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For an archive of past events: https://math.illinois.edu/research/seminars-department-calendar

Special Colloquium/Candidate Presentation: Efficient learning algorithms through geometry, and applications in cancer research

Event Type
Seminar/Symposium
Sponsor
n/a
Location
245 Altgeld Hall and Zoom
Date
Jan 24, 2022   3:00 pm  
Speaker
Caroline Moosmueller (UCSD)
Contact
Jared Bronski
E-Mail
bronksi@illinois.edu
Views
154

Zoom link: https://illinois.zoom.us/j/82432437552?pwd=a1VkbjNhZ3d2Y2MxVkg3YnFTRG1qQT09

Abstract:  In this talk, I will discuss how incorporating geometric information into classical learning algorithms can improve their performance. The main focus will be on optimal mass transport (OMT), which has evolved as a major method to analyze distributional data. In particular, I will show how embeddings can be used to build OMT-based classifiers, both in supervised and unsupervised learning settings. The proposed framework significantly reduces the computational effort and the required training data. Using OMT and other geometric data analysis tools, I will demonstrate applications in cancer research, focusing on the analysis of gene expression data and on protein dynamics.

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