• Assistant Professor, Industrial & Systems Engineering
  • Mike and Sugar Barnes Faculty Fellow, Industrial & Systems Engineering
  • Faculty Fellow, Center for Health Systems & Design (CHSD)
  • Faculty Fellow, Center for Remote Health Technologies & Systems (CRHTS)
Maryam Zahabi

Educational Background

  • Ph.D., Industrial & Systems Engineering, North Carolina State University - 2017
  • Ph.D. Minor, Statistics, North Carolina State University - 2017

Research Interests

    • Human performance modeling
    • Human-computer interaction
    • Human factors in surface transportation
    • Assistive technology

Awards & Honors

  • Young Investigator Award, Applied Ergonomics Conference/Texas A&M Ergo Center, 2022
  • Mike and Sugar Barnes Faculty Fellow I, Wm Michael Barnes '64 Department of Industrial and Systems Engineering, 2021
  • Stephanie Binder Young Professional Award, Human Factors & Ergonomics Society - Surface Transportation Technical Group, 2021
  • CAREER Award, National Science Foundation, 2021

Selected Publications

  • Shahini, F., Park, J., Welch, K., & Zahabi, M. (2022). Effects of unreliable automation, non-driving related task, and takeover time budget on drivers’ takeover performance and workload. Ergonomics, 1-16.
  • Park, J., & Zahabi, M. (2022). Cognitive Workload Assessment of Prosthetic Devices: A Review of Literature and Meta-Analysis. IEEE Transactions on Human-Machine Systems.
  • Wozniak, D., Shahini, F., Nasr, V., & Zahabi, M. (2021). Analysis of advanced driver assistance systems in police vehicles: A survey study. Transportation research part F: traffic psychology and behaviour, 83, 1-11.
  • Zahabi, M., Nasr, V., Mohammed Abdul Razak, A., Patranella, B., McCanless, L., & Maredia, A. (2021). Effect of secondary tasks on police officer cognitive workload and performance under normal and pursuit driving situations. Human factors, 00187208211010956.
  • Zahabi, M., Wang, Y., & Shahrampour, S. (2021). Classification of Officers’ Driving Situations Based on Eye-Tracking and Driver Performance Measures. IEEE Transactions on Human-Machine Systems, 51(4), 394-402.