SIGNALS Laboratory
Transforming physiological signals into actionable insight for human health
We develop computational approaches to understand how physiological signals—such as EEG, ECG, voice, and movement—reflect underlying health states.
Our work is driven by the idea that even sparse, noisy, and heterogeneous data contain meaningful structure. When modeled appropriately, these signals can enable early detection, forecast health trajectories, and inform personalized interventions across clinical and community settings.
Signals → Modeling → Health Trajectories → Clinical Decisions
Research
Physiological Signals as Health Indicators
Extracting clinically meaningful information from real-world data streams.
Forecasting Health Trajectories
Modeling how physiological systems evolve over time.
Clinical Decision Support
Designing scalable tools for primary care and hospital settings.
Computational Modeling & Digital Twins
Simulating and optimizing interventions using mechanistic and data-driven models.
Approach
We build end-to-end systems that connect data acquisition, infrastructure, modeling, and clinical use—shifting healthcare from reactive interpretation toward anticipatory, data-informed decision-making.
Collaboration
We collaborate across engineering, neuroscience, and clinical medicine, and welcome students and partners interested in physiological signals, cognitive health, and translational data science.