16 papers
MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms
Saiyang Feng, Yuanyun Zhang, Shi Li
Foundation models have recently emerged as a powerful paradigm for learning transferable representations from large scale biomedical data, yet existing approaches for physiological…
AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
Yuanyun Zhang, Shi Li
Recent healthcare foundation models have achieved strong predictive performance through large scale self supervised learning, yet their latent representations frequently entangle p…
WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records
Ruan Dong, Yuanyun Zhang, Shi Li
Representation learning in electronic health records (EHR) has largely followed paradigms inherited from natural language processing, relying on sequence modeling and reconstructio…
Event Fields: Learning Latent Event Structure for Waveform Foundation Models
Li Na, Yuanyun Zhang, Shi Li
We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time series as realizations of latent e…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Uncertainty-Aware Foundation Models for Clinical Data
Qian Zhou, Yuanyun Zhang, Shi Li
Healthcare foundation models have largely followed paradigms from natural language processing and computer vision, emphasizing large scale pretraining and deterministic representat…