Atiye Alaeddini

Postdoctoral Research Scientist

Atiye Alaeddini

Postdoctoral Research Scientist


Atiye has a Ph.D. in Aeronautics and Astronautics, Nonlinear Dynamics and Control Laboratory from the University of Washington, Seattle. Atiye also holds a MSc in Computer Engineering, Control and Robotics Laboratory from the University of California, Santa Cruz. Atiye brings seven years of experience working on projects from optimal detection of epidemic disease outbreaks to privacy of networks using online optimization algorithms, distributed conflict resolution, and bio-inspired design of a ground robot. Atiye is interested in developing mathematical models for complex disease dynamics which provide accurate inferences from disease surveillance data which will aide in future endeavors for improving and saving people’s lives. As a member of IDM’s team Atiye will be developing a statistical model and optimization algorithm for model calibration and parameter space exploration. These novel methods will be used to generate quantitative results across various disease models.

Biography

Atiye has a Ph.D. in Aeronautics and Astronautics, Nonlinear Dynamics and Control Laboratory from the University of Washington, Seattle. Atiye also holds a MSc in Computer Engineering, Control and Robotics Laboratory from the University of California, Santa Cruz. Atiye brings seven years of experience working on projects from optimal detection of epidemic disease outbreaks to privacy of networks using online optimization algorithms, distributed conflict resolution, and bio-inspired design of a ground robot. Atiye is interested in developing mathematical models for complex disease dynamics which provide accurate inferences from disease surveillance data which will aide in future endeavors for improving and saving people’s lives. As a member of IDM’s team Atiye will be developing a statistical model and optimization algorithm for model calibration and parameter space exploration. These novel methods will be used to generate quantitative results across various disease models.

Publications

Wednesday, August 2, 2017

The main focus of this work is on the use of PSPO to maximize the pseudo-likelihood of a stochastic epidemiological model to data from a 1861 measles outbreak in Hagelloch, Germany.

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