1. Distinguished Paper Award at CCS 2024 for the paper titled 'Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?'; 2. Published several papers, such as 'Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-ray, and Cholesterol Dataset'.
Research Experience
A Ph.D. candidate in the SAND Lab, involved in multiple research projects including the feasibility study of poisoning text-to-image AI models via adversarial mislabeling.
Education
1. Ph.D. in Computer Science, University of Chicago, 2028 (expected), Advisors: Prof. Ben Y. Zhao and Prof. Heather Zheng; 2. MS, Korea University, 2023; 3. BS, Korea University, 2021.
Background
Research Interests: Security and Privacy, Trustworthy Machine Learning/AI; Brief Introduction: A second-year Ph.D. candidate in Computer Science at the University of Chicago, aiming to develop models that can help protect users against malicious attacks. Passionate about enabling safer and more practical machine learning algorithms.
Miscellany
Attended various international conferences, including USENIX Security 2024, IEEE S&P 2024, CCS 2023, etc.