邮箱：tongxia@mail.tsinghua.edu.cn
夏彤，博士，助理教授，博士生导师。研究主要关注数智赋能的健康监测, 尤其是基于移动与可穿戴设备的健康监测与管理。
个人主页：https://xtxiatong.github.io/
教育经历：
2013-2017 武汉大学电子信息学院，电子信息工程专业学士
2017-2020 清华大学电子系， 电子与通信工程硕士
2020-2024 英国剑桥大学计算机系，计算机科学博士
工作经历：
2023.9-2024.5 英国剑桥大学计算机系，助理研究员
2024.5-2025.7 英国剑桥大学计算机系，博士后副研究员
2025.8 – 至今 清华大学万科公共卫生与健康学院，助理教授
研究领域：
1) 面向健康预测的可信机器学习
2) 面向健康推理的基础模型、多模态大模型、大模型智能体
3) 移动与可穿戴设备的数据建模以及健康应用
【课题组长期招收科研助理，欢迎有相似研究兴趣的申请者邮件沟通】
学术兼职：
IEEE Pervasive Computing编委
Frontiers in Digital Health副主编
Lancet Regional Health-Europe、Nature Scientific Data等期刊审稿人
NeurIPS、KDD、AAAI、UbiComp、ICASSP等会议审稿人
ACM UbiComp 2022 Poster and Demo Session Chair
社会兼职：
全英清华校友会副秘书长、宣传组组长（2021-2025.7）
荣誉奖励：
亚洲校长论坛工程领域女性学术新星 (2024)
剑桥大学名人堂更美好未来奖（2021）
剑桥大学华为学者全额博士奖学金（2020-2023）
清华大学优秀硕士毕业论文、优秀硕士毕业生（2020）
学术成果（部分）：
Liu, Y., Tao, W., Xia, T., Knight, S. & Zhu, T. 2025. SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis. Proceedings of the 31st ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pp. 1903-1914.
Tang, J., Xia, T., Lu, Y., Mascolo, C. & Saeed, A. 2025. Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5.
Zhang, Y., Xia, T.* & Mascolo, C. 2024. FedEE: Uncertainty-Aware Personalized Federated Learning for Realistic Healthcare Applications. Proceedings of the 4th Machine Learning for Health Symposium (ML4H) , PMLR 259:1067-1086.
Zhang, Y., Xia, T.*, Saeed, A. & Mascolo, C. 2024. RespLLM: Unifying audio and text with multimodal LLMs for generalized respiratory health prediction. Proceedings of the 4th Machine Learning for Health Symposium (ML4H) , PMLR 259:1053-1066.
Zhang, Y.^, Xia, T.^, Han, J., Wu, Y., Rizos, G., Liu, Y., Mosuily, M., Chauhan, J. & Mascolo, C. 2024. Towards open respiratory acoustic foundation models: Pretraining and benchmarking. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 37, pp. 27024–27055.
Xia, T., Ghosh, A., Qiu, X. & Mascolo, C. 2024. FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation. Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pp. 3484-3494.
Xia, T., Dang, T., Han, J., Qendro, L. & Mascolo, C. 2024. Uncertainty-aware health diagnostics via class-balanced evidential deep learning. IEEE Journal of Biomedical and Health Informatics (JBHI), 28(11), pp. 6417–6428.
Xia, T., Li, Y., Qi, Y., Feng, J., Xu, F., Sun, F., Guo, D. & Jin, D. 2023. History-enhanced and uncertainty-aware trajectory recovery via attentive neural network. ACM Transactions on Knowledge Discovery from Data (TKDD), 18(3), p. 22.
Dang, T., Han, J., Xia, T., Bondareva, E., Siegele-Brown, C., Chauhan, J., Grammenos, A., Spathis, D., Cicuta, P. & Mascolo, C. 2023. Conditional Neural ODE Processes for Individual Disease Progression Forecasting: A Case Study on COVID-19. Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) , pp. 3914-3925.
Xia, T., Han, J., Ghosh, A. & Mascolo, C. 2023. Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1-5.
Han, Z., Xia, T., Xi, Y. & Li, Y. 2023. Healthy Cities: A comprehensive dataset for environmental determinants of health in England cities. Scientific Data, 10(1), p. 165. Nature Publishing Group UK.
Feng, T., Song, S., Xia, T. & Li, Y. 2023. Contact tracing and epidemic intervention via deep reinforcement learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 17(3), p. 24.
Feng, T., Xia, T., Fan, X., Wang, H., Zong, Z. & Li, Y. 2022. Precise mobility intervention for epidemic control using unobservable information via deep reinforcement learning. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 2882–2892.
Dang, T., Han, J., Xia, T., Spathis, D., Bondareva, E., Siegele-Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A. & Floto, R.A. 2022. Exploring longitudinal cough, breath, and voice data for COVID-19 progression prediction via sequential deep learning: model development and validation. Journal of Medical Internet Research (JMIR), 24(6), p. e37004. JMIR Publications.
Han, J.^, Xia, T.*^, Spathis, D., Bondareva, E., Brown, C., Chauhan, J., Dang, T., Grammenos, A., Hasthanasombat, A. & Floto, A. 2022. Sounds of COVID-19: exploring realistic performance of audio-based digital testing. NPJ Digital Medicine, 5(1), p. 16. Nature Publishing Group UK.
Xia, T.^, Spathis, D.^, Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Bondareva, E., Dang, T. & Floto, A. 2021. COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening. Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS) , pp.1-13.
Zhang, Y., Xu, F., Xia, T. & Li, Y. 2021. Quantifying the causal effect of individual mobility on health status in urban space. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp), 5(4), p. 30.
Xia, T., Qi, Y., Feng, J., Xu, F., Sun, F., Guo, D. & Li, Y. 2021. Attnmove: History enhanced trajectory recovery via attentional network. Proceedings of the AAAI Conference on Artificial Intelligence(AAAI), 35(5), pp. 4494–4502.
Xia, T., Li, Y., Feng, J., Jin, D., Zhang, Q., Luo, H. & Liao, Q. 2020. DeepApp: Predicting personalized smartphone app usage via context-aware multi-task learning. ACM Transactions on Intelligent Systems and Technology (TIST), 11(6), pp. 1–12.
Brown, C.^, Chauhan, J.^, Grammenos, A.^, Han, J.^, Hasthanasombat, A.^, Spathis, D.^, Xia, T.^(equal-contribution, alphabetical order), Cicuta, P. & Mascolo, C. 2020. Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pp. 3474–3484.
Xia, T. & Li, Y. 2019. Revealing Urban Dynamics by Learning Online and Offline Behaviours Together. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp), 3(1), p. 30.
Xia, T., Yu, Y., Xu, F., Sun, F., Guo, D., Jin, D. & Li, Y. 2019. Understanding Urban Dynamics via State-sharing Hidden Markov Model. Proceedings of the ACM World Wide Web Conference (WWW), pp. 3363–3369.
