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.
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, GraphLab Inc. & University of Washington: Graphs at Scale with GraphLab
Prof. Joe Hellerstein – Professor, UC Berkeley and Co-Founder/CEO, Trifacta – Productivity for Data Analysts: Visualization, Intelligence and Scale
Prof. Mark Oskin, University of Washington, Grappa graph engine.
Prof. Christopher Re, University of Wisconsin-MadisonThe Thorn in the Side of Big Data: too few artists
Prof. S V N Vishwanathan, Purdue NOMAD: Non-locking stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix factorization
Prof. Michael Mahoney, Stanford – Randomized regression in parallel and distributed environments
LUNCH sponsored by LexisNexis
Dr. Theodore Willke, Intel Labs Intel GraphBuilder 2.0
Dr. Avery Ching, Facebook – Graph Processing at Facebook Scale
Prof. Vahab Mirrokni, Google – Large-scale Graph Clustering in MapReduce and Beyond
Dr. Derek Murray , Microsoft Research- Incremental, iterative and interactive data analysis with Naiad
Dr. Pankaj Gupta, Twitter – WTF: The Who to Follow Service at Twitter
Aapo Kyrola, CMU – What can you do with GraphChi – what’s new?
Dr. Lei Tang – Walmart Labs – Adaptive User Segmentation for Recommendation
Molham Aref, LogicBlox – Datalog as a foundation for probabilistic programming
Poster & demo session social hour beer & snacks is sponsored by Yelp!
- 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
- 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