Michael Aivazis

TL;DR: The use of Python has become the de facto standard for exploratory, interactive, and computation-driven scientific research as mentioned in this paper, and several of the core Python libraries and tools used in scientific research have been discussed.

TL;DR: The Community Seismic Network (CSN) as discussed by the authors is a dense open seismic network based on low-cost sensors, which is designed for robustness and to dynamically handle the load of impulsive earthquake events.

TL;DR: In this article, a framework for massively parallel optimization and rigorous sensitivity analysis is presented, which enables these motivating questions to be addressed quantitatively as global optimization problems, such as: "How well does my computational model represent reality?", "What are the most important parameters in the problem?", and "What is the best next experiment to perform?" are fundamental in solving scientific problems.

TL;DR: In this article, the authors derived the distribution of subsurface fault slip from geodetic coseismic offsets using a Bayesian approach, including a full description of the data covariance and accounting for errors in the elastic structure of the crust.

TL;DR: The design behind an optimization framework, and also a framework for heterogeneous computing that when utilized together, can make computationally intractable sensitivity and optimization problems much more tractable.