GraphLab Conference 2014

GraphLab Conference 2014

 

We are pleased to announce that the 3rd annual GraphLab Conference has concluded with enormous success. Our two-day event drew hundreds of participants in the day one general program as well as a full second day of hands-on training. Data scientists, engineers and big data industry technical stake holders joined our marquee list of sponsors, speakers and demo providers to exchange ideas, showcase innovation and help support our goal of taking everyone with big data ambitions from inspiration to production.

Below is the substance packed keynote from our CEO which contains more than a few applause worthy demos of simultaneous analytics on tables and graphs, analysis of 1TB of data on a laptop and deep learning.

Watch the Keynote

Day 1: Monday, July 21, 2014

General Admission. Registration opens at 8:00am.
Session 1: Data Product Pipeline in Practice
9:00am Prof. Carlos Guestrin Co-Founder & CEO, GraphLab Keynote: GraphLab Strategy, Vision and Practice   VIDEO | PDF
10:10am Baldo Faieta Social Computing Lead, Adobe Systems Algorithms for Creatives Talent Search using GraphLab   VIDEO
10:30am Amit Moran Chief Data Scientist, Crosswise Customer Spotlight: Crosswise   VIDEO
10:40am Coffee Break (20 mins)
Session 2: Data Science
11:00am Alice Zheng Director of Data Science, GraphLab Machine Learning Toolkits in GraphLab Create   VIDEO | PDF
11:20am Karthik Ramachandran, Erick Tryzelaar Lab41 Dendrite large scale graph analytics
11:40am Tao Ye Sr. Scientist, Pandora Internet Radio Large scale music recommendation @ Pandora
12:00pm Prof. Alex Smola CMU and Google Scaling Distributed Machine Learning with the Parameter Server
12:20pm Jonathan Dinu Co-Founder, Zipfian Academy Customer Spotlight: Zipfian Academy   VIDEO
12:30pm Lunch (70 mins)
Session 3: Data Engineering
1:40pm Yucheng Low Co-Founder & Chief Architect, GraphLab Scalable Data Structures: SFrame & SGraph   VIDEO | PDF
2:00pm Prof. Joe Hellerstein Co-Founder & CEO, Trifacta Data, DSLs and Transformation: Research and Practice   VIDEO
2:20pm Reynold Xin Co-Founder, Databricks Unified Data Pipeline in Apache Spark   VIDEO
2:40pm Wes McKinney Founder & CEO, DataPad Fast Medium Data Analytics at Scale   VIDEO
3:00pm Coffee Break (20 mins)
Session 4: Deployment
3:20pm Rajat Arya Senior Software Engineer, GraphLab Deployment with GraphLab Create   VIDEO | PDF
3:40pm Milind Bhandarkar Chief Scientist, Pivotal The Zoo Expands: Labrador ♥ Elephant thanks to Hamster   VIDEO
4:00pm Prof. Vahab Mirrokni Google Research ASYMP: Fault-tolerant Graph Mining via ASYnchronous Message Passing
4:20pm Josh Wills Director of Data Science, Cloudera What Comes After The Star Schema?   VIDEO
4:40pm Dr. Markus Weimer Microsoft Research REEF: Towards a Big Data stdlib   VIDEO
Session 5: Networking and Demos (5:00-7:00pm)

Day 2: Tuesday, July 22, 2014

Training Admission. Registration opens at 8:00am.
GraphLab Create Hands-on Training
The goal of the day is to teach participants how to build a machine learning system at scale from prototype to production using GraphLab Create. A laptop is required to participate.
9:30am Alice Zheng Director of Data Science, GraphLab Introduction
9:45am Yucheng Low Co-Founder & Chief Architect, GraphLab Prepping Data for Analysis: Using GraphLab Create Data Structures and GraphLab Canvas
10:30am Coffee Break (15 mins)
10:45am Srikrishna Sridhar Data Scientist, GraphLab Supervised Learning: Regression and Classification
11:15am Brian Kent Data Scientist, GraphLab Unsupervised Learning: Clustering, Nearest Neighbors, Graph Analysis
11:45am Hands-on Training Exercises and Lunch
1:45pm Chris Dubois Data Scientist, GraphLab Recommender Systems and Text Analysis
2:15pm Coffee Break (15 mins)
2:30pm Rajat Arya Sr. Software Engineer, GraphLab Deployment
3:15pm Hands-on Training Exercises
4:00pm Danny Bickson Co-Founder & Data Scientist, GraphLab Practical Data Science Tips
4:45pm Alice Zheng Director of Data Science, GraphLab Closing Remarks

Interested in sponsoring the GraphLab Conference 2014? There are multiple tiers of opportunities available ranging from Platinum to Bronze packages. There may also be some space available at our Exhibitor tables. Contact us to inquire.


Media Sponsors

The second GRADES workshop, to be held on June 22, 2014 at the premier database systems conference ACM SIGMOD/PODS in Snowbird (Utah), attracts database systems architects, graph data management researchers and practitioners to describe and discuss scenarios, experiences and system internals encountered in managing and analyzing large quantities of graph-shaped data. The GRADES workshop is co-sponsoring the third GraphLab Conference.
O'Reilly spreads the knowledge of innovators through its technology books, online services, magazines, research, and tech conferences. Since 1978, O'Reilly has been a chronicler and catalyst of leading-edge development, homing in on the technology trends that really matter and galvanizing their adoption by amplifying "faint signals" from the alpha geeks who are creating the future. An active participant in the technology community, O'Reilly has a long history of advocacy, meme-making, and evangelism.
 
MMDS 2014 MMDS 2014 (Workshops on Algorithms for Modern Massive Data Sets) will address algorithmic and statistical challenges in modern large-scale data analysis. It will take place on the UC Berkeley campus Tuesday, June 17 through Friday, June 20; and it will have talks by experts from academia and industry as well as workshop sessions and poster presentations. Planned workshop themes include: statistical data analysis; industrial and scientific applications; algorithmic approaches to data; graph and matrix methods; large scale computing and machine learning.
The HPC Advisory Council’s mission is to bridge the gap between high-performance computing (HPC) use and its potential, bring the beneficial capabilities of HPC to new users for better research, education, innovation and product manufacturing, bring users the expertise needed to operate HPC systems, provide application designers with the tools needed to enable parallel computing, and to strengthen the qualification and integration of HPC system products.

 

Companies who would like to get involved by presenting / sponsoring should email Danny Bickson: bickson@graphlab.com

 

Detailed exhibitions

Dr. Ari Tuchman: Beyond Sentiment and Buzz: Extracting the Answers that Matter Though Predictive Correlations from Unstructured Chatter
David Gerster, VP Data Science at BigML: BigML: Machine Learning Made Easy
Paul Hoffman: Large Scale Machine Learning on Sparse Graphs
Dr. Jans Aasman, CEO, Franz Inc. Drag and Drop Graph Query Generator
Dr. Zhisong Fu, Mike Personick, and Bryan Thompson: Ultra fast graph mining on GPUs.
TBA
TBA
Tristan Zajonc, co-Founder and CEO and Anand Patil, Sense: Agile Data Science with Sense
Dr. David Talby: Beyond ML basics: Localized, evolving, hybrid & automated modeling at scale
Simon Chan: An Open Source Machine Learning Server for Developers
TBA
Adam Fuchs, CTO Sqrrl: How To Build Secure, Massively Scalable Graphs with Sqrrl
Dr. Steven Hillion, Alpine Data Labs:

Fast classification algorithms on Hadoop

Jacob Nelson: Grappa graph engine
Prof. Joshua Bloom, wiso.io: Machine-learning Driven Automated Insight Workflows
Dr. Matthias Broecheler, Titan – Scalable Graph Computing in Real-time and Offline
TBA
Prof. Eric Xing: Petuum – a new distributed machine learning framework
Corey Lanum, General Manager of North America, Cambridge Intelligence: How to make useful interactive graph visualizations
Kevin Madden, Technology Director, Tom Sawyer Software: Dynamic Data-Driven Graph Visualization and Analysis
Dr. Jason Riedy, Georgia Tech: STING: High-Performance Analysis for Streaming Graph Data
Dr. Hassan Chafi, Oracle: Graph Analytics Research at Oracle Labs
Dr. Achim Rettinger, EPPICS: Cross-lingual Cross-modal Analytics of Dynamic Graphs
Peter Wang, Continuum Analytics: Agile Data Exploration & Visualization with Blaze and Bokeh
Graphistry Leo Meyerovich, Graphistry: Scaling Visualization with Design and GPUs
Chris Yang: Domino: A Platform-as-a-Service for Enterprise Data Science
Dr. Fernando Perez, Berkeley: IPython: from interactive computing to computational narratives
Dr. Linas Baltrunas and Dr. Dionysos Logothetis:, Telefonica Research, and Georgos Siganos, Qatar Computing Research Institute: Grafos.ml: Tools for large scale ML and graph analysis
Ms. Raquel Pau, Sparsity Technologies: DAMA-UPC & Sparsity Technologies
SriSatish Ambati, co-founder and CEO: TBA
Jonathan Dinu, CTO Zipfian Academy: TBA
Demian Bellumio, COO Senzari: MusicGraph
Sutanay Choudhury, Pacific Northwest National Lab: M&Ms4Graphs: Multi-scale, Multi-dimensional Graph Analytics Tools for Cyber-Security
Michael Zeller, CEO Zementis: Accelerate predictive analytics with massively parallel scoring
Sébastien Heymann CEO and Jean Villedieu Co-founder, Linkurious: How can graph visualization help understand graphs faster?
Amit Moran, Crosswise: TBA
Vishal Vaidyanathan, Royal Caliber: VertexAPI2: A Graphlab-Style API for GPU Graph Computation
Murat Can Cobanoglu, CMU & Pitt: BalestraWeb: Efficient, online drug-target interaction prediction
Nik Reed, Co-Founder Ravel Law: TBA
Keiichiro Ono, UCSD Cytoscape: an open source software platform for complex networks analysis
Liz Derr, Simularity: TBA
Florian Douetteau, Dataiku: write your own data story
David Andrzejewski, Sumo Logic: Machine Data Analytics with Sumo Logic
Matt Sundquist and Andrew Seier, Plotly: Collaborative Python Graphing
Leon Guzenda, Co-Founder Objectivity: Graphing The Internet Of Things
Jamison Feramisco, MD, PhD and Kim Branson, PhD, Lumiata: Medical Graph Analytics in Action
Vasiliki Kalavri, KTH: Apache Flink demonstration

Poster Presentations

  • Aydin Buluc, Berkely Lab - Communication-avoiding graph algorithms
  • Semih Salihoglu, Stanford - HelP: High-level Primitives For Large-Scale Graph Processing
  • Seif Haridi and Vasiliki Kalavrii, KTH: Asymmetry in Large-Scale Graph Analysis, Explained
  • Jiwon Seoi, Stanford: SociaLite: a Hadoop-compatible query language for large-scale graph analysis
  • Sebastian Schelter, TU Berlin: Bringing Algebraic Semantics to Apache Mahout
  • Daniel McEnnis: Prestige and Closeness in GraphLab
  • Shobeir Fakhraei,Tagged: Social network analysis to identify spam
  • Arthur Keen, VP Solutions Architecture and Tim Milovich, VP Business Development, SparqlCity: SPARQLverse: A Scalable High Performance Engine for Graph Analytics
  • Sri Kanajan, Mijail Gomez, Prasad Telukuntla, Zipfian Academy: Winning Tata Competition with GraphLab
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