Academic Credentials
  • Ph.D., Civil Engineering, University of California, Los Angeles (UCLA), 2024
  • M.S., Civil Engineering, University of California, Los Angeles (UCLA), 2020
  • B.S., Civil Engineering, University of Florida, 2018
Professional Honors
  • The National GEM Fellowship
  • UCLA Eugene V. Cota Robles Fellowship
Professional Affiliations
  • American Society of Civil Engineers (ASCE)
  • Earthquake Engineering Research Institute (EERI)
  • The Structural Engineers Association of Southern California (SEAOSC)
  • GEM Alumni Association (GEM)

Dr. Abdelmalek-Lee specializes in probabilistic risk assessment and natural hazards engineering. He has expertise in evaluating seismic risk for buildings and evaluating designs of structures subjected to extreme loads using nonlinear analyses methods and performance-based engineering methodologies. He has experience applying machine intelligence and statistical methods to remote sensing data to develop predictive models and perform efficient analyses of portfolios of structures and assets subjected to hazards such as seismic loads or other extreme events.

Prior to joining Exponent, Dr. Abdelmalek-Lee worked with ImageCat to develop seismic risk models for industrial facilities. He also worked at the Idaho National Laboratory (INL) in the Nuclear Science and Technology Division, where he developed a stochastic ground motion model to quantify seismic hazard. The new methods allowed for improved uncertainty quantification and included sensitivity analyses of nuclear facilities structures subjected to multiple seismic hazard levels.

Dr. Abdelmalek-Lee earned his PhD at the University of California, Los Angeles in Civil Engineering with a focus on Structural/Earthquake Engineering. During his Ph.D., Dr. Abdelmalek-Lee worked on an end-to-end seismic framework that implements design, analysis, and loss estimation for a suite of buildings to aid rapid post-event assessments. The framework modifies current methodologies, offering a machine learning alternative to traditional probabilistic seismic hazard analyses and enabling the use of strong motion data where computationally expensive nonlinear structural models are usually required.