Microsoft Research New England | 1 Memorial Drive, Cambridge, MA 02142 | cnmusco at mit dot edu

I am a Postdoctoral Researcher at Microsoft Research New England. I will join UMass Amherst's College of Information and Computer Sciences as an Assistant Professor in September 2019.

I study algorithms, focusing on applications in data science and machine learning. I often work on randomized methods and algorithms that adapt to streaming and distributed computation. I am also interested in understanding randomized computation and algorithmic robustness by studying computational processes in biological systems.

I completed my Ph.D. in the Theory of Computation Group at MIT, advised by Nancy Lynch. 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.

**A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms**

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

In submission, 2018.

**Learning Networks from Random Walk-Based Node Similarities**

Jeremy G. Hoskins, Cameron Musco, Christopher Musco, and Charalampos E. Tsourakakis

Conference on Neural Information Processing Systems (NIPS) 2018.

Code repository.

**Eigenvector Computation and Community Detection in Asynchronous Gossip Models**

Frederik Mallmann-Trenn, Cameron Musco, and Christopher Musco.

International Colloquium on Automata, Languages, and Programming (ICALP), 2018.

**Minimizing Polarization and Disagreement in Social Networks**

Cameron Musco, Christopher Musco, and Charalampos E. Tsourakakis

The Web Conference (WWW) 2018.

Code repository.

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

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

Innovations in Theoretical Computer Science (ITCS) 2018.

Slides from my talk at ITCS.

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

Cameron Musco, Christopher Musco, and Aaron Sidford

ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018.

Code repository 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.

Code repository.

**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.

Slides and video from my talk at FOCS. Extended slides slides for hour long talk.

**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 and video from my talk at ICML. Chris's extended slides for an hour long talk.

**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.

Slides from my talk at SODA. Chris's extended slides from his talk at University of Utah.

**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.

Code repository. Chris's slides from his talk at ICML.

**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 and video from my talk at NIPS. Code repository.

** 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.

**Spiking Neural Networks: An Algorithmic Perspective **

Nancy Lynch, Cameron Musco, and Merav Parter

Presentation at Workshop on Biological Distributed Algorithms (BDA) 2017.

Slides from my talk at BDA.

**New Perspectives on Algorithmic Robustness Inspired by Ant Colony House-Hunting**

Tsvetomira Radeva, Cameron Musco, and Nancy Lynch

Presentation at Workshop on Biological Distributed Algorithms (BDA) 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. Corresponding video.

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.