ABSTRACT:

Dynamic linear algebra — algorithmic techniques for matrices that change over time — lies at the core of many applications, from continuous optimization to efficient graph algorithms to machine learning. Yet, until recently, the full power of dynamic linear algebra was not known and exploited in most applications. 

In this talk, I will describe several new advances in using these techniques and outline the limits of what can be done with them. I will overview progress on longstanding problems in dynamic shortest path data structures, regression algorithms, optimal transport, and other problems using dynamic linear algebra.

BIO:

Jan van den Brand obtained his bachelor's and master's degrees in both mathematics and computer science at the Goethe University Frankfurt. Currently, he is a Ph.D. candidate at KTH Royal Institute of Technology, Stockholm, and recipient of the Google Ph.D. Fellowship. His research is on efficient algorithms with focus on optimization and dynamic problems.

WATCH HERE: https://bluejeans.com/558696336