The Data Sciences Summer School, in conjunction with the LANL Mathematical Modeling and Analysis group, HPC and CCS Divisions, and UCLA, will be offering 10-15 positions this summer in Los Alamos, NM. The program is intended for students pursuing Masters degrees and PhDs; however, exceptional undergraduates entering their senior year will be considered. We welcome applicants from computer science, engineering, mathematics and the physical sciences.

Scientific Focus

The 2007 summer school will focus on tools for the extraction and measurement of data features and the application of these tools to image analysis problems. Geometric measure theory gives us tools to study sets, measures and functions. One project will develop new insights and algorithms for geometric measure theory, with a view to data analysis problems. These insights and tools will be then be applied to image and data analysis problems: this characterizes three other projects. At the moment, focus on three data sets is planned: shapes derived from placental development, videos of C. elegans swimming and fluorescing (fluorescence used to track neuronal behaviour), and video data of flocking phenomena in birds. Each of these data present challenging problems, the practical solution of which is valuable to the scientific domains from which they come.

One last point: theory and application will not be segregated by projects since this flies against our philosophy, but the geometric analysis project will have a more theoretical flavor while the others will have a more applied flavor, at least at the outset.

Projects

• Geometric Analysis with a View to Data Challenges
• Tracking Neuron Activity in Worms as they Move Across A Detector
• Modeling and Simulation of Flocking Behavior
• Shapes Measures for Placental Development and Health Evalutaion
• Algorithms on Structured Networks

Lectures and Special topics Courses

Click here for information on the data sciences summer school classes.

Organizers, Mentors and Lecturers

• Katharine Chartrand
• Tom Asaki
• Pete Schultz
• Matt Sottile
• Kevin R. Vixie
• Allon Percus
• Simon Morgan
• Wotao Yin
• William K. Allard
• Carolyn M. Salafia
• Melanie Asaki

The Summer School details

• The summer school will run 9 weeks, from June 25 to August 24, 2007
• The first 2 weeks of the summer school will consist of project orientation and initial lectures and tutorials. Participants will get in-depth exposure to all of the projects
• There will be an ongoing lecture series covering tools and methods pertinent to various projects.
• There will be several points throughout the 9 weeks that we review each other's work, get feedback, etc.
• We will have many visitors throughout the summer who will give talks, interact with participants, and take part in the projects.
• By the end of the summer, everyone will have written up their work and submitted it to a new open access "Journal of Data Driven Modeling and Analysis" that we are starting. Very briefly, this journal will be built on top of a preprint server (like the ArXiv based at Cornell) with added layers of review and other novel features.
• We will have a final workshop with an open invitation to other scientists to come and see the results of the projects, gives students more exposure and opportunities, etc. -- more on this soon.
• ... and of course, we won't forget the social aspects of life! More on that as the summer approaches.

Logistics

Students will receive payment comparable to that of LANL student employees. Follow this link under "Technical Structure".

Application

Applications for the summer school are no longer being accepted

Applications will be reviewed in three rounds until the positions are filled. The deadlines for the first three rounds are March 15, April 15, May 15. We will give a response of yes, no or possible within two weeks of the deadline.

Applications should be submitted on-line through this link.

Recommendations should be e-mailed to datasciences@gmail.com, with the subject line: "Your Last Name" recommendation.

Contact

Please contact datasciences@gmail.com for more information.