I have completed my PhD at the Weizmann Institute of Science in 2014. Afterwards, I have been a postdoc at Cornell, UC Berkeley and Princeton. 

I am interested in how theoretical insights from computer science, discrete mathematics and probability theory can lead to better algorithms in the domains of artificial intelligence and machine learning. Intractability results such as NP hardness arise in numerous AI applications. Devising ``beyond worst case" approaches that can bypass intractability results is one of my main research interests.

I'm also interested in applying methods from artificial intelligence and machine learning to develop new methods to reason about the utilities and preferences of humans resulting with computers and artificial agents that are better aligned with people.