Research

My research interests include surrogate modeling, uncertainty quantification, and computer simultation, with a particular focus on deep Gaussian processes and machine learning.

Current Work

Our current work, Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation, proposes a two-layer deep Gaussian process framework that employs mini-batch latent MAP estimation to enable scalable inference. The method utilizes stochastic gradient-based optimization, including Adam, to efficiently learn latent representations while maintaining computational tractability.

Publications

Chang, C.-Y. and Sung, C.-L. (2026). Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning. International Journal for Uncertainty Quantification, accepted.