Metcalf - MCS - Raval, Kiran - 2.10.26.

This project will explore different ways to validate Nonnegative Matrix Factorization (NMF) results. NMF is a linear dimensionality reduction tool which is useful for approximating nonnegative data into semantically distinct clusters. Understanding the conditions under which such approximations are meaningful is crucial to incorporate such tools in further downstream tasks. The topic has multiple interesting areas for explorations which can be tailored accordingly:
Theoretical: Characterising the regularity conditions for unique NMF results.
High-Performance computing (HPC): Current methods for model validation are all NP-hard to verify. Designing parallel/randomised algorithms to accelerate these techniques is required.
Data Science: Validating these linear models against other nonlinear embedding techniques.

Education and Experience Requirements

•   The entirety of the appointment must be conducted within the United States. 

•   Must be 18 years or older at the time the appointment begins.

•   Applicants must be:

• Currently enrolled in undergraduate or graduate studies at an accredited institution. 
• Graduated from an accredited institution within the past 3 months; or
• Actively enrolled in a graduate program at an accredited institution. 

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