After master’s studies in Computational Physics, Augustinas Sukys completed his PhD jointly at the University of Edinburgh and the Alan Turing Institute, where he focused on developing and implementing novel methods to approximate the chemical master equation, a framework commonly used for stochastic modelling of biochemical systems. His research interests also include transcriptomic data analysis, mechanistic model inference, open source computational software development and the applications of machine learning to biology.

Favourite thing about Melbourne? The weather.

Favourite thing about Australia? Good mango selection.

Favourite thing in science? Good question.

Favourite thing in general? Sleep.