Network Data: Dealing with Incompleteness and Bias

Methods used to construct networks and the resulting bias and incompleteness draw limited attention.

The Future of High Performance Scientific Computing is Anything but Clear

Cutting-edge scientific computing has relied for decades on what seems to be a never-ending parade of faster and faster computers.

Mathematical modeling can be taught at every stage of a student’s mathematical education, from kindergarten to undergraduate school.

A workshop in honor of Jerry Ericksen's 90th birthday reviews his impact on the fields of materials science.

In honor of Math Awareness Month, Randy Paffenroth discusses big data, algorithmic predictions, and deep learning.

At the 2016 Joint Mathematics Meetings this January, SIAM hosted a panel in collaboration with the AMS and the MAA.

Dan Gusfield points out how two recent New York Times articles misinterpreted elementary concepts in probability and statistics.

Electricity poses a challenge; an electrical network should carry power in an amount that is “enough” but not “too much.”

Karl Kempf of Intel Corporation discusses the reliability and influence of intuition when making big-business decisions.

The power grid in the U.S. and throughout the world is implementing new technologies to realize the “smart grid” vision.

In the upstream oil and gas industry, decision-making is based on models that reconstruct seismic data into three-dimensional images.

The Obama administration declared food security a federal priority area, presenting opportunities for modeling and computational expertise.

Des Higham, section editor of SIAM Review, gives an overview of the "Survey and Review" paper in the September 2015 issue.

Computational scientists in physics, chemistry, biology, and engineering have been experiencing their own big data revolution.

Ulrich Rüde explains the underperformance of many novel algorithms and the creative research necessary to design efficient HPC software.