ZOOM Meeting ID: 832 6600 9463
https://snu-ac-kr.zoom.us/j/83266609463

초록: Electronic health records (EHR) contain valuable information regarding how patients are treated individually, as well as statistical insights on medical practice conducted by large hospitals. However, due to its complex structure and heterogeneous data modalities, non-database experts often have difficulties in utilizing such information. In this talk, we introduce question answering based on EHR, which enables users to interact with machines using natural language to retrieve information from the raw EHR. Furthermore, focusing on the multi-modality of EHR, we discuss how to handle such heterogeneity using multi-modal learning with Transformer architectures.

최윤재 교수
2020.03 ~ 현재: KAIST AI 대학원 조교수
2018.09. ~ 2020.02.: Google Brain, Software Engineer
2017.02. ~ 2017.08.: DeepMind, Google Research Intern
2010.02. ~ 2014.04.: 한국전자통신연구원 (ERTI) 연구원
박사, 2018.08. Georgia Tech, Computer Science
석사, 2009.08. KAIST, 전산학과
학사, 2008.08, 서울대학교, 컴퓨터공학과

주최: 데이터 과학 분야 인공지능 센터
지원: 서울대학교 AI연구원 선도혁신 연구센터 지원사업
문의: 이종진 조교(ga0408@snu.ac.kr)