B.S. Computer Science, INSA Lyon (France), 2012M.S. Computer Science, University of Montpelier (France), 2013Ph.D. Computer Science, University of Quebec (Canada), 2016

Mehdi Boukhechba is an assistant professor in the Engineering Systems and Environment Department and the co-director of the Sensing Systems for Health Lab. His primary research interests are in ubiquitous computing, data science, behavioral modeling and pervasive health technologies. In recent years, he has been developing novel ubiquitous sensing platforms to understand human behaviors in the wild. His research has been focused on designing new assessment and intervention methods for multiple health conditions such as depression, anxiety, cancer, infectious disease and traumatic brain injury. 

Research Interests

  • Ubiquitous Computing
  • Human-Computer Interaction
  • Mobile Sensing
  • Data Science and Machine Learning
  • Digital Health

Selected Publications

  • “Adaptive Passive Mobile Sensing Using Reinforcement Learning,” 2019 IEEE 20th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), Jun. 2019. L. Cai, M. Boukhechba, N. Kaur, C. Wu, L. E. Barnes, and M. S. Gerber
  • “SocialText: A Framework for Understanding the Relationship Between Digital Communication Patterns and Mental Health,” 2019 IEEE 13th International Conference on Semantic Computing (ICSC), Jan. 2019. S. Mendu, M. Boukhechba, A. Baglione, S. Baee, C. Wu, and L. Barnes
  • “Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study,” JMIR Mental Health, vol. 5, no. 3, p. e10101, Jul. 2018. M. Boukhechba, Y. Huang, P. Chow, K. Fua, B. A. Teachman, and L. E. Barnes
  • “Monitoring social anxiety from mobility and communication patterns,” Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers on - Ubi M. Boukhechba, Y. Huang, P. Chow, K. Fua, B. A. Teachman, and L. E. Barnes
  • “ActiPPG: Using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors,” Smart Health, vol. 14, p. 100082, Dec. 2019. M. Boukhechba, L. Cai, C. Wu, and L. E. Barnes
  • “Vector Space Representation of Bluetooth Encounters for Mental Health Inference,” Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers - UbiComp ’18, 2 C. Wu, L. Cai, M. S. Gerber, M. Boukhechba, and L. E. Barnes
  • “A novel Bluetooth low energy based system for spatial exploration in smart cities,” Expert Systems with Applications, vol. 77, pp. 71–82, Jul. 2017. [7] M. Boukhechba, A. Bouzouane, S. Gaboury, C. Gouin-Vallerand, S. Giroux, and B. Bouchard

Courses Taught

  • Mobile Sensing and Health Fall 2017, Fall 2018, Fall 2019