Track: Applied Machine Learning and Data Science


Day of week:

Understand your big big data!

Track Host:
Daniel Tunkelang
Head of Search Quality at LinkedIn
Daniel Tunkelang is LinkedIn's Director of Engineering for Search Quality. Before that, he led LinkedIn's product data science and query understanding teams. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT. @dtunkelang
10:35am - 11:25am

by Daniel Tunkelang
Head of Search Quality at LinkedIn

This talk is about applying machine learning to solve problems.

It’s not a talk about machine learning — or at least not about the theory of machine learning. Theoretical machine learning requires a deep understanding of computer science and statistics. It’s one of the most studied areas of computer science, and advances in theoretical machine learning give us hope of solving the world’s “AI-hard” problems.

Applied machine learning is more grounded but no less important. We are...

11:50am - 12:40pm

by Andrea Burbank
Search and Data Mining Engineer at Pinterest

As engineers, we regularly consider running an A/B experiment for an incremental change: should this button be red or blue, 30 pixels or 50, to maximize clicks?

This talk will focus on the risks, benefits, and lessons from running a single huge experiment with hundreds of moving parts, and with long-term engagement as the metric of success.

1:40pm - 2:30pm

by Amy Heineike
Director of Mathematics at Quid

For anyone to works with large amounts of information, network analysis provides a powerful and intuitive way to understand what your data sets contain. When the output that you want is to empower the user versus improve the system, visualization becomes paramount. 

Quid has built a complete platform to do this with unstructured data, but there are also simple ways you can leverage network visualizations to empower your exploration of data. This session will cover how to...

2:55pm - 3:45pm

by Sean Taylor
Data Scientist at Facebook

We've all seen impressive examples of what machine learning and statistics can do when applied by experts. But how can we democratize the techniques used by researchers and turn them into better knowledge and decisions for everyone?

4:10pm - 5:00pm

by Richard Kasperowski
QCon Open Space Facilitator

Open Space

Join Daniel Tunkelang, our speakers, and other attendees as we explore Big Data, Machine Learning, and Data Science!

What is Open Space?

Every day at QConSF, we’ll open space five times, once for each track. Open Space is a kind of unconference, a simple way to run productive meetings for 5 to 2000 or more people, and a powerful way to lead any kind of organization in everyday practice and extraordinary change.



5:25pm - 6:15pm

by Oscar Celma
Director of Research, Pandora

Pandora began with The Music Genome Project, the most sophisticated taxonomy of musicological data ever collected and an extremely effective content-based approach to music recommendation. But what happens when you have a decade of additional data points, given off by more than 250 million registered users who have created 6+ billion personalized radio stations and given 45+ billion thumbs? You hire an astrophysicist.


This session will look at how the interdisciplinary...


Covering innovative topics

Monday, 3 November

Tuesday, 4 November

Wednesday, 5 November