees, Support Vector Machines, Random Forests, and Artificial Neural Networks.
  • Multivariate Analysis of Diabetes in 2015:

• SAS programming

  • Predicting the Index Trend of 'KOSPI' through Time Series Analysis:

• Python programming

  • Fine-Tuning BERT for Fake News Detection: Developed a transformer-based BERT(Bidirectional Encoder Representations from Transformers) model that efficiently classified real and fake news articles, leveraging a dataset of 72,134 news titles and applying key techniques such as preprocessing, text cleaning, tokenization, padding, and hyperparameter tuning.