Posts Tagged ‘CSE13 video’
At the SIAM Conference on Computational Science and Engineering held in Boston in February 2013, professional mathematicians from various fields discussed the significance of big data and the importance of mathematical modeling to make sense of and interpret all that data in various fields from social networks and epidemiology to climatology. Watch a brief video with highlights!
At the SIAM Conference on Computational Science and Engineering held in Boston in February, mathematicians from academia, industry and government labs discussed and answered questions about the various career options students and those in their early careers could pursue in the fields of computational science and engineering. Watch video highlights!
Telescope projects now routinely obtain massive digital movies of the dynamic night’s sky. But given the growing data volumes, coupled with the need to respond to transient events quickly with appropriate followup resources, it is no longer possible for people to be involved in the real-time loop.
At the SIAM Conference on Computational Science and Engineering held in Boston in February 2013, Dr. Joshua Bloom discussed the development of robotic telescopes, autonomous follow-up networks, and a machine-learning framework that act as a scalable, deterministic human surrogate for discovery and classification in astronomical imaging. Read the rest of this entry »
Computational models of the human heart can be very useful in studying not just the basic mechanisms of heart function, but also to analyze the heart in a diseased state, and come up with methods for diagnosis and therapy.
Dr. Natalia Trayanova’s Computational Cardiology Lab at the Johns Hopkins University is doing just that—her group uses mathematical models to look at cardiac function and dysfunction, examining the mechanisms behind disorders such as cardiac arrhythmias and pump dysfunction.
In a plenary lecture at the SIAM Conference on Computational Science and Engineering in February, Dr. Trayanova described how her lab uses imaging data from clinics, such as MRIs and CT scans, to create heart models. Using detailed information from such images, the team geometrically constructs 3-D computer models by incorporating information about chemical and protein interactions as well as cardiac fiber orientation. Read the rest of this entry »