個人簡介
劉曉鴻，于清華大學計算機科學與技術(shù)系獲得本科和博士學位，長期圍繞醫(yī)學人工智能開展研究。近年來在相關(guān)領(lǐng)域取得了一系列重要研究成果，在Nature Medicine、Nature Biomedical Engineering、Nature Genetics、Cell等高水平期刊上發(fā)表論文多篇。主持國家自然科學基金青年科學基金項目（B類）、青年科學基金項目（C類）。
主要研究方向
醫(yī)學大語言模型和智能體，醫(yī)學圖像理解和生成，生物多組學大數(shù)據(jù)融合和分析，生物分子相互作用模擬和預測
代表性成果等
[1] X. Liu#, H. Liu#, G. Yang#, et al. A generalist medical language model for disease diagnosis assistance. Nature Medicine 2025
[2] G. Wang#*, X. Liu#, Z. Ying#, G. Yang, et al. Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial. Nature Medicine 2023
[3] G. Wang#*, X. Liu#, K. Wang#, Y. Gao#, G. Li#, D.T. Baptista-Hon#, et al. Deep-learning-enabled protein–protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution. Nature Medicine 2023
[4] G. Wang#*, K. Wang#, Y. Gao#, L. Chen, T. Gao, Y. Ma, Z. Jiang, G. Yang, F. Feng, S. Zhang, Y. Gu, G. Liu, L. Chen, L.-S. Ma, Y. Sang, Y. Xu*, G. Lin*, and X. Liu*. A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning. Patterns (2024).
[5] Y. Yang, X. Liu*, T. Gao, X. Xu, P. Zhang, and G. Wang*. Dense Contrastive-based Federated Learning for Dense Prediction Tasks on Medical Images. IEEE journal of biomedical and health informatics 2024
