Xin Liu
Xin Liu is a Research Scientist at Google Consumer Health Research. He received his PhD in Computer Science from the University of Washington in 2023. His PhD research was supported by the Google PhD Fellowship .
His work is at the intersection of ubiquitous and mobile computing, machine learning, and health. In Xin's research, he studies how to enable mobile health + AI at scale, with a focus on building foundation models for sensor and consumer health data.
More information: https://xliucs.github.io/
Authored Publications
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SimPer: Simple Self-Supervised Learning of Periodic Targets
Yuzhe Yang
Jiang Wu
Dina Katabi
Ming-Zher Poh
Daniel McDuff
International Conference on Learning Representations (ICLR) (2023)
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From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes. Due to their inherent label scarcity, learning useful representations for periodic tasks with limited or no supervision is of great benefit. Yet, existing self-supervised learning (SSL) methods overlook the intrinsic periodicity in data, and fail to learn representations that capture periodic or frequency attributes. In this paper, we present SimPer, a simple contrastive SSL regime for learning periodic information in data. To exploit the periodic inductive bias, SimPer introduces customized augmentations, feature similarity measures, and a generalized contrastive loss for learning efficient and robust periodic representations. Extensive experiments on common real-world tasks in human behavior analysis, environmental sensing, and healthcare domains verify the superior performance of SimPer compared to state-of-the-art SSL methods, highlighting its intriguing properties including better data efficiency, robustness to spurious correlations, and generalization to distribution shifts.
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Large Language Models are Few-Shot Health Learners
Daniel McDuff
Geza Kovacs
Isaac Galatzer-Levy
Jake Sunshine
Jiening Zhan
Ming-Zher Poh
Shun Liao
Paolo Di Achille
Shwetak Patel
ArXiv (2023)
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Large language models (LLMs) can capture rich representations of concepts that are useful for real-world tasks. However, language alone is limited. While existing LLMs excel at text-based inferences, health applications require that models be grounded in numerical data (e.g., vital signs, laboratory values in clinical domains; steps, movement in the wellness domain) that is not easily or readily expressed as text in existing training corpus. We demonstrate that with only few-shot tuning, a large language model is capable of grounding various physiological and behavioral time-series data and making meaningful inferences on numerous health tasks for both clinical and wellness contexts. Using data from wearable and medical sensor recordings, we evaluate these capabilities on the tasks of cardiac signal analysis, physical activity recognition, metabolic calculation (e.g., calories burned), and estimation of stress reports and mental health screeners.
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