Current research:

  • Denoising galaxy images using deep learning techniques – testing the importance of incorporating image Point Spread Function in the network in various ways
  •  Stellar mass calculation of galaxy clusters from the Planck all sky survey
  •  Supernova and variable star detection and analysis using active anomaly detection
  •  Detection and morphological classification of dwarf galaxies from the MATLAS
  •  Detection and classification of tidal remnants around massive galaxies from the
    MATLAS survey

Past research :

  • Analysing the effect of including measurement errors in machine learning and how it improves astronomical applications using Probabilistic Random Forests.
  • Supernova photometric classification using active learning.
  • Comparison of machine learning techniques to classify galaxies according to their morphological types.
  •  Formulating a pre-processing technique for astronomical software such as Source Extractor using anisotropic diffusion filtering.
  •  Investigating merger remnants around and within early-type galaxies. 
  •  Detection and analysis of dwarf galaxy environments from the MATLAS survey.