Mazdak Tootkaboni

Associate Professor

Civil & Environmental Engineering

508-999-8465

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Education

2009 Johns Hopkins University, Baltimore, MD Ph.D in Structural Mechanics
2002 Tehran University, Iran M.Sc. in Civil Engineering
2000 Tehran University, Iran B.Sc. in Civil Engineering

Research interests

  • Stochastic computational mechanics and uncertainty quantification
  • Construction of consistent data-driven stochastic models
  • Stochastic multi-scale modeling of heterogeneous materials
  • Design topology optimization under uncertainty
  • Stochastic non-linear analysis of thin-walled structures

Select publications

A. Asadpoure, M. Tootkaboni, and J.K. Guest (2010).
Robust topology optimization of structures with uncertainties in stiffness - application to truss structures
Computers and Structures

M. Tootkaboni, L. Graham-Brady (2010).
A multi-scale spectral stochastic method for homogenization of multi-phase periodic composites with random material properties
International Journal for Numerical Methods in Engineering, 83, 59-90.

M. Tootkaboni, L. Graham-Brady (2010).
Stochastic direct integration schemes for dynamic systems subjected to random excitations
Probabilistic Engineering Mechanics, 25, 163-171.

Mazdak P. Tootkaboni earned his high school diploma in mathematics and physics in June 1995. He then attended University of Tehran in Iran where he was awarded BSc in Civil Engineering and MSc in Earthquake engineering in April 2000 and December 2002 respectively. He joined the Department of Civil Engineering at the Johns Hopkins University in 2004 and earned his PhD degree in Structural Mechanics in May 2009.

Dr Tootkaboni’s research lies at the intersection of computational mechanics and applied probability and statistics. He develops schemes that combine recent advances in stochastic modeling (e.g. stochastic PDE solving techniques) and applied statistics (e.g. machine learning and statistical inference) with the existing methods in computational mechanics. These schemes have a wide range of applications, from uncertainty modeling (representation and propagation) to model validation and from reliability analysis to integration of experiments and computational models, and fault tolerant (uncertainty informed) design topology optimization. He is an associate member of ASCE and a member of Engineering Mechanics Institute (EMI) and its Probabilistic Mechanics Committee. 

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