Through a series of annual workshops focused on professional development in team-based research, skills in data science, interdisciplinary experience, and grant writing, the education program has fostered and supported 14 multi-institutional research teams with members from 21 universities.
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.
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.
This team is working to elucidate genetic influences on the pathologies and pathways of substance abuse.
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.
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.
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.
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.
We are currently developing an algorithm that will recognize specific cell division defects out of multiple images without the need of human intervention.
This team is working together to apply information theory techniques to accurately automate information gathering from cell biology imaging.
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.
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.
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.
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.