I am a Data Scientist/Postdoc in the Artificial Intelligence (AI) Lab and the Ice Dynamics and Palaeoclimate (IDP) Team at the British Antarctic Survey (BAS) environmental research institute in Cambridge, UK. My research is supported by the Alan Turing Institute’s AI for Science programme at the AI Lab. I am working with Dr Scott Hosking in the AI Lab and Dr Hamish Pritchard in the IDP Team.

My research revolves around the intersection of computer science, data science, atmospheric science, and climate science. My research interests focus on machine learning, causal inference, deep learning, topological data analysis, atmospheric phenomena, weather extremes, and large climate processes.

My PhD research work was concerned with the development of machine learning and deep learning methods for detecting atmospheric phenomena (patterns) in large climate datasets. In particular, I have carried out research on atmospheric rivers and atmospheric blocks that usually lead to extreme weather events in mid-latitude regions. I have also participated in the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) where I have developed a pattern recognition method that combines topological data analysis and machine learning (TDA-ML) for identifying atmospheric rivers in global climate model outputs. My thesis is available on the university archive.

My current research at BAS spans the application of machine learning, causal inference techniques, and causal discovery methods to improve the understanding of the causation (drivers) of precipitation, droughts, and water security in the Himalayan region. As a member of AI Lab and IDP Team, I share my research experience in machine (deep) learning and applied topology for weather pattern recognition in high-resolution climate model simulations and reanalysis products.

Highlights of my research