Stata Center | 32 Vassar Street, Cambridge, MA 02139 | Office 32-G670 | cnmusco at mit dot edu

I am a fifth year Ph.D. student (graduating spring 2018) in the Theory of Computation Group at MIT.

I am lucky to be advised by Nancy Lynch and supported by an NSF Graduate Fellowship. I study algorithms, focusing on randomized methods for linear algebra, with applications in machine learning and data analysis. I am also interested in understanding randomized computation and algorithmic robustness by studying computational processes in biological systems.

Before MIT, I studied Computer Science and Applied Mathematics at Yale University and worked as a software developer on the Data Team at Redfin.

Here are my Google Scholar profile, CV, and GitHub.

The website for our reading group on distributed computation in biological systems can be found here.

**Stability of the Lanczos Method for Matrix Function Approximation**

Cameron Musco, Christopher Musco, and Aaron Sidford

Preprint, 2017.

Matlab code for matrix function approximation (see `lanczos.m`

).

**Recursive Sampling for the Nyström Method**

Cameron Musco and Christopher Musco

Conference on Neural Information Processing Systems (NIPS 2017)

Matlab code.

**Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?**

Cameron Musco and David P. Woodruff

Conference on Neural Information Processing Systems (NIPS 2017)

**Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices**

Cameron Musco and David P. Woodruff

IEEE Symposium on Foundations of Computer Science (FOCS 2017)

**Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees**

Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker and Amir Zandieh

International Conference on Machine Learning (ICML 2017)

Slides from my talk at ICML.

**Input Sparsity Time Low-Rank Approximation via Ridge Leverage Score Sampling**

Michael B. Cohen, Cameron Musco, and Christopher Musco

ACM-SIAM Symposium on Discrete Algorithms (SODA 2017)

Chris's slides from his talk at University of Utah.

**Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness**

Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff

Preprint, 2016.

**Online Row Sampling**

Michael B. Cohen, Cameron Musco, and Jakub Pachocki

International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2016)

**Invited to special issue of Theory of Computing.**

**Principal Component Projection Without Principal Component Analysis**

Roy Frostig, Cameron Musco, Christopher Musco, and Aaron Sidford

International Conference on Machine Learning (ICML 2016)

Matlab code.

**Faster Eigenvector Computation via Shift-and-Invert Preconditioning**

Daniel Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, and Aaron Sidford

International Conference on Machine Learning (ICML 2016)

**Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition**

Cameron Musco and Christopher Musco

Conference on Neural Information Processing Systems (NIPS 2015)

**Selected for Oral Presentation (1 of 15 out of 403 papers).**

Slides from my talk at NIPS. Matlab code.

** Dimensionality Reduction for k-Means Clustering and Low Rank Approximation**

Michael B. Cohen, Sam Elder, Cameron Musco, Christopher Musco, and Madalina Persu

ACM Symposium on Theory of Computing (STOC 2015)

Slides from my talk at MIT's Algorithms and Complexity Seminar.

My Master's Thesis containing empirical evaluation along with a guide to implementation.

**Uniform Sampling for Matrix Approximation**

Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, and Aaron Sidford

Innovations in Theoretical Computer Science (ITCS 2015)

Slides from my talk at MIT's Algorithms and Complexity Seminar.

**Single Pass Spectral Sparsification in Dynamic Streams**

Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco and Aaron Sidford

IEEE Symposium on Foundations of Computer Science (FOCS 2014)

**Appeared in special issue of SIAM Journal on Computing.**

Chris's Slides from his talks at FOCS and the Harvard TOC Seminar.

**Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks**

Nancy Lynch, Cameron Musco, and Merav Parter

International Symposium on Distributed Computing (DISC 2017)

**Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks**

Nancy Lynch, Cameron Musco, and Merav Parter

Innovations in Theoretical Computer Science (ITCS 2017)

** Ant-Inspired Density Estimation via Random Walks**

Cameron Musco, Hsin-Hao Su, and Nancy Lynch

Proceedings of the National Academy of Sciences (PNAS), 2017.

Full paper also available on arXiv. An extended abstract initially appeared in PODC, 2016.

** Distributed House-Hunting in Ant Colonies**

Mohsen Ghaffari, Cameron Musco, Tsvetomira Radeva, and Nancy Lynch

ACM Symposium on Principles of Distributed Computing (PODC 2015)

Here are a few writeups, notes, and talks.

VC dimension in (neural) circuit complexity, outline of a talk on the basics of VC dimension and how it can be used to give circuit size lower bounds for certain functions.

Fast Low-Rank Approximation and PCA Beyond Sketching, slides for a talk I gave at Mining Massive Datasets (MMDS 2016) on new techniques for large scale low-rank approximation.

Chebyshev Polynomials in TCS and Algorithm Design, outline of a talk I gave at the MIT Theory student retreat on the many applications of Chebyshev polynomials to upper and lower bounds in Theoretical Computer Science.

Subspace Scores for Feature Selection in Computer Vision, final project report where we test leverage score based sampling algorithms for k-means/PCA dimension reduction and general feature selection.

Applications of Linear Sketching to Distributed Computing, slides for a talk I gave at our Theory of Distributed Systems seminar. High level overview of linear sketching, my work on k-means clustering and spectral sparsification, and applications to distributed data analysis.

Graph Sparsification and Dimensionality Reduction, final project for Jelani Nelson's Algorithms for Big Data class.

Linear Regression and Pseudoinverse Cheatsheet, since there are a lot of ways to explain the pseudoinverse.

Big-O and Asymptotic Notation Cheatsheet, just in case.

Fast Approximation of Maximum Flow using Electrical Flows, undergraduate Applied Mathematics senior project report. I was fortunate to be advised by Dan Spielman for both my senior projects. They were my first introduction to research in computer science and the reason I decided to go to graduate school.

Graph Construction Through Laplacian Function Optimization, Computer Science senior project report.

I've worked on a lot of projects, some more serious than others.

I built this Rap Collaboration Graph, which gives a visualization of musical collaborations in hip hop. Unfortunately, colors are only rendering in Safari right now, it looks fuzzy on retina displays, and the data is about a year out of date. But I promise I'll get to it.

I had a lot of fun helping build the first version of a site that sold buffalo chicken sandwiches. It's gotten a major facelift and evolved into Crunchbutton, but here is a screenshot of the original One Button Wenzel in its glory

My friend Charlie and I once built an AI to play Transport Tycoon Deluxe. Here is a poster describing the project.

In college I also had a lot of fun working on Yale's Formula Hydrid Racecar Team.

I love to ski, and a long time ago, my brother Chris and I used to build custom skis. Our first pair had maple/poplar cores, varnished wood sidewalls, and flex comparable to a pair of 2x4s. Our second pair has more precisely shaped pine cores, an improved top sheet, and a much smoother flex. The tips delaminated once, but we riveted them together and still ski on them today! Here's an old forum post on SkiBuilders.com with more pictures of our setup.