Friday, October 7, 2016 - 10:30am


100 Baker-Porter Hall


Jimmy Lin University of Waterloo

Event Website:

The Academic:  There and Back Again

ABSTRACT  In "The Hobbit", J.R.R. Tolkien tells the tale of Bilbo Baggins being whisked away from the comfort of the Shire on an adventure involving dragons and treasure.  This life-changing experience enriched his character and irrevocably altered his world view.

In 2010, I started on my own adventure: leaving the comforts of the Ivory Tower, I joined Twitter, which at the time was a frantic, fast-growing company stumbling and bumbling around in the new topsy-turvy world of social media and big data.  I worked on analytics infrastructure to support data science and services designed to connect users to relevant content.  In the following two years, Twitter's Hadoop data warehouse grew from dozens of nodes to tens of thousands of nodes across multiple datacenters, ingesting tens of terabytes daily from dozens of heterogeneous sources.  Beyond analytics infrastructure, I had the opportunity to contribute to a variety of data products, including real-time search and graph-based recommendations.

Eventually, I made it back to the comforts of the academic Ivory Tower, and like Bilbo Baggins, the experience has profoundly shaped my core identity as a hobbit (errrr, I mean researcher).  In this talk, I will attempt to distill my experiences working on "big data" systems into a series of high-level "lessons learned", using graph-based recommendations as a backdrop.  I'll attempt to connect my experiences in industry back to academic research and discuss my views about the evolving roles of academia and industry.

BIO  Professor Jimmy Lin holds the David R. Cheriton Chair in the David R. Cheriton School of Computer Science at the University of Waterloo.  He graduated with a Ph.D. in Electrical Engineering and Computer Science from MIT in 2004.  Lin's research aims to build tools that help users make sense of large amounts of data.  His work lies at the intersection of information retrieval and natural language processing, with a focus on large-scale distributed algorithms and infrastructure for data analytics.  From 2010-2012, Lin spent an extended sabbatical at Twitter, where he worked on services designed to connect users to relevant content and analytics infrastructure to support data science.  Experience in academia and industry guides him in building useful applications that solve real-world user problems while addressing fundamental challenges in computer and information science.

FACULTY HOST  Jamie Callan


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