There are two interlinked facets to my current research:

• Understanding the structure, dynamics and spectroscopic properties of water and aqueous systems (particularly interfaces) using atomistic simulations. In particular, I use methods based on the imaginary-time Feynman path integral picture to account for the effects of quantum-mechanics on the motion of the nuclei.
• The development and application of supervised machine-learning methods, particularly symmetry-adapted methods that allow the efficient and accurate prediction of molecular and materials properties transforming like a tensor (e.g. dipole moment, polarizability ...). See alphaml.org for an example.

External links:

• My Google Scholar profile
• AlphaML Web App -- predict molecular polarizabilities with better-than-density functional theory accuracy
• TENSOAP -- python code for symmetry-adapted regression of tensors