Christopher Musco

32 Vassar Street, Cambridge, MA • Office G578 • cpmusco [at] • CVGitHubGoogle Scholar

I am a 5th year PhD student in computer science at MIT, graduating in the spring of 2018.

I am fortunate to be advised by Jonathan Kelner. My research is on the algorithmic foundations of data science and machine learning. I study computational methods for processing large graphs and matrices.

I draw on tools from theoretical computer science, numerical linear algebra, and optimization, with a focus on methods that adapt to modern computational environments, including streaming and distributed frameworks.

Before MIT I was an engineer at Redfin and before that I studied Applied Math and Computer Science at Yale. When I am not doing research I'm the delivery driver/accountant/handyman at STILL.


Our paper on Recursive Sampling for the Nyström Method appeared in NIPS 2017. Code for the algorithm is available on GitHub. Also check out our recent SODA 2018 paper on the Lanczos method: it pins down the stability of Lanczos for applying matrix polynomials, proving correct our method for Principal Component Regression that combines Lanczos with any iterative or stochastic system solver.


Minimizing Polarization and Disagreement in Social Networks
Cameron Musco, Christopher Musco, Charalampos Tsourakakis.
The Web Conference (WWW 2018).
MATLAB Code, requires CVX library for convex optimization.

Stability of the Lanczos Method for Matrix Function Approximation
Cameron Musco, Christopher Musco, Aaron Sidford.
ACM-SIAM Symposium on Discrete Algorithms (SODA 2018).
Sample code for the version of Lanczos analyzed: lanczos.m

Recursive Sampling for the Nyström Method
Cameron Musco, Christopher Musco.
Conference on Neural Information Processing Systems (NIPS 2017).
Poster: Poster.
MATLAB Code, including accelerated version of the algorithm.

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh.
International Conference on Machine Learning (ICML 2017).
Slides: 60 Minutes.

Input Sparsity Time Low-Rank Approximation via Ridge Leverage Score Sampling
Michael B. Cohen, Cameron Musco, Christopher Musco.
ACM-SIAM Symposium on Discrete Algorithms (SODA 2017).
Slides: 60 Minutes.

Determining Tournament Payout Structures for Daily Fantasy Sports
Christopher Musco, Maxim Sviridenko, Justin Thaler.
SIAM Algorithm Engineering and Experiments (ALENEX 2017).
Slides: 20 Minutes.

Principal Component Projection Without Principal Component Analysis
Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford.
International Conference on Machine Learning (ICML 2016).
Slides: 15 Minutes.
MATLAB Code for standard algorithm and faster Krylov method analyzed in our recent paper.

Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition
Cameron Musco, Christopher Musco.
Conference on Neural Information Processing Systems (NIPS 2015).
Invited for full oral presentation (1 of 15 out of 403 accepted papers).
Slides: 20 Minutes.

Dimensionality Reduction for k-Means Clustering and Low-Rank Approximation
Michael B. Cohen, Samuel Elder, Cameron Musco, Christopher Musco, Madalina Persu.
ACM Symposium on Theory of Computing (STOC 2015).
Slides: 20 Minutes, 60 Minutes.

Principled Sampling for Anomaly Detection
Brendan Juba, Fan Long, Christopher Musco, Stelios Sidiroglou-Douskos, Martin Rinard.
Network and Distributed System Security Symposium (NDSS 2015).
Slides: 20 Minutes.

Uniform Sampling for Matrix Approximation
Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford.
Innovations in Theoretical Computer Science (ITCS 2015).

Single Pass Spectral Sparsification in Dynamic Streams
Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford.
IEEE Symposium on Foundations of Computer Science (FOCS 2014).
Invited to SIAM Journal on Computing Special Issue for FOCS, appeared 2017.
Slides: 20 Minutes, 60 Minutes.


Prototype code for some of the algorithms I work on is freely available through my GitHub page. I am always happy to answer questions about these implementations or to put you in touch with others who have implemented the methods in different languages and environments.


If you're an MIT affiliate running into algorithmic problems in your research, check out our "Algorithms Office Hours". You can submit a description of your project and we'll set you up to meet with algorithms researchers in CSAIL who may be able to offer some guidance.

I'm an advisor with the EECS Graduate Communication Lab. We offer peer reviewing and coaching for research papers, fellowship and job applications, CVs, talks, and any other writing task or presentation. MIT affiliates can schedule an appointment with me or any other advisor here.