Henry Moss

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.

Research Interests

  1. Scalable Bayesian models to help scientists better understand the world around us.
  2. Active learning and Bayesian optimisation to accelerate the design of new technologies
  3. Machine learning for climate science.
  4. Molecular search and gene design.