Faculty Research Profile
Saerom Park
학력
· 2018: Ph.D. Industrial Engineering, Seoul National University
· 2013: B.S. Industrial Engineering, Seoul National University
주요 경력
· 2023~Present: Assistant Professor, Department of Industrial Engineering, UNIST
· 2020~Present: Outside Director, Kakao Corporation
· 2019~2023: Assistant Professor, Department of Convergence Security Engineering, Sungshin Women’s University
· 2018~2019: Researcher, Industrial and Mathematical Data Analytics Center (IMDARC), Seoul National University
수상/학회/외부활동
· 2019: The Grand Prize, WISET・KIIE
· 2014: Best Paper Award, APIEMS
· 2013: Best Graduate Award, Seoul National University Alumni Association
안전한 인공지능
Safe Artificial Intelligence
Our research is focused on addressing the interconnected challenges of privacy, fairness, and security to promote the safe use of artificial intelligence (AI) algorithms in real-world systems. Our goal is to develop innovative solutions that enable privacy-preserving, fairness-aware, and security-enhanced machine learning. To achieve this goal, we are pursuing two key problem thrusts:
(i) We are developing comprehensive approaches for reliable AI while considering security and privacy threats.
(ii) We are addressing the need for realistic threat models and evaluation for security-aware algorithms.
We are committed to advancing the field of artificial intelligence in a responsible and ethical manner. We believe that these three pillars are critical for building AI systems that are safe and beneficial for individuals, industry, and society.
Our research is focused on addressing the interconnected challenges of privacy, fairness, and security to promote the safe use of artificial intelligence (AI) algorithms in real-world systems. Our goal is to develop innovative solutions that enable privacy-preserving, fairness-aware, and security-enhanced machine learning. To achieve this goal, we are pursuing two key problem thrusts:
(i) We are developing comprehensive approaches for reliable AI while considering security and privacy threats.
(ii) We are addressing the need for realistic threat models and evaluation for security-aware algorithms.
We are committed to advancing the field of artificial intelligence in a responsible and ethical manner. We believe that these three pillars are critical for building AI systems that are safe and beneficial for individuals, industry, and society.
연구분야
Security-enhanced Machine Learning, Privacy-preserving Machine Learning, Fairness-aware Machine Learning
Security-enhanced Machine Learning, Privacy-preserving Machine Learning, Fairness-aware Machine Learning
연구 희망분야
Privacy evaluation for synthetic data, Practical attack models for artificial intelligence
Privacy evaluation for synthetic data, Practical attack models for artificial intelligence
연구주제
Safe Artificial Intelligence
- Security-enhanced Machine Learning
- Privacy-preserving Machine Learning
- Fairness-aware Machine Learning
Safe Artificial Intelligence
- Security-enhanced Machine Learning
- Privacy-preserving Machine Learning
- Fairness-aware Machine Learning
논문
· Saerom Park, Sungmin Kim* and Yeon-sup Lim, “Fairness Audit of Machine Learning Models with Confidential Computing”, The ACM Web Conference (WWW) 2022, Apr. 25-29, 2022
· Saerom Park and Jaewook Lee*. “Stability Analysis of Denoising Autoencoder based on Dynamical Projection System”, IEEE Transactions on Knowledge Data Engineering, Aug. 2021
· Sungyoon Lee, Jaewook Lee and Saerom Park*, “Lipschitz-Certifiable Training with a Tight Outer Bound”, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Dec. 7-12, 2020