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MLconf 2013, was on 11/15/13 in San Francisco. See the event page for full details and pictures.

Advisors/Event Committee:

• Danny Bickson, Co-Founder at GraphLab
• Ted Dunning, Chief Application Architect at Map R
• Nikolaos Vasiloglou, Data Science at LogicBox
• Abhijit Bose, VP Data Science at American Express
• Eric Bieschke, Chief Scientist and VP of Playlists at Pandora
• Ashfaq Munshi, Knobout Inc.
• Josh Wills, Directof Data Science at Cloudera

Speakers:

• Xavier Amatriain, Research/Engineering Director, Netflix
• Ted Willke, Principal Engineer & General Manager, Graph Analytics Operation at Intel
• Jake Mannix, Applied Machine Learning Engineer, Twitter
• Joseph Gonzalez, Co-Founder at GraphLab
• Scott Triglia, Search and Data Mining Engineer at Yelp
• Josh Patterson, VP Services, Continuuity
• Lukas Biewald, CEO Crowdflower
• Quoc Le, Software Engineer at Google, PhD Stanford
• Eric Battenberg, Audio DSP Engineer, Gracenote
• Eric Bieschke, Chief Scientist and VP of Playlists, Pandora
• Abhijit Bose, VP Data Science, American Express
• Ameet Talwalkar, Postdoctoral Fellow at UC Berkeley
• Matei Zaharia, CTO Databricks
• Michael Mahoney, Research Scientist at Stanford University

Topics covered: Deep Learning, Matrix Factorization, Collaborative Filtering and Recommender Systems.
Please see the event page for full details, pictures and links to videos and lecture slides.

Sponsors include:

Platinum Sponsor: Intel
Gold Sponsors: GraphLab, HiringSolved, Adobe
Bronze Sponsors: Oxdata
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
See all the full speaker list »

The GraphLab Big Learning Workshop is a meeting place for both academia and industry to discuss upcoming challenges of large scale machine learning and solution methods. The main goal for this year’s workshop is to bring together top researchers from academia, as well as top data scientists from industry with the special focus of large scale machine learning on sparse graphs.
We have secured talks and demos about the hottest graph processing systems including: GraphLab, Pregel (Google), Giraph (Facebook) , Cassovary (Twitter), Combinatorial BLAS (LBNL/UCSB), Allegro Graph (Franz) ,Neo4j, Titan (Aurelius), DEX (Sparsity Technologies), YarcData and others!
View the event pictures here »

Event Schedule:

8am – 9am: Registration, Coffee & Snacks
9am – Presentations begin (See list of speakers below)
5pm – 7pm Networking with hosted bar / appetizers
For updated agenda and detailed speaker info see the GraphLab Workshop site.

Confirmed Speakers for the GraphLab 2013 Workshop include:

• Prof. Carlos Guestrin, University of Washington – Graphlab 2.2 and Beyond
• Dr. Avery Ching, Facebook – Graph Processing at Facebook Scale
• Prof. Vahab Mirrokni, Google – Clustering and Connected Components in Mapreduce and Beyond
• Dr. Pankaj Gupta, Twitter – WTF: The Who to Follow Service at Twitter
• Prof. Joe Hellerstein – Professor, UC Berkeley and Co-Founder/CEO, Trifacta – Productivity for Data Analysts: Visualization, Intelligence and Scale
• Dr. Lei Tang – Walmart Labs – Adaptive User Segmentation for Recommendation
• Dr. Derek Murray – Incremental, iterative and interactive data analysis with Naiad
• Dr. Ralf Herbrich, Facebook – TBA
• Prof. Mark Oskin, University of Washington, Grappa graph engine.

Featured Projects

• Google’s Pregel is their Bulk Synchronous graph framework. Prof. Vahab Mirrokni is going to give an oral talk about graph processing @ Google.
• Apache Giraph is the open source equivalent system to Google’s Pregel. Dr. Avery Ching, one of Giraph contributors, will give a talk about large scale graph processing @ Facebook.
• Dr. Pankaj Gupta, the creator of Cassovary Graph Processing system @ Twitter will give a talk about Who To Follow (WTF) service in Twitter.
• Naiad is a parallel data flow framework from Microsoft with the focus of incremental computation. Dr. Derek Murray from Microsoft Research will present Naiad.
• GraphLab is CMU+UW open source graph processing system, which supports both bulk synchronous parallel as well as asynchronous computation. Prof. Carlos Guestrin will present the latest GraphLab project.
• Allegro Graph is a high performance graph database with RDF support. Jans Aasman, the CEO of Franz, will give a demo of their newest graph database.
• Combinatorial BLAS is a distributed memory parallel graph library from LBNL/UCSB. Dr. Aydin Buluc will present comb-BLAS.
• Grappa is a distributed graph processing framework using commodity processors, from The University of Washington. Prof. Mark Oskin will present Grappa.
• Titan is a distributed graph database. Dr. Matthias Broecheler will present Titan.
• Neo4j is an open source distributed graph database in Java. Alex Averbuch from neo4j will present neo4j.
• Infinite Graph from Objectivity is a distributed graph database.
• DEX is a high performance and scalable graph database system. Dr. Noerert Martinez will present DEX.
• YarcData, a Cray spinoff is creating customized hardware solutions for ultra fast graph processing.
• Systap LLC is a startup working on speeding up graph algorithms using GPUs. Bryan Thompson from Systap will present preliminary results of applying the gather apply scatter model on GPU.

Other notable talks at the GraphLab workshop:

• Trifacta is the hottest bay area startup out there, started by Prof. Joe Hellerstein from Berkeley and Prof. Jefferey Heer from Stanford. Prof. Joe Hellerstein will talk about Productivity for Data Analysts: Visualization, Intelligence and Scale.
• Dr. Lei Tang from Walmart Labs will talk about adaptive user segmentation for collaborative filtering.
• Alpine Data Labs is a Greenplum spinoff focusing on big data analytics. Seven Hillion will describe a case study of big data analytics on top of Hadoop.

MLconf presents: Join us on Monday, July 9th in San Francisco for a full-day workshop on Large Scale Machine Learning. Featuring CMU’s Graphlab and including presentations from Twitter, Pandora, Netflix, Intel Labs, MapR, and many more.
The GraphLab workshop on large scale machine learning is a meeting place for both academia and industry to discuss upcoming challenges of large scale machine learning and solution methods. GraphLab is Carnegie Mellon’s large scale machine learning framework. The workshop will include demos and tutorials showcasing the next generation of the GraphLab framework, as well as lectures and demos from the top technology companies about their applied large scale machine learning solutions.
The workshop will be held on Monday, July 9th in San Francisco. Register today to enjoy early bird registration fee! Talks GraphLab Version 2 Overview- Carlos Guestrin, Carnegie Mellon University Large scale ML challenges – Theodore Willke, Intel Labs TBD – Alexander Smola, Yahoo! Labs Large scale ML learning at MapR – Ted Dunning, MapR Technologies Large scale ML at Pandora – Tao Ye, Pandora Internet Radio TBD – Xavier Amatriain – Netflix Cassovary Graph Processing System – Pankaj Gupta, Twitter
Posters/Demos Green Marl graph processing framework – Dr. Sungpack Hong, Oracle Labs Machine learning benchmark framework – Nicholas Kolegraff, Accenture TBD – Prof. Alexander Gray, Georgia Tech Alpine and MADLib Demo – Steven Hilion, Alpine Data Labs Platinum Sponsor Gold Sponsors Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries. All other logos are trademarks of the companies who own them, respectively. For more Machine Learning events follow @mlconf on twitter.