​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.

• ​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

• Postdoc, University of New South Wales, Sydney, Australia
• PhD, KAUST, 2014-2019, Uncertainty quantification and assimilation for efficient coastal ocean forecasting
• M.Sc. Earth Sciences and Engineering, KAUST, Jan 2014 – Jun 2018, Uncertainty quantification and assimilation for efficient coastal ocean forecasting
• B.Sc., Computational Science, Walailak University, Thailand, 201

• ​Physical Science and Engineering Division (PSE)