Monday, July 08, 2013

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 – 09:00Registration and reception
09:00 – 10:15Prof. Carlos Guestrin, GraphLab Inc. & University of Washington:Large Scale Machine Learning and Graphs Slides (pptx)
10:15 – 10:45Prof. Joe Hellerstein – Professor, UC Berkeley and Co-Founder/CEO, Trifacta - Productivity for Data Analysts: Visualization, Intelligence and ScaleSlides (pdf)
10:45 – 11:05Coffee Break
11:05 – 11:35Prof. Mark Oskin, University of Washington Grappa graph engine.
11:35 – 12:05Prof. Christopher Re, University of Wisconsin-Madison The Thorn in the Side of Big Data: too few artists
12:05 – 12:25Prof. S V N Vishwanathan, Purdue NOMAD: Non-locking stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix factorization Slides (pdf)
12:25 – 12:45Prof. Michael Mahoney, Stanford Randomized regression in parallel and distributed environments
12:45 – 14:00
 Lunch sponsored by LexisNexis Jonathan Stephenson – LexisNexis - Discovering Structure in Crowd Behaviors
14:00 – 14:30Dr. Theodore Willke, Intel LabsI ntel GraphBuilder 2.0
14:30 – 14:50Dr. Avery Ching, FacebookGraph Processing at Facebook Scale
14:50 – 15:10Prof. Vahab Mirrokni, Google - Large-scale Graph Clustering in MapReduce and Beyond
15:10 – 15:30Dr. Derek Murray , Microsoft Research- Incremental, iterative and interactive data analysis with NaiadSlides (pdf)

15:45 – 16:05Dr. Pankaj Gupta, Twitter – WTF: The Who to Follow Service at Twitter
16:05 – 16:25Aapo Kyrola, CMU - What can you do with GraphChi – what’s new? Slides (pptx)
16:25 – 16:45Dr. Lei Tang – Walmart Labs - Adaptive User Segmentation for Recommendation
16:45 – 17:05Molham Aref, LogicBlox - Datalog as a foundation for probabilistic programming
17:05 – 19:00Poster & Demo sessionPoster & 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
  • 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, – 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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