From perceiving the world to deciding what to do next: one model that connects sensors, language, and actions.

One general motion model, learned from a single wrist sensor, that transfers across placements, devices, and tasks.

A unified benchmark for evaluating hierarchical reasoning capabilities of LLMs over general health time series.

Real-world regression often exhibits shortcuts: attributes that are spuriously correlated with continuous targets in training, yet unreliable under deployment shifts; regressing targets using such sho...

Advancing scalable, multisensory, and generative AI for healthcare, medicine, and scientific discovery.

Polysomnography (PSG) provides the gold standard for sleep assessment but suffers from substantial heterogeneity across recording devices and cohorts. There have been growing efforts to build general-...

The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of health time series moda...

We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Despite the critical role of sleep, learning-...