" rel="stylesheet">
Skip Main Navigation
Page Content
This event has ended

2013 Graphlab Workshop on Large Scale Machine Learning

MLconf- The Machine Learning Conference

Monday, July 1, 2013 from 8:00 AM to 7:00 PM (PDT)

2013 Graphlab Workshop on Large Scale Machine Learning

Ticket Information

Ticket Type Sales End Price Fee Quantity
Student Registration (Valid Student I.D. Required) Ended $50.00 $2.24
Last-Chance Registration Ended $300.00 $8.49
Instant Sponsorship Package   more info Ended $1,500.00 $9.95
On-Site --Day of event registration Ended $600.00 $9.95

Who's Going

Loading your connections...

Share 2013 Graphlab Workshop on Large Scale Machine Learning

Event Details

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 Posters:


  • 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.
<tr style="padding: 0px; border: 0px; margin: 0px; font: inherit; vertical-align: baselin
Have questions about 2013 Graphlab Workshop on Large Scale Machine Learning? Contact MLconf- The Machine Learning Conference
Attendee List Sort by: Date
Show More

When & Where

Hotel Nikko
222 Mason St
San Francisco, CA

Monday, July 1, 2013 from 8:00 AM to 7:00 PM (PDT)

  Add to my calendar


MLconf- The Machine Learning Conference

MLconf - The Machine Learning Conference gathers communities to discuss the recent research and application of Algorithms, Tools, and Platforms to solve the hard problems that exist within organizing and analyzing massive and noisy data sets.

Courtney Burton is the Founder and Executive Producer of MLconf. Nick Vasiloglou is the Technical Chair. 

  Contact the Organizer
2013 Graphlab Workshop on Large Scale Machine Learning
San Francisco, CA Events Class

Please log in or sign up

In order to purchase these tickets in installments, you'll need an Eventbrite account. Log in or sign up for a free account to continue.