1. 일시: 2025년 3월 27일(목), 13:00-15:00 ​ 2. 장소: 판교 테크노밸리 산업수학혁신센터 세미나실 ​ 3. 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소 / 무료주차는 2시간 지원됩니다. ​ 4. 발표자: 김재경 교수(카이스트) ​ 5. 주제: Advancing Static and Time-series data: Random Matrix Theory, Causal Inference and Mathematical Modeling ​ In this talk, I will discuss methods for extracting meaningful information from static and time-series data. For static data, Principal Component Analysis (PCA) is widely used to detect signals in noisy datasets. However, determining the appropriate number of signals often relies on subjective judgment. I will introduce an approach based on random matrix theory to objectively select the optimal number of signals. For time-series data, causal inference techniques such as Granger causality are commonly employed. Unfortunately, these methods often yield high false-positive rates. I will present a novel mathematical model-based approach to causal inference. Finally, I will talk about how to use mathematical modelign approach to analyze time-series data with an example of wearable data.