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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. 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
  2. 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
  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

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