Following the success of the first GraphLab workshop, we hosted the 2nd annual GraphLab Workshop on Monday July 1st at the Nikko Hotel in downtown San Francisco. The first GraphLab workshop brought together over 300 researchers from over 100 companies to discuss challenges of applied large scale machine learning.

graphlab 2013

Last year’s GraphLab workshop – photo by Shon Burton

Over 550 of the top academics, data scientists, and users of large-scale machine learning and graph computation to joined us in the conversation to define the emerging field of Big Learning. We hosted talks and demos from leaders in the field including, Pregel (Google), Giraph (Facebook), Cassovary (Twitter), Naiad (Microsoft Research), GraphBuilder (Intel Labs), Presto (HP Labs), Grappa (UW), Combinatorial BLAS (LBNL/UCSB), Allegro Graph (Franz), Neo4j (Neo Technology), Titan (Aurelius), DEX (Sparsity Technologies), YarcData and others!

Prof. Carlos Guestrin
prof carlos guestrin

Prof. Carlos Guestrin, GraphLab Inc. & University of Washington: Graphs at Scale with GraphLab

SLIDES (pptx) — VIDEO

Prof. Joe Hellerstein
prof. joe hellerstein

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

SLIDES (pdf)VIDEO

Prof. Mark Oskin
prof mark oskin

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

VIDEO

Prof. Christopher Re
christopher re

Prof. Christopher Re, University of Wisconsin-MadisonThe Thorn in the Side of Big Data: too few artists

Prof. S V N Vishwanathan
prof. SVN Vishwanathan

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

SLIDES (pdf)VIDEO

Prof. Michael Mahoney
prof michael mahoney

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

SLIDES (pdf) — VIDEO

LUNCH sponsored by LexisNexis

Dr. Ted Willke
Ted Willke MLconf

Dr. Theodore Willke, Intel Labs Intel GraphBuilder 2.0

SLIDES (pdf)VIDEO

Dr. Avery Ching
dr avery ching

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

SLIDES (pdf)VIDEO

Prof. Vahab Mirrokni
prof vahab mirrokni

Prof. Vahab Mirrokni, Google – Large-scale Graph Clustering in MapReduce and Beyond

SLIDES (pdf)VIDEO

Dr. Derek Murray
derek murry

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

SLIDES (pdf)VIDEO

Dr. Pankaj Gupta
pankaj gupta

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

VIDEO

Aapo Kyrola
aapo kyrola

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

Slides (pptx)Video

Dr. Lei Tang
lei tang

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

SLIDES (pdf)VIDEO

Molham Aref
Molham aref

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

VIDEO

Poster & demo session social hour beer & snacks is sponsored by Yelp!
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
  • Asghar Dehghani, Alpine Data Labs: A parallel implementation of kernel machines

Demos:

  • 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
  • Harsh Agrawal, Virginia Tech – CloudCV: Large Scale Distributed Computer Vision on the Cloud
  • Baldo Faieta, Adobe – ‘Likes’ diffusion over social networks

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