Tong Xia, Assistant Professor

Dr. Tong Xia is an Assistant Professor and Ph.D. supervisor. Her research focuses on intelligent health monitoring empowered by data and AI, particularly for those health monitoring and management applications based on mobile and wearable devices.

Profile: https://xtxiatong.github.io/

Education

· 2013–2017: B.Eng. in Electronic Information Engineering, School of Electronic Information, Wuhan University

· 2017–2020: M.Eng. in Electronics and Communication Engineering, Department of Electronic Engineering, Tsinghua University

· 2020–2024: Ph.D. in Computer Science, Department of Computer Science and Technology, University of Cambridge, UK

Professional Experience

· Sep. 2023 – May 2024: Research Associate, Department of Computer Science and Technology, University of Cambridge

· May 2024 – Jul. 2025: Research Associate (Postdoctoral), Department of Computer Science and Technology, University of Cambridge

· Aug. 2025 – present: Assistant Professor, Vanke School of Public Health, Tsinghua University

Research Interests

1. Trustworthy machine learning for health prediction

2. Foundation models, multimodal large models, and intelligent agents for health reasoning

3. Mobile and wearable devices data modeling and health applications

(The research group is continuously recruiting research assistants. Applicants with similar research interests are welcome to get in touch via email.)

Academic Service

· Executive Committee Member, Pervasive Computing Technical Committee, China Computer Federation (CCF)

· Editorial Board Member, IEEE Pervasive Computing

· Associate Editor, Frontiers in Digital Health

· Reviewer for journals including The Lancet Regional Health – Europe, Nature Scientific Data, etc.

· PC for conferences including NeurIPS, KDD, AAAI, UbiComp, ICASSP

· Poster and Demo Session Chair, ACM UbiComp 2022

Community Service

· Deputy Secretary-General and Head of Communications, Tsinghua Alumni Association in the UK (UKTA) (2021–Jul. 2025)

Honors and Awards

· Rising Stars in Women in Engineering, Asian Presidents’ Forum (2024)

· Better Future Award, University of Cambridge Hall of Fame (2021)

· Huawei–Cambridge Scholarship for Ph.D. Studies (Full Funding, 2020–2023)

· Outstanding Master’s Thesis Award and Outstanding Graduate, Tsinghua University (2020)

Selected publications

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 30^th 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 29^th 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 26^th 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.