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  • Diseases are outcomes of a complex dynamical system of interacting factors related to epidemiology, socio-economic conditions, environment, geographical connectedness, demographics, and individual behavior and lifestyle. Understanding and modeling these dynamical systems is thus crucial for informing disease prevention and intervention decisions. Our lab works on development of new methodologies and computational models for simulating these systems dynamics for purposes of disease prediction, prevention, and control analysis. Our work is funded by the National Institutes of Health, the National Science Foundation, and the World Health Organization.


  • Simulation-based modeling
    • agent-based network modeling
    • differential equations or compartmental modeling
    • micro-simulation
    • discrete-event
  • Stochastic processes
    • discrete-time Markov chain
    • continuous-time Markov processes
  • Simulation-based optimization (parametric and stochastic control)
    • numerical methods for non-linear optimization
    • Markov decision processes
    • dynamic programming
    • reinforcement learning

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