Crafting cutting-edge deep generative models and quantifying uncertainties in waveform imaging and inversion, and more.
I am a PhD candidate at KAUST in the
Physical Science and Engineering division and affiliate with the
Seismic Wave Analysis Group,
Deep Imaging Group, and
Extreme Computing Research Center.
I specialize in deep generative modelling, Bayesian inference and uncertainty quantification. My research contributions spanned across the fields of waveform imaging and inversion through the development of novel deep generative modelling approach and scalable Bayesian inference methods aimed at quantifying the uncertainties of subsurface imaging and inversion products. I am working closely under supervision of Tariq Alkhalifah and Matteo Ravasi.
I am also actively involve with philanthropist works, and I actively support cerebral palsy, autism, and cancer organisations in Malaysia and Russia.
Precondition physics-driven inverse problems with non-linear dimensionality reduction networks.
Use of optimal transport and deep learning to increase the sensitivity of full-waveform inversion cost functions to target areas.