Jake Mannix

Personalization and the Social Web

Jake is an experienced search / recommender-systems / distributed systems architect and team lead, currently specializing in social network dynamics, distributed machine learning and search-relevance algorithm development as applied to personalized search and content recommendations. He's worked in a variety of search, interest-modeling, and advertiser ROI engineering teams at Twitter over the past four years, and before that he was at LinkedIn, where he worked on search relevance and infrastructure and was a founding member of the Recommender Systems team. He originally studied pure mathematics (algebraic topology and differential geometry) at the University of Washington, and theoretical physics (investigating the intersection of strongly interacting field theories and cosmology).

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