학술행사
- 발표자 김재경 교수(카이스트)
- 개최일시 2025-03-27 13:00-15:00
- 장소 국가수리과학연구소 산업수학혁신센터(판교)
유튜브 스트리밍 예정입니다.
- 일시: 2025년 3월 27일(목), 13:00-15:00
- 장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소 / 무료주차는 2시간 지원됩니다.
- 발표자: 김재경 교수(카이스트)
- 주제: Advancing Static and Time-series data: Random Matrix Theory, Causal Inference and Mathematical ModelingIn 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.
유튜브 스트리밍 예정입니다.
- 일시: 2025년 3월 27일(목), 13:00-15:00
- 장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소 / 무료주차는 2시간 지원됩니다.
- 발표자: 김재경 교수(카이스트)
- 주제: Advancing Static and Time-series data: Random Matrix Theory, Causal Inference and Mathematical ModelingIn 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.
