I am an Early Career Advanced Fellow in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge.
Previously, I spent two years as a Research Scientist at Secondmind (formerly PROWLER.io). I leveraged information-theoretic arguments to provide efficient, reliable and scalable Bayesian optimisation for problems inspired by science and the automotive industry.
During my PhD, I developed information-theoretical Bayesian Optimisation routines for real-world problems, including batch, multi-fidelity and discrete structure design. My primary applications were natural language processing, genomics and molecular search. In addition, I applied Bayesian optimisation to real machine learning systems, including Amazon Alexa’s text-to-speech pipeline.
- Scalable Bayesian models to help scientists better understand the world around us.
- Active learning and Bayesian optimisation to accelerate the design of new technologies
- Machine learning for climate science.
- Molecular search and gene design.