In recent years, there has been a rapidly increasing demand for targeted analysis of large data streams and large networks. One of the 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.
Applications and connections to real challenges will be drawn from: data fusion, automated feature extraction, face and shape recognition, spectral and hyperspectral image analysis, relational data mining, link analysis and discovery, graph mining, social and transactional networks, robust network design (making networks hard to break), optimal epidemic intervention (making networks easy to break), and hidden state inference (where are targets based on indirect measurements).
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 applications. The program is organized as follows.
Edmond Chow
(D.E. Shaw Research & Development)
Tina Eliassi-Rad
(Lawrence Livermore National Laboratory)
Yann LeCun
(New York University)
Carey Priebe
(Johns Hopkins University)
Kevin Vixie, Chair
(Washington State University)