[Person photo]

Phuong Pham

Graduate Student, Dr. Wang

School of Arts and Sciences

Computer Science

Faculty Advisor: Jingtao Wang

Pham, P. & Wang, J. (2017) Understanding emotional responses to mobile video advertisements via physiological signal sensing and facial expression analysis. Proceedings of the 22nd ACM Conference on Intelligent User Interfaces (IUI '17), pp 67-78. ACM, New York, NY, USA.

Xiao, X., Pham, P., & Wang, J. (2017). Dynamics of affective states during MOOC learning. In André E., Baker R., Hu X., Rodrigo M., & du Boulay B. (Eds.) Artificial Intelligence in Education (AIED 2017): Vol. 10331. Lecture notes in computer science (pp. 586-589). Cham: Springer International Press.

Pham, P. & Wang, J. (2017). AttentiveLearner2: A multimodal approach for improving MOOC learning on mobile devices. In André E., Baker R., Hu X., Rodrigo M., & du Boulay B. (Eds.), Artificial Intelligence in Education (AIED 2017): Vol. 10331. Lecture notes in computer science (pp. 561-564). Cham: Springer International Press.

Pham, P., Wang, J. (2016). AttentiveVideo: quantifying emotional responses to mobile video advertisements. Proceedings of the 18th ACM International Conference on Multimodal Interaction, Tokyo, Japan, 12-16 November (pp. 423-424). New York, NY: ACM.

Pham, P., Wang, J. (2016). Adaptive review for mobile MOOC learning via implicit physiological signal sensing. Proceedings of the 18th ACM International Conference on Multimodal Interaction, Tokyo, Japan, 12-16 November (pp. 37-44). New York, NY: ACM.

LRDC graduate student researcher Phuong Pham, Computer Science, (Jingtao Wang is Phuong’s advisor), was awarded the Outstanding Student Paper Award at the ACM International Conference on Multimodal Interaction for "Adaptive Review for Mobile MOOC Learning via Implicit Physiological Signal Sensing."

November 2016