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Hello, I'm Nick and welcome to my GitHub page!

I recently graduated from UCLA with a Master of Science (M.S.) in Statistics and am currently looking for full-time data scientist/data analyst roles. I have experience in machine learning research in various domains, mostly in academic environments, and I previously earned a B.S. in Applied Mathematics at UCLA.

Most recently, I performed research on topics in generative AI at the Trustworthy AI Lab at UCLA. My research emphasized the methodology and application of synthetic data to improve modern statistical/ML models, particularly in terms of robustness, fairness, and differential privacy. Specifically, I investigated the paradigm of privacy auditing -- investigating the true privacy offered by different data synthesis methods, specifically under the risk of membership inference attacks and other attacks on generative models, in an adversarial framework.

Please feel free to check out my website (linked below) for some examples of my work from grad school and research. Thanks!

Please feel free to check out some of my other homes on the web:

[Website](https://nicklauskim.github.io)
[Kaggle](https://www.kaggle.com/nicklauskim)

nicklauskim's Projects

machine-learning-methods icon machine-learning-methods

Various implementations of some common machine learning algorithms in Python, including models from scratch and applied problems such as NLP tasks.

nba-shooting-analytics icon nba-shooting-analytics

Data science project exploring the various capabilities of Python libraries such as Numpy, Pandas, Matplotlib, and Seaborn. In the end, I use advanced shooting metrics to analyze the shooting tendencies and similarities of NBA players in the 2019-20 regular season and apply some basic unsupervised learning algorithms to find the most similar players.

nicklauskim.github.io icon nicklauskim.github.io

My professional website, displaying my projects and other work in statistics and data science.

old-website icon old-website

🐟 Nicklaus Kim's professional and academic website using Github Pages and Bay, a simple theme for Jekyll.

tabular-synthetic-data-privacy-auditing icon tabular-synthetic-data-privacy-auditing

Evaluating the privacy of tabular synthetic data generators using an adversarial toolbox, specifically the TAPAS toolbox as introduced in https://arxiv.org/abs/2211.06550. This code is used to produce the results presented in my master's thesis.

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