Adil Siripatana

PhD Student

Current: Postdoctoral Fellow, University of New South Wales, Sydney, Australia


Research Interests

​Adil is interested in uncertainty quantification and data assimilation for coastal ocean forecasting. His Ph.D. thesis focuses on developing uncertainty reduction and parameter estimation techniques for coastal ocean model, using Bayesian inference and Spectral methods such as Ensemble Kalman Filter (EnKF), Markov Chain Monte Carlo (MCMC), and Polynomial Chaos (PC) expansion.

Selected Publications

  • Ensemble Kalman filter inference of spatially-varying Manning's n coffiecients in the coastal ocean
    A. Siripatana, T. Mayo, O. Knio, C. Dawson, O. Le Maitre, I. Hoteit
    Journal of Hydrology, 562, 664-684, 2018
  • Assessing an ensemble Kalman filter inference of Manning's n coefficient of an idealized tidal inlet against a polynomial chaos based MCMC
    A. Siripatana, T. Mayo, I. Sraj, O. Knio, C. Dawson, O. Le Maitre, I. Hoteit
    Ocean Dynamics, 67 (8), 2017
  • Single-site Lennard-Jones models via polynomial chaos of Monte Carlo molecular simulation
    A. Kadoura, A. Siripatana, S. Sun, O. M. Knio, I. Hoteit
    The Journal of Chemical Physics, 144 (21), 2016


  • ​M.Sc., Earth Science, KAUST, Thuwal, Saudi Arabia, 2014
  • B.Sc., Computational Science, Walailak University, Thailand, 201

Research Interests Keywords

Ocean modeling Data assimilation Uncertainty quantification