Research

My research interests rely on Bayesian inference & reasoning, machine learning, and data science, with a focus on both theoretical algorithmic development and interdisciplinary research with real-world applications.

  • Data Science and Signal Processing
    • High dimensional data processing and representation learning
    • Multi-modal, heterogeneous data fusion
    • Multi-channel time series analysis and spatio-temporal data processing
  • Statistical machine learning and deep learning
    • Bayesian machine learning including MCMC, SMC, Nested Sampling, Variational Inference etc.
    • Probabilistic density estimation and uncertainty quantification in inverse problems
    • Multivariate latent variable estimation and uncertainty quantification in multi-channel / multi-sensory systems
    • Supervised and unsupervised learning using deep learning architectures (RNN/CNN/Transformer)

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