Academic Credentials
  • M.S., Electrical Engineering, University of Texas, Dallas, 2023
  • Ph.D., Electrical Engineering, University of Texas, Dallas, 2023
  • B.S., Electrical Engineering, University of Texas, Dallas, 2019
Professional Honors
  • Excellence in Education Doctoral Fellowship, University of Texas as Dallas, 2020-2023.
  • Best Paper Award, Joint Workshop on CPS and IoT Security and Privacy (CPSIoTSec), in conjunction with the ACM Conference on Computer and Communications Security, 2021, Seoul, South Korea.
Professional Affiliations
  • Institute of Electrical and Electronics Engineers

Dr. Sleiman Safaoui's area of expertise is in robotics and autonomy with an emphasis on motion planning and decision making under uncertainty. He has extensive knowledge in designing algorithms that make robots and autonomous systems robust to uncertainties in the dynamics and the surrounding environment, along with experience designing robotic platforms and implementing autonomy solutions on physical systems.

Prior to joining Exponent, Dr. Safaoui was a research associate in the Controls, Optimization, and Networks Lab (CONLab) at The University of Texas at Dallas where he worked on designing a software package to make encoding and solving complex risk-based optimization problems easier and implementing motion planning algorithms on single and multi-robot systems. Before that, he was a research assistant in CONLab focusing on designing motion planning and control algorithms for robotic systems in the presence of uncertainty. More specifically, his work focused on utilizing statistics, optimization, and data-driven methods for safe trajectory planning for ground and aerial robots including quadrotors and autonomous vehicles.

Dr. Safaoui also worked at Mitsubishi Electric Research Laboratories (MERL) where he leveraged machine learning and optimization-based methods to design motion planning algorithms for robot teams. Furthermore, he implemented autonomy algorithms on physical platforms using quadrotors and miniature autonomous vehicles.