Matlab Seminar, Thursday 11/12/2009


Matlab Seminar, Thursday 11/12/2009

All interested students, researchers, and faculty are encouraged to register.

The MathWorks will be presenting two seminars on using MATLAB on Thursday, November 12, 2009. The morning session is scheduled at the Main Campus and the afternoon session is at RSMAS.Register now at

Technical computing at the University of Miami
Presenter: Saket Kharsikar, Application Engineer

Agenda – Session I
Whitten University Center, Flamingo D

8:45 a.m. – 9:00 a.m     Registration sign-in. Walk-ins are welcome.
9:00 a.m. – 11:30 a.m   Data Acquisition, Analysis, and Visualization using MATLAB (overview)

During this session we will use examples to demonstrate how to acquire, analyze, and visualize data through mathematical, statistical, and engineering functions that support common engineering operations. This session is designed to be an overview of the MATLAB technical computing environment. R2009b will be used for this demonstration.

Highlights include:

  • Importing data from various sources
  • Performing statistical analysis
  • Automating analysis via automatic m-code generation
  • Building GUIs and generating reports

Agenda – Session II
SLAB Seminar Room, Rosenstiel School of Marine and Atmospheric Science

1:15 p.m. – 1:30 p.m. Registration sign-in. Walk-ins are welcome.
1:30 p.m. – 3:30 p.m. Speeding Up MATLAB Applications with Parallel Computing

We will discuss and demonstrate how to perform parallel and distributed computing in MATLAB to boost execution speed on computationally and data-intensive problems. We will introduce you to parallel processing constructs such as parallel for-loops, distributed arrays, parallel
numerical algorithms and message-passing functions that let you implement task and data parallel algorithms in MATLAB at a high level without programming for specific hardware and network architectures.

Highlights include:

  • Applications of parallel computing
  • Implicit multi-threaded computations
  • Interactive task and data parallel applications
  • Interactive applications to scheduled applications
  • Scaling your work up to a computer cluster