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Research Teams

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 Information Frontiers Learning Program has facilitated and supported 20 multi-institutional research teams with members representing 29 universities, and 26 distinct departments, with a goal of maintaining an overall 1:1 female to male ratio of participants along with broader participation. To date, these teams have produced 50+ conference posters/presentations and 25+ journal papers. Contact Director of Education, Brent Ladd, for more information.

D2IE Team - "Addressing Digital Divide and Inclusion in Education".

This unique XPrize Team organizes an educational research and policy student team undergraduate, graduate, staff, and faculty across four universities and a local government office for the Open Data Insitute's (ODI) education challenge sponsored by Microsoft as a global XPrize Challenge. The primary purpose is to address the lack of access to reliable and fast internet connections for learners in the U.S. (and globally), especially in the light of COVID-19 pandemic pressures. 


Math-Prep Team - "Implications of Teacher Knowledge and Attitudes: A Cross-National Exploration of Secondary Math Teacher Preparation".

This team brings together two educational psychologists with a physicist/data scientist, a computer scientist, and an environmental scientist to address a systemic problem in U.S. education: Secondary education teacher preparation in math. The U.S. is 39th place world-wide in math achievement. Such outcomes negatively impact the number of U.S. citizens who are well prepared to study and research in the science of information fields and STEM fields in general. Recent studies are showing that there are significant positive relationships between teachers' attitudes toward math and math ability with the resulting math performance of their students. This team's primary goal is to use data-driven approaches to explore STEM teacher preparation and attitudes about math in and across six countries (South Korea, United States, Taiwan, Mexico, Bulgaria, and Germany) and then making this data analysis accessible and digestible for policymakers to improve the fact-based conversation around how best to prepare teachers in STEM fields in the U.S.


Team Information Polarization – “The Polarization of Information on the Web”


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.

Team Forest Ecology – “Identifying Shifts in Forest Communities Using Machine Learning Techniques”


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.

Codeswitching Triggers Team – “Identification and Analysis of Conversational Codeswitching Triggers”

This team brings together a Center postdoctoral scholar in information theory with two PhD students and two undergraduates, a Channels scholar and National Math Alliance fellow working on new theoretical models and data analysis to better understand the mechanisms of codeswitching between two languages. Little research has been done to date on this subject, and this team's unique interdisciplinary composition and expertise aims to tackle this problem.

Team Cyber-Physical Security – “Vetting the Energy and Security of Smart Buildings with Data Science”


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.

Team Disaster Emergency Data – “Improving the Distribution of Disaster Emergency Assistance Programs in the USA Based on Major Disasters Data Mapping 2008-2017”


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.

Diabetes Probability Team


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.

Crash Reduction Team

Crash Reduction Team

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.

Team Genetics Analysis of Substance Abuse

This team is working to elucidate genetic influences on the pathologies and pathways of substance abuse.

Team Machine Learning Security

The team goal is to design algorithms as well as systems that will defend the large scale distributed machine learning against the known range of adversarial attacks.

Team Cancer Data

This team will develop effective tools to extract clinical value from existing databases by correlating phenotypic, and where possibly genotypic, traits with the administered treatment drugs and their eventual treatment outcomes.

Team Lowe Syndrome

The objective of this student-initiated team is to develop tools to extract and analyze quantitative biological information related to organelle content and localization of target proteins.

Yeast Algorithms Team

The objective of this student-initiated proposal is to develop an automatic quantification algorithm to extract and analyze quantitative biological information related to endocytosis of membrane proteins to facilitate data analysis and reduce user-introduced errors.

Cell Morphology Analysis Team

We are currently developing an algorithm that will recognize specific cell division defects out of multiple images without the need of human intervention.

Biological Imaging Team

This team is working together to apply information theory techniques to accurately automate information gathering from cell biology imaging.

Info-Energy Team

Problems we are looking at include energy in communications and computation with error-correcting codes that attain minimum total communication power, and minimizing energy in computing the Fourier transform.

Metabolic Information Team

We will attempt to look at dynamic control goals that guide metabolic function as represented in complex sets of bioinformatics data. Our team will first address how to best analyze gene expression data as a big data problem.

Random Walks Team

This project was completed in December of 2013. The team brought together machine learning and information theory researchers to estimate models of graph data from network samples.

Fresh Boolean Team

This project was completed in December of 2013. This team sought to address the problem of improving information theoretic techniques, because almost all current methods rely on measuring phenomena and non-asynmptotic problem formulations, which are poor methods. These student members are now alumni of the Center.


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