[주요 경력]
Indiana University (2018 ~ 현재)
• 의과대학 조교수 (2024~ 현재)
• 의과대학 연구 조교수 (2018 ~ 2024)
University of Michigan (2015 ~ 2017)
• 컴퓨터 의학 및 생물정보학과 박사후 연구원
University of Missouri (2013 ~ 2015)
• 컴퓨터과학 박사후 연구원
일본 이화학 연구소 (2011 ~ 2013)
• 컴퓨터 생명과학 박사후 연구원
[주요 프로젝트 및 논문]
1. 딥러닝을 이용한 대사 물질 연구 (2023)
Taeho Jo, Junpyo Kim, Paula Bice, Kevin Huynh,
Tingting Wang, Matthias Arnold, Peter J. Meikle,
Rima Kaddurah-Daouk, Andrew J. Saykin*,
and Kwangsik Nho*, “Circular-SWAT for deep learning
based diagnostic classification of Alzheimer’s disease:
Application to metabolome data”, eBioMedicine, 2023.
2. 딥러닝을 이용한 유전 변이 연구 (2022)
Taeho Jo, Kwangsik Nho*,
Paula Bice, and Andrew J. Saykin*, “Deep learning-based
identification of genetic variants: Application to Alzheimer's
disease classification”, Briefings in Bioinformatics, 2022.
3. 딥러닝을 이용한 알츠하이머 원인 단백질 추적 (2020)
Taeho Jo, Kwangsik Nho, Shannon L Risacher, and
Andrew J Saykin*, “Deep learning detection of informative
features in tau PET for Alzheimer’s disease classification”,
BMC Bioinformatics, 2020.
4. 딥러닝을 이용한 신경 이미지 분류 (2019)
Taeho Jo*, Kwangsik Nho, and Andrew J Saykin,
“Deep Learning in Alzheimer's Disease: Diagnostic
Classification and Prognostic Prediction Using Neuroimaging Data”,
Frontiers in Aging Neuroscience, 2019
5. 딥러닝을 이용한 단백질 구조의 예측 (2015)
Taeho Jo, Jie Hou, Jesse Eickholt,
and Jianlin Cheng, “Improving Protein Fold Recognition
by Deep Learning”, Scientific Reports, 5:17573, 2015
[집필 및 강연활동]
• 저서
• 모두의 딥러닝 1판 (2017), 2판 (2020), 3판 (2022)
• 당신의 이유는 무엇입니까 (2020)
• 번역: 그림으로 이해하는 인지과학 (2022)
• 번역: 쉽게 시작하는 캐글 데이터 분석 (2021)
• 감수: 딥러닝 워크북 (2018)
• 외부 강연 및 컨설팅
방송/유튜브 (최근 3년)
• 2024 : 세상을 바꾸는 시간 15분
• 2022 : EBS-TV 비즈니스 리뷰 “AI와 알츠하이머”
• 2022 : EBS-TV 원데이 스쿨
• 2022 : 매불쇼 과학코너
• 2022-현재 : 유튜브 채널 “조태호의 AI + 생명과학” 운영
• 초청특강
• "Deep Learning Approaches in Alzheimer's Disease: Multi-Modal Biomarker Analysis and Classification",
2023 International Healthcare Symposium hosted by Ajou University Medical Center and the Graduate School of AI Convergence Innovation (2023)
• "Advanced Deep Learning Techniques in Alzheimer's Disease: Integrating Biomarker Analysis for Enhanced Diagnosis and Research",
Lecture at the Department of Bioinformatics and Medical Information, Seoul National University (2023)
• "From Perceptron to Chat-GPT: A Comprehensive Overview of Deep Learning Principles",
AIAI 2023 Summer School (2023)
• "Novel circular-SWAT for deep learning based diagnostic classification of Alzheimer’s disease: Application to metabolome data",
Elsevier, AI in Aging and Age-related Diseases conference (AAAD), China (2022)
• “A Dual-Deep Learning AI Strategy to Identify Tau-associate Genetic Variants in Alzheimer’s Disease”,
Indiana Alzheimer's Disease Research Center Fall Research Symposium, Indiana USA (2022)
• “Deep learning-based identification of genetic variants: Application to Alzheimer's disease classification”,
International Symposium Interdisciplinary Graduate Program in Medical Big data Convergence (2021)