I work at the intersection of machine learning and genetics. My main research interest is understanding how local molecular rules give raise to emergent spatial patterns in the context of biological dynamical systems. To this end, I use techniques from statistical optimization, statistical physics and domain adaptation to identify contextual phenotypes in spatial transcriptomic data and to understand the identity of single cells and their interactions in early developement. I am also interested in active learning and graphical neural networks as models to study multi-agent systems inspired by biology and ecology.

Previously, I was an Affiliated Lecturer in the Computer Science Department at Cambridge University, a Member in the School of Mathematics at the Institute for Advanced Study and a visitor of the Duke Statistical Science Department. I received my PhD in Computational Biology at Princeton University. I did my undergraduate studies in Mathematics at MIT.