Welcome to the Wearable AI Lab at San José State University. Our lab develops intelligent, reliable, and equitable AI systems for real-world health monitoring using wearable and mobile sensor technologies. We study how AI and machine learning models can transform raw time-series sensor data into meaningful health insights that support behavioral understanding, early risk detection, and personalized interventions.

Our research focuses on wearable sensing, mobile health (mHealth), human activity recognition, and trustworthy AI for biomedical applications. Unlike controlled laboratory datasets, real-world sensor data is noisy, incomplete, and highly variable. We investigate how to design robust machine learning pipelines that address missingness, sensor drift, bias, and deployment variability. By combining statistical modeling, deep learning, visualization-driven AI, and responsible computing principles, we aim to build systems that are both scientifically rigorous and socially responsible.

We develop new computational methods for modeling multimodal wearable data, including wrist-worn inertial sensors, physiological signals, and mobile interaction traces. Our work includes generative modeling for sensor augmentation, fairness-aware evaluation in healthcare datasets, temporal modeling of behavioral signals, and interpretable AI approaches for health monitoring. Through these efforts, we seek to advance scalable digital health technologies that function reliably in everyday life.

Our lab is highly collaborative and interdisciplinary. We work with researchers across AI, data science, health sciences, and engineering, and we actively mentor undergraduate and graduate students in cutting-edge research at the intersection of AI and health. Student-led projects frequently result in peer-reviewed publications and conference presentations.

We are committed to advancing wearable AI systems that are not only accurate, but also fair, transparent, and impactful for real-world communities.