2013 Graphlab Workshop on Large Scale Machine Learning

Jul 1 - 2, 2013 · San Francisco, United States of America
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.

8am - 9am: Registration & Contintental Breakfast
9am - Presentations begin (See agenda below)
5pm - 7pm Networking with hosted bar / appetizers      

Parking:  Click here for parking near the Hotel Nikko 

08:00 – 09:00
Registration and reception

09:00 – 10:00

Prof. Carlos Guestrin, GraphLab Inc. & University of Washington: GraphLab v2.2 and Beyond

10:00 – 10:30

Prof. Joe Hellerstein – Professor, UC Berkeley and Co-Founder/CEO, Trifacta - Productivity for Data Analysts: Visualization, Intelligence and Scale

10:30 – 11:00

Prof. Mark Oskin, University of Washington, Grappa graph engine.

11:00 – 11:20
Coffee Break

11:20 – 11:50

Prof. Christopher Re, University of Wisconsin-Madison – TBA

11:50 – 12:10

Prof. S V N Vishwanathan, Purdue - NOMAD: Non-locking stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix factorization

12:10 – 12:30

Prof. Michael Mahoney, Stanford - Randomized regression in parallel and distributed environments

12:30 – 14:00
Lunch break (on your own)

14:00 – 14:20

Dr. Theodore Willke, Intel Labs - Intel GraphBuilder 2.0

14:20 – 14:40

Dr. Avery Ching, Facebook – Graph Processing at Facebook Scale

14:40 – 15:00

Prof. Vahab Mirrokni, Google - Clustering and Connected Components in Mapreduce and Beyond

15:00 – 15:20

Dr. Derek Murray , Microsoft Research- Incremental, iterative and interactive data analysis with Naiad

15:20 – 15:35
Coffee Break

15:35 – 15:55

Dr. Pankaj Gupta, Twitter – WTF: The Who to Follow Service at Twitter

15:55 – 16:15

Aapo Kyrola, CMU - What can you do with GraphChi – what’s new?

16:15 – 16:35

Dr. Lei Tang – Walmart Labs - Adaptive User Segmentation for Recommendation

16:35 – 16:55

Molham Aref, LogicBlox - Datalog as a foundation for probabilistic programming

16:55 – 17:15

Dr. Steven Hillion, Alpine Data Labs – General implementation methods for machine-learning algorithms on billions of rows and millions of features

17:15 – 19:00
Poster & Demo session

Aydin Buluc, LNL – Parallel software for high-performance and high-productivity graph analysis.
Bryan Thompson, Systap – GAS Engine for the GPU.
Norbert Martínez, Andrey Gubichev , Alex Averbuch, LDBC -Linked Data Benchmark Council – an initiative to standardize graph systems benchmarking
Norbert Martínez Sparsity technologies DEX: a High-Performance Graph Database Management System
Valeria Nikolaenko ,Stanford – Privacy-Preserving Ridge Regression on Hundreds of Millions of Records
Ameet Talwalkar, Bekereley – MLBase
George Ng, YarcData – YarcData:  Enabling discovery at speed and scale.
Radhika Tekkath, Agivox – A Deeper Dive into Understanding User Interest in News and Blogs
Eiko Yoneki (Universityof Cambridge); Amitabha Roy (EPFL) - Scale-up Graph Processing: A Storage-centric View
Paul Hofmann, SaffronTech – Predicting Threats For The Gates Foundation — Protecting The People, Investment, Reputation and Infrastructure - Large Scale Machine Learning on Sparse Graphs
Eriko Nurvitadhi, Intel - GraphGen: Compiling Graph Applications onto Accelerator-Based Platforms


Joseph Gonzalez & Reynold Xin, Berkeley AMP Lab – GraphX: Interactive Graph Mining
Shivaram Venkataraman & Kyungyong Lee Bekereley/HP Labs – Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices
Ely Kahn, Sqrrl - Sqrrl + Apache Accumulo = Massively Scalable Graphs
Jans Aasman, Allgero Graph -Exploring and discovering new patterns in graphs using Gruff and AllegroGraph
Jan Neumann, Comcast-  Personalized Recommendations at Comcast
Murat Can Cobanoglu, Pitt/CMU - Repurpose drugs by running collaborative filtering algorithms on pharmacological datasets
Tim Wilson, smarttypes.org – The map equation: using information theory to analyze your markov transition matrix
Matthias Broecheler,   Aurelius -   The Aurelius Graph Cluster – Graph Computing at Scale
Jason Riedy, USF – STING: High-Performance Analysis for Streaming, Graph-Structured Data
Francisco Martin, Poul Petersen, Adam Ashenfelter- BigML – Machine Learning Made Easy

Platinum Sponsors

Gold Sponsors







Instant Sponsors

Media Sponsors

Systems Presented:

 Prof. Vahab Mirrokni will discuss clustering @ Google scale.

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.

Intel GraphBuilder is a software for creating graphs out of raw data, utilizing Hadoop for parallel graph creation. Dr. Theodore Willke from Intel Labs will present Intel Labs work in this domain.

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.

Presto is a distributed framework for speeding up R computations by HP Labs. Shivaram Venkataraman from Bekreley and Kyungyong Lee will present Persto.

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.

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