Ilya Grigorik

Ilya Grigorik is a developer, an open-source and Ruby evangelist, a data-geek, and a proverbial early adopter of all things digital. Now living in the San Francisco Bay Area, Ilya is working on Social Analytics @ Google. Prior to joining the Google Analytics team, Ilya was the founder and CTO of PostRank, a social web analytics company which was acquired by Google in June 2011. In 2008, Ilya was the recipient of the Ruby Hero award for his contributions to the Ruby community.



Intelligent Code: Getting Started with Machine Learning


0-60 with Goliath: Building High Performance Ruby Web-Services

Track: Web, friday 10:00 - 10:50

Building a high-performance web service in Ruby? Then, chances are, you are looking at implementing a non-blocking server. Goliath is an open source, event-driven I/O framework, much like node.js or Tornado, except that Goliath is based on EventMachine, features a Ruby API, and most importantly, does away with the asynchronous "callback muck" by utilizing Ruby 1.9’s Fibers to preserve the nice synchronous look-and-feel of your code – which makes it much easier to write, test, and maintain.

Modeling concurrency in Ruby and beyond

Track: Cool Languages, friday 15:15 - 16:05

The world of concurrent computation is a complicated one. We have to think about the hardware, the runtime, and even choose between half a dozen different models and primitives: fork/wait, threads, shared memory, message passing, semaphores, and transactions just to name a few. And that's only the beginning.What about "alternative concurrency models"? Can you name any, how are they different, what do they give us? Stop by to learn about CSP, Actor, and other models, in Ruby and beyond.

Intelligent Code: Getting Started with Machine Learning

Track: Xtra(ck), thursday 14:20 - 15:10

Machine learning is a discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data — a fancy name for a simple concept. Behind all the buzzword algorithms such as Decision Trees, Singular Value Decomposition, and Support Vector Machines lie simple observations and principles. In this presentation, we will take a ground-up look at how they work, and how you can apply them in your own applications.