Machine learning is about developing computer algorithms that can detect patterns in data without being explicitly programmed for any specific pattern. Usually, the process is designed for tabular data, but much scientific data is not in this form. For example, genomes are sequential data, and structural formulas of chemical compounds are network-like graphical data. My special focus is to develop machine learning algorithms that can handle these kinds of non-numerical data.

The ability to process the various kinds of data generated by chemical experiments and simulations is indispensable for rationally designing chemical reactions. With cutting-edge machine learning, I hope to make full use of data and theory to uncover the highly complex nature of real-world chemical reactions. This can contribute to modelling uncertain factors, predicting any promising targets and conditions, extracting new knowledge on determining factors, and seamlessly integrating theory-driven, knowledge-driven, and data-driven information.