Xin Liu

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
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    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)
    Preview abstract 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. View details
    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)
    Preview abstract 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. View details