Yannet Interian

My work involves analyzing large-scale data (e.g. from web logs or set top-box data). I have work in various projects related to ad quality, that is how enjoyable or relevant ads are perceived by users / tv viewers. At the moment, I am interested in questions like: How people interact with YouTube? Can I predict popular videos using early data about these videos, like the first 100 views?

I received a PhD in Applied Mathematics from Cornell University and a BS in Mathematics from University of Havana. Following a postdoctoral position at UC Berkeley and a postdoctoral fellowship at IPAM (UCLA), I joined Google. I currently work as a Quantitative Analyst for YouTube.

Web page: www.interian.org

Google Publications

Previous Publications


    A Model for Generating Random Quantified Boolean Formulas

    Hubie Chen, Yannet Interian

    Nineteenth International Joint Conference on Artificial Intelligence (IJCAI) (2005)