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Robust AI for Real-World Digital Health

Robust AI for Real-World Digital Health

We create AI systems that are practical and resilient in real-world clinical settings, including mobile and resource-constrained environments. Our research emphasises lightweight models, few-shot learning, and seamless integration into digital health platforms.

Publications

  1. Mesinovic, Munib, et al. "Foundation model embeddings enable cardiovascular screening for people living with HIV in Vietnam using wearable signals." medRxiv (2025): 2025-10. paper
  2. Li, Chenqi, et al. "AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Feature Space-Guided Inversion." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 13. 2025. paper, code
  3. Zhu, Mingcheng, et al. "Bridging Data Gaps of Rare Conditions in ICU: A Multi-Disease Adaptation Approach for Clinical Prediction." arXiv preprint arXiv:2507.06432 (2025).  paper, code
  4. Zhang, Shuo, et al. "Student loss: Towards the probability assumption in inaccurate supervision." IEEE Transactions on Pattern Analysis and Machine Intelligence 46.6 (2024): 4460-4475. paper, code

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