If you like the videos and slides from GraphLab 2013, follow the authors for their latest research!
I created a listing of DBLP links (Linkedin links where I could not find a DBLP author listing) for the participants at GraphLab 2013.
Thought you might find it useful:
Presentations:
- Molham Aref, LogicBlox – Datalog as a foundation for probabilistic programming.
- Dr. Avery Ching, Facebook – Graph Processing at Facebook Scale.
- Prof. Carlos Guestrin, GraphLab Inc. & University of Washington: Graphs at Scale with GraphLab.
- Dr. Pankaj Gupta, Twitter – WTF: The Who to Follow Service at Twitter.
- Prof. Joe Hellerstein – Professor, UC Berkeley and Co-Founder/CEO, Trifacta – Productivity for Data Analysts: Visualization, Intelligence and Scale.
- Aapo Kyrola, CMU – What can you do with GraphChi – what’s new?
- Prof. Michael Mahoney, Stanford – Randomized regression in parallel and distributed environments.
- 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.
- Prof. Mark Oskin, University of Washington, Grappa graph engine.
- Dr. Lei Tang – Walmart Labs – Adaptive User Segmentation for Recommendation.
- Prof. S V N Vishwanathan, PurdueNOMAD: Non-locking stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix factorization.
- Dr. Theodore Willke, Intel LabsIntel GraphBuilder 2.0.
Posters:
- Aydin Buluc, LNL – Parallel software for high-performance and high-productivity graph analysis.
- Asghar Dehghani, Alpine Data Labs: A parallel implementation of kernel machines.
- Paul Hofmann, SaffronTech – Predicting Threats For The Gates Foundation — Protecting The People, Investment, Reputation and Infrastructure – Large Scale Machine Learning on Sparse Graphs.
- 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.
- George Ng (Linkedin), YarcData – YarcData: Enabling discovery at speed and scale.
- Eriko Nurvitadhi, Intel – GraphGen: Compiling Graph Applications onto Accelerator-Based Platforms.
- Ameet Talwalkar, Bekereley – MLBase.
- Radhika T[h]ekkath (Linkedin), Agivox – A Deeper Dive into Understanding User Interest in News and Blogs.
- Bryan Thompson, Systap – GAS Engine for the GPU.
- Eiko Yoneki (Universityof Cambridge); Amitabha Roy (EPFL) – Scale-up Graph Processing: A Storage-centric View.
Demos:
- Harsh Agrawal, Virginia Tech – CloudCV: Large Scale Distributed Computer Vision on the Cloud.
- Jans Aasman, Allgero Graph – Exploring and discovering new patterns in graphs using Gruff and AllegroGraph.
- Matthias Broecheler, Aurelius – The Aurelius Graph Cluster – Graph Computing at Scale.
- Murat Can Cobanoglu, Pitt/CMU – Repurpose drugs by running collaborative filtering algorithms on pharmacological datasets.
- Baldo Faieta, Adobe – ‘Likes’ diffusion over social networks.
- Joseph Gonzalez & Reynold Xin, Berkeley AMP Lab – GraphX: Interactive Graph Mining.
- Ely Kahn (Linkedin), Sqrrl – Sqrrl + Apache Accumulo = Massively Scalable Graphs.
- Francisco Martin (Linkedin), Poul Petersen (Linkedin), Adam Ashenfelter– BigML – Machine Learning Made Easy.
- Jan Neumann, Comcast- Personalized Recommendations at Comcast.
- Jason Riedy, USF – STING: High-Performance Analysis for Streaming, Graph-Structured Data.
- Shivaram Venkataraman & Kyungyong Lee Bekereley/HP Labs – Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices.
- Tim Wilson (Linkedin), smarttypes.org – The map equation: using information theory to analyze your markov transition matrix.