Amina has been working on machine learning research and defended her Ph.D. thesis in 2021 February in the Department of Computer Science at the University of Geneva.  After the defense, she has continued working in the DMML research group in HES-SO as a postdoctoral researcher.

The main focus of her research are representation learning, generative models for discrete data, and optimization algorithms. Specifically, she has worked on how to incorporate meta-features (feature side-information) into learning to improve generalization performance, and how to enable the conditional generation and style transfer over discrete structured data such as molecules, trees, or graphs. These are typically hard to optimize, especially for a conditional generation. Methods developed for natural images often fail dues to the discrete nature of the space. She is interested in developing efficient algorithms that can deal with the discrete nature of the data.