Through a series of annual workshops focused on professional development in team-based research, skills in data science, interdisciplinary experience, and grant writing, the CSoI education program has fostered and supported 18 multi-institutional research teams with members representing 24 universities, and 22 distinct departments, while maintaining a 1:1 female to male ratio of participants. To date, these teams have produced 44 conference posters/presentations and 25 journal papers.
This team brings together four computer scientists and electrical engineers to develop a robust method for quantifying the degree of polarization between “camps” on a topic by topic basis via Twitter data. This will allow both researchers and consumers of this media to appropriately describe the current state of discourse on the internet. It can also provide insight into how particular groups of people may be more or less susceptible to polarizing information. And, may shed light on how polarization can spread with particular topics.
This team brings together three PhD students, one from Forestry and Natural Resources, one from Mathematics, and one from Computer Science and Engineering. The team is focused on developing machine learning methods to better decipher community ecology research, and broaden understanding of the interconnectedness of species across the eastern U.S. The outcomes from this research can be applied for improved forest management decision making, as well as understand the on the ground impacts of climate change on the forest ecosystem.
This team brings together three young female scientists (Civil Engineering, Mathematics, and Statistics), with a male PhD candidate in Physics to develop data mining technologies in intelligent building systems and apply them to common cyber-physical infrastructures, which will have a significant influence on system security. The team will develop a model to identify and assess outside “attacks” on the system. Based on this information the team will create an algorithm/system design to apply to smart buildings that will greatly aid in detecting issues and security breaches.
This unique team brings together two PhD candidates in Migration Research Social Science, with an Economics major and a Computer Science PhD student. The team aims to develop a systematic understanding of disaster assistance funds allocations that will provide decision-makers with better data, point out discrepancies in assistance provisions, and minimize the inequalities in emergency assistance provisions. These outcomes can lead to improving service provision policy-making.
This team brings together a postdoctoral scholar in statistics and medicine, with a PhD candidate in math, an undergraduate National Math Alliance fellow, and an undergraduate physics major to attempt to elucidate and predict hospital readmission for diabetes patients.
This team combines three PhD students with expertise in civil engineering combined with information theory, and economics, and an undergraduate Channels scholar to better understand interstate highway crashes using data science techniques that will aid in fulfilling the U.S. Federal Highway Vision Zero initiative.