Doogesh Kodi Ramanah

Doogesh Kodi Ramanah has worked as an ML & AI Engineer at Neurons since October 2022. In this role, they are responsible for designing, implementing, validating, and deploying ML & AI models in the Predict software. Doogesh has achieved notable accomplishments, including designing and implementing a TensorFlow version of the YOLO object detection model for identifying areas of interest in customer-uploaded images.

Prior to this, Doogesh worked as a DARK Postdoctoral Fellow at the Niels Bohr Institute, University of Copenhagen from October 2019 to September 2022. During their time there, they made significant research contributions, including the development of a super-resolution emulator capable of generating high-resolution cosmological simulations and the creation of a spatio-temporal ML classifier for identifying gravitationally lensed supernovae. Doogesh also implemented a variant of the smooth manifold extraction algorithm from the field of medical image processing.

Doogesh Kodi Ramanah completed their Bachelor of Science (BSc) degree with a minor in Astrophysics from the University of Mauritius during the period of 2011 to 2014. Doogesh then pursued a Master's degree in Theoretical and Mathematical Physics at Imperial College London from 2014 to 2015. Subsequently, Doogesh undertook their Doctor of Philosophy (PhD) studies in Bayesian statistical inference and deep learning in cosmology at Sorbonne Université, which spanned from 2016 to 2019.