Dr. Gong is an Associate Professor of Computer Science in the College of Engineering and the Director of Sensor-Accelerated Intelligent Learning Laboratory (SAIL Lab). Remarkably, Artificial Intelligence (AI) technologies in various research fields are speeding up multiple digital revolutions, from shifting paradigms in healthcare, engineering to public services and education. As such, interdisciplinary research, teaching, and services in AI are infused across and within all his activities. Furthermore, he also brings research expertise for advancing AI technologies’ sensitivity to social and ethical context in smart and connected health. One NIH R01 and one NIH research center grant currently support SAIL lab (https://sail.ua.edu/).
- Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence, A.N. Baglione, J. Gong, M. Boukhechba, K. J. Wells, L. E. Barnes. IEEE Pervasive Computing 19: 24-36, 2020.
- Self-powered cardiac monitoring: Maintaining vigilance with multi-modal harvesting and E-textiles, L. J. L. Luis, M. Ridder, D. Fan, J. Gong, B. M. Li, A. C. Mills, E. Cobarrubias, J. Strohmaier, J. S. Jur, J. Lach. IEEE Sensors Journal 21: 2263-2276, 2020.
- Understanding Behavioral Dynamics of Social Anxiety Among College Students Through Smartphone Sensors, J. Gong, Y. Huang, P. I. Chow, K. Fua, M. S. Gerber, B. A. Teachman, L. E. Barnes. Information Fusion 49: 57-68, 2019.
- Validation of a virtual reality buffet environment to assess food selection processes among emerging adults, C SL Cheah, S. Barman, K. TT Vu, S. E. Jung, V. Mandalapu, T. D. Masterson, R. J. Zuber, L. Boot, J. Gong. Appetite 153: 104741, 2020.
- Causality analysis of inertial body sensors for multiple sclerosis diagnostic enhancement. J. Gong, Y. Qi, M. D. Goldman, J. Lach. IEEE Journal of Biomedical and Health Informatics 20: 1273-1280, 2016.
The research in the SAIL lab focuses on the convergence of human and artificial intelligence that spans the area of human-centered computing, cyber-physical systems, and smart and connected health, through inventing, developing, and applying mobile and wearable computing systems to examine and understand the coupled relationship between humans and intelligent systems towards advancing human capabilities. Specifically, creating the novel framework for the convergence of human and artificial intelligence is crucial to tackling the challenges introduced by large amounts of big heterogeneous data and the new data science problems that arise in applications such as human health and diseases.