collaborators

10 papers

cs.AI2026

TRACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding

Sparsh Rastogi, Tanmay Kumar, Baiyu Chen +3

Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to activity labels. Recent LM-based…

cs.CL2026

RubricsTree: Scalable and Evolving Open-Ended Evaluation of Personal Health Agents across Health Memory and Medical Skills

Weizhi Zhang, Zechen Li, Hamid Palangi +16

The LLM-empowered personal health agents with user health (sensor) metrics have offered a promising pathway to alleviate global disparities in healthcare access. However, large-sca…

cs.LG2026

GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring

Zechen Li, Keerthana Natarajan, Weizhi Zhang +11

Continuous glucose monitoring (CGM) provides a dense view of daily metabolic physiology, yet existing generic time-series and CGM-specific foundation models often encode glucose tr…

cs.CV2026

AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild

Baiyu Chen, Zechen Li, Wilson Wongso +5

As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highl…

cs.AI2026

MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs

Ruiyi Yang, Zechen Li, Hao Xue +2

Self-evolving language-model agents must decide what to learn next and how to preserve what they have learned across iterations. Existing systems typically carry this cross-iterati…

cs.LG2026

CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining

Hada Melino Muhammad, Zechen Li, Flora Salim +1

Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; -cell dysfunction), but population-scale deployment faces two coupled prob…