| 7월 23일 (목) | |||
| 10:00 - 11:00 |
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| 11:00 - 12:00 |
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| 17:00 - 18:00 |
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| 7월 24일 (금) | |||
| 10:00 - 11:00 |
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| 11:00 - 12:00 |
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| 17:00 - 18:00 |
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황성재 교수
(연세대)
| Biography | |
|---|---|
| TBD | |
AI+의료 (60분)
TBD
이주용 교수
(서울대)
| Biography | |
|---|---|
| TBD | |
AI+Bio (신약개발) (60분)
TBD
최동희 교수
(부산대)
| Biography | |
|---|---|
| 2025-현재 | 부산대학교 정보컴퓨터공학부 조교수 |
| 2023-2025 | Imperial College London - Research Associate |
| 2022-2023 | Sony Research - Research Intern |
| 2018-2019 | LYZE, Inc 공동창업자 & Research Lead |
| 2016-2019 | Kono Labs 데이터 사이언티스트 |
| 2014-2016 | Opinion8 공동창업자 & CIO |
| 2023 | 고려대학교 컴퓨터학과 박사 |
| 2014 | 고려대학교 바이오협동과정 석사 |
| 2012 | 고려대학교 컴퓨터통신공학부 학사 |
| 2025-현재 | 한국정보과학회 데이터소사이어티 이사 |
Relation Extraction for Diet, Non-Communicable Disease and Biomarker Associations (60분)
Diet plays a critical role in human health, with growing evidence linking dietary habits to disease outcomes. However, extracting structured dietary knowledge from biomedical literature remains challenging due to the lack of dedicated relation extraction datasets. To address this gap, we introduce RECoDe, a novel relation extraction (RE) dataset designed specifically for diet, disease, and related biomedical entities. RECoDe captures a diverse set of relation types, including a broad spectrum of positive association patterns and explicit negative examples, with over 5,000 human-annotated instances validated by up to five independent annotators. Furthermore, we benchmark various natural language processing (NLP) RE models, including BERT-based architectures and enhanced prompting techniques with locally deployed large language models (LLMs) to improve classification performance on underrepresented relation types. The best performing model was gpt-oss-20B, a locally-deployed open-weight LLM, achieving an F1-score of 64% (macro) for multi-class classification and 92% for binary classification using a hierarchical prompting strategy with a separate reflection step built in. To demonstrate the practical utility of RECoDe, we introduce the Contextual Co-occurrence Summarisation (CoCoS) framework, which aggregates sentence-level relation extractions into document-level summaries and further integrates evidence across multiple documents. CoCoS produces effect estimates consistent with established dietary knowledge, demonstrating its validity as a general framework for systematic evidence synthesis.
김범준 교수
(KAIST)
| Biography | |
|---|---|
| TBD | |
AI+Robot (60분)
TBD
임성빈 교수
(고려대)
| Biography | |
|---|---|
| TBD | |
Generative AI for Causal Reasoning (60분)
TBD
김은솔 교수
(한양대)
| Biography | |
|---|---|
| TBD | |
Generative AI for Scientific Time Series (60분)
TBD