I am a Machine Learning Researcher at Carnegie Mellon University’s Software Engineering Institute, working on research projects to help create robust, secure, and scalable AI/ML systems. I graduated from Carnegie Mellon University with a major in Statistics and Machine Learning, and an additional major in Computer Science. I love all things data, machine learning, and artificial intelligence. I’ve been in to triathlons the past few years (I raced a half-Ironman!), and I also like playing chess, skiing, philosophy, and playing soccer, but it’s hard to pin me down to just one thing! You can find my resume here.

Publications

  • Casper, Stephen, Jieun Yun, Joonhyuk Baek, Yeseong Jung, Minhwan Kim, Kiwan Kwon, Saerom Park et al. “The SaTML’24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability.” arXiv preprint arXiv:2404.02949 (2024).
    • 2nd-Place Solution at the 2024 IEEE SaTML CNN Interpretability Competition
  • Grimes, Keltin, Collin Abidi, Cole Frank, and Shannon Gallagher. “Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning.” Extended Abstract. Deep Learning Security and Privacy Workshop (2024).

Presentations

  • AI Engineering and LLMs, SCADS (invited), June 17, 2024, with Jasmine Ratchford
  • Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning, IEEE S&P DLSP Workshop, May 23, 2024
  • Statistical Validation of Fuel Savings from In-Flight Data Recordings, DATAWorks, April 18, 2024