DIBBS: Martha Palmer and James Martin
On Oct. 1, $31 million in grants were announced as part of the second year of the Data Infrastructure Building Blocks (DIBBs) program. The program supports the NSF’s priority goals “to improve the nation's capacity in data science by investing in the development of infrastructure, building multi-institutional partnerships to increase the number of U.S. data scientists and augmenting the usefulness and ease of using data. … Many of the benefits of ‘Big Data’ have yet to surface because of a lack of interoperability, missing tools and hardware that is still evolving to meet the diverse needs of scientific communities.”
Nearly $1.5 million of that award went to a cross-disciplinary CU-Boulder project called “Porting Practical Natural Language Processing (NLP) and Machine Learning (ML) Semantics from Biomedicine to the Earth, Ice and Life Sciences.” The research team includes Chris Jenkins of the Institute of Arctic & Alpine Research; Ruth Duerr of the National Snow and Ice Data Center; and Martha Palmer and James Martin of Computer Science.
Palmer is serving as the project’s principal investigator, while Martin will be lending his NLP expertise to the effort. He explained that while strides have been made in NLP for educational and medical applications, no one has yet applied it to the vast amounts of data being collected in other sciences.
CyberSEES: Qin Lv
On Oct. 15, $12.5 million in grants were announced through the Cyber-Innovation for Sustainability Science and Engineering (CyberSEES) program. According to the NSF, “the awards aim to advance the science of sustainability in tandem with advances in computing and communication technologies. The (grants) bring together teams of researchers from computer science and other disciplines to develop new tools, technologies and models that advance sustainability science.”
Over $650,000 from the program went to a team of CU-Boulder engineers, including principal investigator Qin (Christine) Lv of Computer Science, Daven Henze and Michael Hannigan of Mechanical and Environmental Engineering, and Li Shang of Electrical, Computer and Energy Eengineering. They will also be working with a colleague of Lv’s from the University of Michigan.
Lv said the collaboration came about because they had all been working on related projects, just on different scales. The team (with the exception ofHenze) had previously received a large CSR grant for personalized air quality sensing, while Henze had been working on modeling techniques to predict air quality in 20-kilometer grid cells.
“We wanted to start looking at how you can connect those and use data already available online to improve models and better predict ozone levels,” she said. “We’re hoping it will lead to better guidance for making personal decisions on how or when you travel, or a better grounding for making government policy changes.”