About Me
I am a PhD student at the Department of Computer Science, Yale University. My advisor is Daniel A. Spielman.
Before attending Yale, I received B.A. in Computer Science from the University of Cambridge in 2011.
My Curriculum Vitae.
Research Interests
My research is focused on fast algorithms and their applications to machine learning.
Papers
- Sampling Random Spanning Trees Faster than Matrix Multiplication
- David Durfee, Rasmus Kyng, John Peebles, Anup B. Rao, Sushant Sachdeva.
- In STOC 2017.
- A Framework for Analyzing Resparsification Algorithms
- Rasmus Kyng, Jakub Pachocki, Richard Peng, and Sushant Sachdeva.
- In SODA 2017.
- Approximate Gaussian Elimination for Laplacians: Fast, Sparse, and Simple
- Rasmus Kyng, Sushant Sachdeva.
- In FOCS 2016.
- Sparsified Cholesky and Multigrid Solvers for Connection Laplacians
- Rasmus Kyng, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Daniel A. Spielman.
- In STOC 2016.
- Fast, Provable Algorithms for Isotonic Regression in all \(\ell_p\)-norms
- Rasmus Kyng, Anup B. Rao, Sushant Sachdeva.
- In NIPS 2015.
- Our implementation of the least-squares Isotonic regression algorithm is available on the Isotonic Github repository.
- Algorithms for Lipschitz Learning on Graphs
- Rasmus Kyng, Anup B. Rao, Daniel A. Spielman, Sushant Sachdeva.
- In COLT 2015.
- Our implementations of the lex-minimization algorithms are available on the YINSlex Github repository.
- Solving SDD Linear Systems in Nearly \( m\log^{1/2}n \) Time
- Michael B. Cohen, Rasmus Kyng, Gary L. Miller, Jakub W. Pachocki, Richard Peng, Anup B. Rao and Shen Chen Xu.
- In STOC 2014.
- Preconditioning in Expectation
- Michael B. Cohen, Rasmus Kyng, Jakub W. Pachocki, Richard Peng, and Anup B. Rao.
- Stretching Stretch
- Michael B. Cohen, Gary L. Miller, Jakub W. Pachocki, Richard Peng, and Shen Chen Xu.
Recent and Upcoming Talks
- IT University of Copenhagen; December 2015. Lipschitz Learning and Isotonic Regression on Graphs
- STOC, Boston; June 2016. Sparsified Cholesky and Multigrid Solvers for Connection Laplacians.
- SIAM Annual Meeting, Boston; July 2016. Approximate Cholesky Factorization
- China Theory Week, Hong Kong; August 2016. Approximate Gaussian Elimination.
- Aarhus University; September 2016. Lipschitz Learning on Graphs.
- MIT A&C Seminar; September 2016. Approximate Gaussian Elimination.
- FOCS, New Brunswick; October 2016. Approximate Gaussian Elimination.
- Google Research NYC; October 2016. Approximate Gaussian Elimination.
- Berkeley; October 2016. Approximate Gaussian Elimination.
- Princeton; October 2016. Approximate Gaussian Elimination.
- Georgia Tech; November 2016. Approximate Gaussian Elimination.
- CMU; December 2016. Approximate Gaussian Elimination.
- University of Copenhagen; January 2017. Approximate Gaussian Elimination.
- MSR Redmond; January 2017. Regression, Elimination, and Sampling on Graphs.
Code
- Laplacians.jl
- Laplacians.jl is a Julia package containing graph algorithms, especially ones related to spectral and algebraic graph theory. It was started by Dan Spielman, and has contributions from Dan, Serban Stan, myself and several others.
- YINSlex Github Repository
- Our implementations of the lex-minimization algorithms from the paper Algorithms for Lipschitz Learning on Graphs . The code was written by Anup Rao, Sushant Sachdeva, Dan Spielman, and myself.
- Isotonic Github Repository
- An implementation of the least-squares Isotonic regression algorithm from the paper Fast, Provable Algorithms for Isotonic Regression in all \(\ell_p\)-norms . The code was written by Anup Rao, Sushant Sachdeva, and myself.
Teaching
- TA for AMTH/CPSC 462/562: Graphs and Networks. Fall 2013.
- TA for CPSC 469/569: Randomized Algorithms. Spring 2013.
- TA for CPSC 201: Introduction to Computer Science. Fall 2012.
Contact
I can be reached by email at
[first name] . [last name] @ yale.edu
[first name] = rasmus
[last name] = kyng