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Thesis Information
Title: Universality of Random Matrices and Random Graphs
Adviser: Van Vu
Institution: Yale University
Graduation Date: May 2017

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Candidate Bio:

I am an NSF postdoctoral fellow at Harvard University with joint appointments in the School of Engineering and Applied Sciences and the Center for Mathematical Sciences and Applications. I graduated from Yale University in 2017 under the supervision of Van Vu. Before that, I received my undergraduate degree from Harvey Mudd College.

Teaching Aims:

I have seven years of teaching experience and have won two competitive teaching awards. I recently designed and taught a graduate course on high-dimensional probability at Harvard. In the classroom, I try to achieve a balance between intuition and rigor. I am also committed to promoting participation in mathematics and computer science from traditionally underrepresented groups.

Paper 1:

Dictionary learning with few samples and matrix concentration, K. Luh and Van Vu, IEEE Transactions on Information Theory, 2016

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Paper 2:

Sparse random matrices have simple spectrum, K. Luh and Van Vu, to appear in Annales de l'Institut Henri Poincare, 2019

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Paper 3:

An improved lower bound for sparse reconstruction from subsampled Hadamard matrices, J. Blasiok, P. Lopatto, K. Luh, J. Marcinek, S. Rao, to appear in FOCS 2019

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Keywords: Random matrices, high-dimensional algorithms, machine learning, compressed sensing

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