Courses

IPAM Graduate Summer School on Intelligent Extraction of Information from Graphs and High Dimensional Data,
UCLA, July 11 - 29, 2005

Organizing Committee: Kevin Vixie (LANL), Edmond Chow (LLNL), Tina Eliassi-Rad (LLNL), Yann LeCun (NYU), Carey Priebe (Johns Hopkins)

In recent years, there has been a rapidly increasing demand for targeted analysis of large data streams and large networks. One of the main goals has been identification of key features: face recognition in video streams and voice recognition in audio streams are two examples. Another goal has been inference of relationships: pattern discovery in large databases and determination of key links in social networks. At the same time, a number of scientific disciplines have come together to develop a theory for the analysis of high-dimensional data, as well as for the analysis of dynamic processes on massive graphs. The new techniques and new mathematics coming out of this line of research are ideally suited to a wide range of applications.

The summer school is intended for graduate students and postdocs, as well as more senior researchers interested in focusing their efforts on these mathematical challenges and crucial applications.

Tutorial lecture series will be integrated with more focused single lectures emphasizing state-of-the-art techniques, application areas, real-life challenges and datasets. Topics will include:

• Dimensionality reduction and high-dimensional data modeling
• Unsupervised learning: image metrics, image reconstruction and kernel methods
• Supervised learning: image detection and recognition, speech recognition
• Structural discovery on graphs, link analysis, relational data mining
• Social networks and network dynamics

www.ipam.ucla.edu/programs/gss2005