Data science has become increasingly important in science, engineering and business. Breakthroughs in data analysis, including deep learning, require mathematical and statistical innovations — leading to the creation of a new domain at the intersection of inferential and computational thinking. Despite many recent innovations, data scientists have barely begun to tackle the hardest problems. Thanks to a new journal, foundational mathematical and statistical advances in data science are being put on center stage.
To help guide the new journal, Tammy Kolda has been named founding editor-in-chief of the SIAM Journal on Mathematics of Data Science (SIMODS), published by the Society for Industrial and Applied Mathematics (SIAM).
Tammy is joined by section editors Alfred Hero (University of Michigan), Michael Jordan (University of California, Berkeley), Robert D. Nowak (University of Wisconsin-Madison), and Joel A. Tropp (California Institute of Technology). The team has assembled a renowned team of associate editors from top universities, including Carnegie Mellon, Duke, MIT, Princeton, Stanford, UCLA and others.
The electronic-only publication’s intention is to focus on the mathematical constituency’s role in the ascent of data science, while strengthening the connections to complementary communities.
“As we move forward, SIMODS will establish the importance of mathematics in the fast-growing domain of data science and curate the best work at this intersection of mathematics, statistics, computer science, network science and signal processing,” Tammy says. “Papers published in SIMODS will develop useful theories, propose new algorithms, describe clever implementations and share novel methodologies across disciplines.”
Tammy says she anticipates that these journal articles will not only be useful to data science but also have ramifications for traditional areas of applied mathematics research since this research incorporates methods that have advanced in the data science regime.
During its first six months, the journal received more than 100 submissions, breaking all records for new SIAM journals. A few examples of topics to be published include analysis of deep neural networks, sparse coding in signal processing, statistical sampling techniques, analysis of data clouds, security of pooled data and low-rank approximability. The first issue will be published in early 2019.
Check out SIAM’s video: “Mathematics of Data Science — Data Science is Everywhere,” created with Tammy’s guidance to promote the new journal and explain the field to non-experts.