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GAUSSI:
Generating, Analyzing, and Understanding Sensory and Sequencing Information

A Transdisciplinary Graduate Training Program in Biosensing and Computational Biology

Biosensing using advanced semiconductor technologies and methods based on next generation DNA sequencing generate information about biological systems at an unprecedented scale. This National Science Foundation Research Traineeship (NRT) award prepares master's and doctoral students at Colorado State University with the tools to extract meaning from huge amounts of sensor and genomics data. Through innovative curricula and internships, trainees will learn to handle the new computational, statistical, mathematical, and engineering challenges that biologists, computer scientists, and engineers are unable to overcome alone. This traineeship program contributes to a new generation of scientists and engineers who are able to tackle complex problems related to large datasets in a variety of disciplines. Moreover, this program builds a community of university, K-12 schools, community colleges, and industry for a wider participation of effective scientific discovery, teaching, and learning.

Research activities integrated with the training program will address themes critical to advance understanding of some fundamental data-related questions facing biological sciences. Interdisciplinary teams will tackle research involving biological sensors, detection of microbes, regulation of gene expression, and evolutionary genomics and genome assembly. Faculty across engineering, life sciences, computer science, mathematics, and statistics will develop a flexible, customizable collection of training modules, to create a training experience personalized for each student regardless of their background. Trainees will develop the skills and tools to process, analyze, visualize, and understand large datasets from biosensing and next generation DNA sequencing. Additionally, the program incorporates a wide range of transferrable skills training so that trainees will be well equipped to engage and lead data-centric research within or outside academia.