Talk, Sharif University of Technology, Department of Computer Engineering, Tehran
Causal learning and discovery have received significant attention in the machine learning community. Causal learning allows us to answer the question about intervention (i.e., tinkering in the world) and imagination. This framework can mitigate the problem of robustness, adaptation, and explanation that is present in current machine learning practice.
Talk, Sharif University of Technology, Department of Computer Engineering, Tehran
In this talk, I presented our work on pattern formation during human embryonic stem cell development. We proposed a multicellular mathematical model for pattern formation during the in-vitro gastrulation of human ESCs. This model enhances the basic principles of the Waddington epigenetic landscape with cell-cell communication, which enables us to describe how the pattern and tissue formation occurs in the course of development.