Tony McDonald

Assistant Professor

Image of Tony McDonald

Office: 4075 ETB
Phone: 979-458-2339
Fax: 979-458-4299
Email: mcdonald@tamu.edu

Personal Website
Curriculum Vitae
Google Scholar Profile

Research Interests

Dr. McDonald's research focuses on applying machine learning to relevant problems in human factors in the transportation and healthcare domains. Specifically he is interested in using knowledge generated from machine learning algorithms to improve existing models of human behavior and improving machine learning algorithm performance by combining traditional approaches with novel data analysis and domain expertise.

Awards & Honors

  • HFES Alphonse Chapanis Best Student Paper Award 2012
  • HFES Surface Transportation Technical Group Best Student Paper Award 2012
  • HFES Surface Transportation Technical Group Best Student Paper Award 2014

Education

  • Ph.D. Industrial Engineering, University of Wisconsin-Madison, Madison, WI, 2014
  • M.S. Industrial Engineering, University of Wisconsin-Madison, Madison, WI, 2012
  • B.S. Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 2010

Selected Publications

McDonald, A.D., Lee, J.D., Aksan, N.S., Dawson, J.D., Tippin, J., and Rizzo, M. (2017). Using kinematic driving data to detect sleep apnea treatment adherence. Journal of Intelligent Transportation Systems, 21(5), 422-434.

Ghazizadeh, M., McDonald, A.D., and Lee, J.D. (2014). A text mining approach to decoding free-response consumer complaints: Insights from the NHTSA vehicle owner’s complaint database. Human Factors: The Journal of the Human Factors and Ergonomics Society, 56(6), 1189-1203.

McDonald, A.D., Lee, J.D., Schwarz, C., and Brown, T.L. (2014). Steering in the random forest: Ensemble learning based detection of drowsiness related lane departures. Human Factors: The Journal of the Human Factors and Ergonomics Society, 56(5), 986-998.

McDonald, A.D., Lee, J.D., Aksan, N.S., Dawson, J.D., Tippin, J., and Rizzo, M. (2013). The Language of Driving: Advantages and Applications of Symbolic Data Reduction for Naturalistic Driving Data Analysis. Transportation Research Record, Journal of the Transportation Research Board, 2392, 22-30.