My research interest focuses on Bayesian statistical models of high-dimensional and large- scale neural responses and fMRI decoding. The current specific works lie in fourfold:
(i) sparse Bayesian structure learning for fMRI decoding with hierarchical generative models,
(ii) fast moment-based convolutional spike-triggered covariance analysis for neural sensory encoding (convolutional neural network),
(iii) Bayesian optimization and active learning,
(iv) Gaussian process latent variable models for brain kernel.
Moreover, my interest also spans many data-driven research using statistical and machine learning tools for efficiently analyzing real world data (e.g. neural data, time series, geospatial data, speech data).