Dr. Kary Myers, Statistical Sciences Group, Los Alamos National Laboratory
(Please note this is not the usual IDC seminar time, and the location is to be confirmed)
As computer simulations continue to grow in size and complexity, they present a challenging example of big data. Simulation output may exceed both the storage capacity and the available bandwidth for transfer to storage. In this talk I’ll describe an in situ approach — i.e., one that embeds calculations in the simulation itself — for efficiently approximating a complex simulation using piecewise linear fitting. This significantly reduces the data transfer and storage requirements while facilitating post processing and reconstruction of the simulation. I’ll illustrate the method with a massively parallel radiation-hydrodynamics simulation performed by Korycansky et al. (2009) to support NASA’s 2009 Lunar Crater Observation and Sensing Satellite mission.
About the Speaker:
Since 2006 Kary Myers has been a scientist in the Statistical Sciences group at Los Alamos National Laboratory. She earned her PhD from Carnegie Mellon’s Statistics Department and her MS from their Machine Learning Department. At Los Alamos she’s been involved with projects examining electromagnetic measurements, large scale computer simulations, and chemical spectra from the Mars Science Laboratory. She serves as an associate editor for the Annals of Applied Statistics and the Journal of Quantitative Analysis in Sports.
Dr Shujun Li
Observer-resistant password systems (ORPSs, also known as human authentication against observers or leakage-resilient password systems)have been studied since the early 1990s in both cryptography and computer security contexts, but until today a both secure and usable ORPS remains an open question to the research community. The concept of ORPS can be used to cover a large family of attacks against password-based human authentication systems such as shoulder surfers, hidden cameras, man-in-the-middle, keyloggers and other malware. A key assumption of ORPS is that human users must respond to authentication challenges without using any computational devices (which are considered untrusted). In other words, the threat model behind ORPSs assumes that other than the human user’s brain, nothing is trusted. The main security requirement is to avoid disclosure of the shared secret between the human user and the verifier (i.e., password) even after a practically large number of authentication sessions observed by untrusted parties.