1 citations · 1 across the 3 of their papers we have counts for
5 papers
TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series
Aditya Radhakrishnan, Maxwell A. Xu, Wanting Mao +3
Contrastive learning for time series depends on deciding which pairs of inputs should be treated as similar and dissimilar. Because few augmentations reliably preserve meaning in t…
Variable Input Masked Autoencoders for Wrist-Worn Accelerometry
Harish Haresamudram, Wanting Mao, Aditya Radhakrishnan +2
Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing models are pretrained using…
How Well Do Multimodal Models Reason on ECG Signals?
Maxwell A. Xu, Harish Haresamudram, Catherine W. Liu +11
While multimodal large language models offer a promising solution to the "black box" nature of health AI by generating interpretable reasoning traces, verifying the validity of the…
Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals
Wanting Mao, Maxwell A Xu, Harish Haresamudram +3
Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide…
Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings
Mithun Saha, Maxwell A. Xu, Wanting Mao +3
Photoplethysmography (PPG)-based foundation models are gaining traction due to the widespread use of PPG in biosignal monitoring and their potential to generalize across diverse he…