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Track: Modern CS in the Real World

Location: Pacific DEKJ

Day of week: Tuesday

Computer Science research did not stop at QuickSort or the LR algorithm. In this track, we'll cover topics such as probabilistic algorithms and data structures, new security and distributed algorithms, advances in typing, formal methods, new approaches to concurrency and much more.  

Why? Because we need to tackle ever more data in shorter periods of time - but our CPUs don't get much faster. Concurrency helps - but that just brings new problems to tackle, and meanwhile more moving parts just means more things that can fall over if we're not careful. Time to sneak a peek at approaches real companies use to tackle these issues using Computer Science research and results from the last few decades.

Track Host: Werner Schuster

InfoQ Editor Functional Programming, QCon PC, Wolfram

Werner Schuster focuses on languages, VMs and compilers, Wolfram Language, performance tuning, and recently cloud taming. He's on the PC for QCon NYC/SF/London

10:35am - 11:25am

Anti-Entropy Using CRDTs on HA Datastores @Netflix

Entropy that causes replicas to diverge on Highly Available datastores is unavoidable in practice. To deal with this, many systems have anti-entropy mechanisms to offer an eventually consistent view of the data. We will be talking about Dynomite, an open-source distributed datastore primarily backed by Redis, built to be highly available, and how it uses CRDTs to make anti-entropy possible with minimal coordination between replicas, as compared to other mechanisms. This talk will briefly introduce Dynomite, offer a deep dive on how anti-entropy is implemented and talk about the underlying principles of CRDTs that make this possible.

Sailesh Mukil, Senior Software Engineer @Netflix

11:50am - 12:40pm

The Talk You've Been Await-Ing For

Rust has finally shipped async/await! It took us a long time, and that's because Rust's implementation works a bit differently than in other languages. In this talk, we'll go over the deep details of how async/await works in Rust, covering concepts like coroutines, generators, stack-less vs stack-ful, "pinning", and more!

Steve Klabnik, Rust Core Team

1:40pm - 2:30pm

Swift for Tensorflow

Swift for TensorFlow is a next-generation machine learning platform that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional AI algorithms and general purpose software development. This talk demonstrates how Swift for TensorFlow can make advanced machine learning research easier and faster.

Paige Bailey, Product Manager (TensorFlow) @Google

2:55pm - 3:45pm

ML/AI Panel

This is your chance to hear from the experts: ML platform builders and longtime users. What makes ML different from other types of applications and why does it require special tooling? How did they get started and what tools would they use if they were starting today? Where is the low-hanging fruit and how do they recommend acquiring those initial quick wins? Join us to learn what you should watch out for in the initial stages, while you're still learning core concepts.

Moderator: Michelle Casbon

Amy Unruh, Staff Developer Relations Engineer @Google Cloud Platform
Chris Albon, Director of Data Science @DevotedHealth
June Andrews, AI @Stitch Fix
Paige Bailey, Product Manager (TensorFlow) @Google
Melanie Warrick, Senior Developer Relations Engineer Company @Google

4:10pm - 5:00pm

Sorbet: Why and How We Built a Typechecker for Ruby

In June we open-sourced Sorbet, a fast, powerful type checker designed for Ruby.
It's now used in hundreds of companies.
Within Stripe, we've used Sorbet to drive code quality via measurable, concrete indicators.

This talk will cover why we started this project and what contributed to its success.
No prior knowledge of Ruby is expected.

Dmitry Petrashko, Developer Productivity @stripe

5:25pm - 6:15pm

Probabilistic programming for software engineers

Big data — and the neural networks we use to make sense of it — have taken the industry by storm! But what might be falling through the cracks?

In this talk, I’ll introduce you to the world of Probabilistic Programming Languages, and why it’s something that the industry should care about today. Some of the most pressing problems in machine learning concern accuracy, interpretability, and reliability of our models, and PPLs, as they’re called, offer a compelling means to handle all of these at once. At the intersection of programming languages and machine learning, PPLs build upon centuries of Bayesian Statistics knowledge to offer predictions qualified with uncertainty estimates. But they also lean on techniques from the Programming Languages space to make this accessible to ordinary developers. And best of all, PPL models can be trained generically!

In addition to introducing you to PPLs and Bayesian Statistics, this talk will give a sneak preview of how we’re advancing probabilistic programming at Facebook, as well as some of the big problems we’ve used it to solve.

Michael Tingley, Engineering Manager @Facebook

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