London, England, United Kingdom
- Led and collaborated on the development of a firm-wide machine learning library for finance, including components for RL, causal-inference, causal-discovery - Created infrastructure for rigorous RL research and empirical experimentation, including the design and development of novel RL agents - Applied causal inference methods to optimize business decisions -Developed a parallelized hyper-parameter tuning library for complex ML models.
- Applied LLM's for processing large corpora of employee surveys in free-text format for document classification and sentiment analysis. - Leveraged Google Cloud's high-end compute resources, utilizing TPU
My research focuses on the large distance modifications of general relativity (GR). The aim is to find a mathematically consistent theory of modified gravity that provides a 'natural' or 'mechanistic' explanation as an alternative to 'dark energy'. One of my focuses is a recently discovered field theory known as Galileon field theory. These are non-linear field theories with novel characteristics, both phenomenological and theoretical. Moreover these theories are extremely universal. They arise as effective field theories in seemingly different conceptual paradigms of modifying gravity such as brane world models and massive gravity.