GraphLab presentations

Today is the start of the SPARS13 meeting. Volkan tells me the plenary talks will be taped. Here is the program.
The GraphLab conference took place a week ago. Danny tells me that some videos should be available but for the time being here is the program with, for some of them, the presentation slides:

08:00 – 09:00	Registration and reception
09:00 – 10:15		Prof. Carlos Guestrin, GraphLab Inc. & University of Washington:Large Scale Machine Learning and Graphs Slides (pptx)
10:15 – 10:45		Prof. Joe Hellerstein – Professor, UC Berkeley and Co-Founder/CEO, Trifacta - Productivity for Data Analysts: Visualization, Intelligence and ScaleSlides (pdf)
10:45 – 11:05	Coffee Break
11:05 – 11:35		Prof. Mark Oskin, University of Washington Grappa graph engine.
11:35 – 12:05		Prof. Christopher Re, University of Wisconsin-Madison The Thorn in the Side of Big Data: too few artists
12:05 – 12:25		Prof. S V N Vishwanathan, Purdue NOMAD: Non-locking stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix factorization Slides (pdf)
12:25 – 12:45		Prof. Michael Mahoney, Stanford Randomized regression in parallel and distributed environments Slides (pdf)
12:45 – 14:00		Lunch sponsored by LexisNexis Jonathan Stephenson – LexisNexis - Discovering Structure in Crowd Behaviors
14:00 – 14:30		Dr. Theodore Willke, Intel LabsI ntel GraphBuilder 2.0
14:30 – 14:50		Dr. Avery Ching, FacebookGraph Processing at Facebook Scale Slides (pdf)
14:50 – 15:10		Prof. Vahab Mirrokni, Google - Large-scale Graph Clustering in MapReduce and Beyond
15:10 – 15:30		Dr. Derek Murray , Microsoft Research- Incremental, iterative and interactive data analysis with NaiadSlides (pdf)
15:45 – 16:05		Dr. Pankaj Gupta, Twitter – WTF: The Who to Follow Service at Twitter
16:05 – 16:25		Aapo Kyrola, CMU - What can you do with GraphChi – what’s new? Slides (pptx)
16:25 – 16:45		Dr. Lei Tang – Walmart Labs - Adaptive User Segmentation for Recommendation Slides (pdf)
16:45 – 17:05		Molham Aref, LogicBlox - Datalog as a foundation for probabilistic programming
17:05 – 19:00	Poster & Demo session	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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