My research focusses on developing lightweight statistical tools to model complex weather and climate processes. I work a lot with understanding the spatio-temporal regularities within noisy weather structures so as to be able to approximate them in a realistic/consistent manner. This allows better understanding of the uncertainty space, and more importantly, agile exploration of it. I employ a range of techniques for this, going from traditional statistical techniques to more recent artificial intelligence techniques.
I also dabble around with cloud-optimised artificial intelligence based training on large datasets (while trying to not use up too many credits), and multi-resolutional image analysis and decomposition. My previous work was at an NGO, and I try to maintain a strong lens on stakeholder engagement and capacity building within my work.