Random Matrix Theory is an area of mathematics that deals with matrix-valued random variables. There are many interesting questions that can asked for these mathematical objects, and this theory has found numerous applications in science over the years, such as biology, quantum physics, computer science... The study of eigenvalues of random matrices has many roots, including early work of Wishart in statistics, Wigner in nuclear physics, as well as Goldstein and von Neumann in numerical analysis.
This 1-day Symposium on Random Matrix Theory and its Applications, organized by Andrew Blumberg, Mathieu Carrière, Ivan Corwin and Raul Rabadan, is part of the events supported by the Columbia University Center for Topology of Cancer Evolution and Heterogeneity (Director Dr Rabadan), which is part of the National Cancer Institute's Physical Sciences in Oncology Network, and the "Probability and Society" initiative for Columbia. The purpose of the symposium is to present foundations and applications of Random Matrix Theory in biology and computer science.
For any questions or concerns please contact Mathieu Carrière at firstname.lastname@example.org or (917)-941-5182
Andrew Blumberg, PhD (University of Texas at Austin)
Luis Aparicio, PhD (Columbia University)
Ivan Corwin, PhD (Columbia University)
Ben Landon, PhD (MIT)
Alex Bloemendal, PhD (Broad Institute)
Jeff Pennington, PhD (Google Brain)
Jonathan Bloom, PhD (Broad Institute)
Friday November 1st:
9.30am - 10.00am --- Coffee/Tea & Registration
10.00am - 10.30am: Andrew Blumberg, PhD, University of Texas at Austin.
Homology of random point clouds
10.30am - 11.00am: Luis Aparicio, PhD, Columbia University.
Applications of random matrix theory to single-cell biology
11.00am - 11.30am --- Coffee break
11.30am - 12.00pm: Ivan Corwin, PhD, Columbia University.
Products of thin random matrices and random walks in random media
12.00pm - 12.30pm: Alex Bloemendal, PhD, Broad Institute.
GWAS and BBP: Uncorrected confounding in genetic association studies
12.30pm - 2.00pm --- Lunch break
2.00pm - 2.30pm: Ben Landon, PhD, MIT.
Extremal eigenvalues of sparse random matrices
2.30pm - 3.00pm: Jeffrey Pennington, PhD, Google Brain.
Operator-valued free probability meets deep learning: training and generalization dynamics in high dimensions
3.00pm - 3.30pm --- Coffee break
3.30pm - 4.00pm: Jonathan Bloom, PhD, Broad Institute.
Loss landscapes, Morse theory, and linear autoencoders
Lerner Hall 477
New York, NY 10027