Computational and Inferential Methods for High Dimensions and Massive Datasets (Spring 2013)

Principal Instructors: Various

Course Day and Time: Course was held at SAMSI (driving directions) in RTP on Tuesdays, 4:30-7:00 p.m. in Room 150.

Schedule: First class Tuesday, January 8, 2013; last class day, Tuesday, April 16, 2013

This course focused on fundamental methodological questions of statistics, mathematics and computer science posed by massive datasets, with applications to astronomy, high energy physics, and the environment.

Topics included:

• Data: storage and transfer of massive datasets, missing and noisy data, complex data structures
• Computing: efficient computational algorithms, simulation

Visualization: data visualization to enhance human understanding

• Statistical Inference: problems and opportunities in high dimensional data; false discovery rates; regularization, Bayes and empirical Bayes; parametric, semi-parametric and non- parametric modeling; leveraging algorithms and computer resources

Registration for this course is processed through your respective university:
Duke: STA 790.01
NCSU: MA 810.002 and ST 810.010
UNC:STOR 892.1

Questions about the course or the MD program should be emailed to [email protected]