Biography

I am a senior graduate student working with Carlos Guestrin in the Machine Learning Department at Carnegie Mellon University (CMU). My research addresses the challenges of designing and building large-scale machine learning algorithms and systems. In particular, my thesis work focuses on large-scale structured machine learning using probabilistic graphical models that are capable of reasoning about billions of related random variables. The resulting algorithms and systems have achieved state-of-the-art performance in tasks ranging from predicting ad preferences in social networks to solving complex protein modeling tasks. As part of my thesis work we created GraphLab , a framework that dramatically simplifies the design and implementation of high-performance large-scale machine learning systems.

I am a recipient of the ATT Labs Graduate Research Fellowship and the National Science Foundation Graduate Research Fellowship. Some of my work is also supported by the ONR Young Investigator Program grant N00014-08-1-0752, the ARO under MURI W911NF0810242, and the ONR PECASE-N00014-10-1-0672.

I completed my BS in computer science at the California Institute of Technology (Caltech) and my MS in Machine Learning at Carnegie Mellon University. As part of my Masters work I developed non-parametric Bayesian models to estimate wireless signal quality in sensor networks.

News

• I am co-organizing the NIPS’11 Big Learning Workshop.
• Checkout our new parallel machine learning framework: GraphLab.
• We protested important machine learning issues at the G20 in Pittsburgh. To learn more about how you can Support Vector Machines checkout out our entertaining pictures.

Recent Publications

• Amr Ahmed, Mohamed Aly, Joseph Gonzalez, Shravan Narayanamurthy, Alex Smola (2012). "Scalable Inference in Latent Variable Models." Conference on Web Search and Data Mining (WSDM).
• Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin (2011). "Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees." In Artificial Intelligence and Statistics (AISTATS). [code release] [abs/bib] [pdf] [pptx]
• Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein (2010). "GraphLab: A New Parallel Framework for Machine Learning." Conference on Uncertainty in Artificial Intelligence (UAI). [code release] [abs/bib] [pdf]
• Joseph Gonzalez, Yucheng Low, Carlos Guestrin (2010). "Parallel Inference on Large Factor Graphs." Book chapter in Scalable MachineLearning.
• Joseph Gonzalez, Yucheng Low, Carlos Guestrin, David O`Hallaron (2009). "Distributed Parallel Inference on Large Factor Graphs." Conference on Uncertainty in Artificial Intelligence (UAI). [abs/bib] [pdf] [pptx]
• Joseph Gonzalez, Yucheng Low, and Carlos Guestrin (2009). "Residual Splash for Optimally Parallelizing Belief Propagation." In Artificial Intelligence and Statistics (AISTATS). [abs/bib] [pdf] [pptx]

Talks

• GraphLab2 Talk at CMU [PPTX] 10/3/2011
• GraphLab talk at the IDGA Data Center Conslidation Summit. [PPTX] 10/3/2011
• Early GraphLab2 Talk at Yahoo! Research. [PPTX] 9/9/2011
• GraphLab talk at Berkeley. [PPTX] 9/7/2011
• GraphLab talk at Greenplum EMC. [PPTX] 8/24/2011
• GraphLab talk at LinkedIn. [PPTX] 8/2/2011
• GraphLab talk at Cloudera. [PPTX] 7/29/2011
• GraphLab talk at Facebook. [PPTX] 7/19/2011
• Thesis Proposal [PPTX]
• Splash Gibbs Sampling. [PPTX] AISTATS 2011
• Parallel Belief Propagation [PPTX] ML Lunch 2009
• Splash Belief Propagation [PPTX] UAI 2009