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)