Christopher Musco

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

I will be joining Princeton's Department of Computer Science as a Research Instructor from Fall 2018 through Summer 2019. In Fall 2019, I will join NYU as an Assistant Professor of Computer Science and Engineering in the Tandon School of Engineering.

My research is on the algorithmic foundations of data science and machine learning. I study efficient methods for processing and understanding large datasets, often working at the intersection of theoretical computer science, numerical linear algebra, and optimization.

I completed my PhD in computer science at MIT, where I was fortunate to be advised by Jonathan Kelner. 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.

News and Travel


I will be in Berkeley to attend the Simons Institute Workshop on Randomized Numerical Linear Algebra from September 24-28th as part of the semester program on Foundations of Data Science.

Teaching


Fall 2018: Princeton COS 521, Advanced Algorithm Design (upcoming)

Spring 2016: MIT 6.854/18.415, Advanced Algorithms (teaching assistant)

Spring 2016: MIT 6.S977, Technical Communication for Graduate Students (workshop leader)

Other: At MIT I was an advisor with the EECS Communication Lab. The lab offers peer reviewing and coaching for research papers, grant proposals, fellowship and job applications, CVs, talks, and any other writing task or presentation. I would encourage MIT affiliates to take advantage of this valuable resource by scheduling an appointment here.

Research


Eigenvector Computation and Community Detection in Asynchronous Gossip Models
Frederik Mallmann-Trenn, Cameron Musco, Christopher Musco.
International Colloquium on Automata, Languages, and Programming (ICALP 2018).

Learning Networks from Random Walk-Based Node Similarities
Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis.
Preprint 2018.
MATLAB Code.

Minimizing Polarization and Disagreement in Social Networks
Cameron Musco, Christopher Musco, Charalampos E. 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).
Slides: 60 Minutes.
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.
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. Video: 80 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).
Invited to special issue of ACM Journal of Experimental Algorithmics for ALENEX.
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.
MATLAB Code.

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 special issue of SIAM Journal on Computing for FOCS, appeared 2017.
Slides: 20 Minutes, 60 Minutes.

Working with Me


If you are interested in working with me on research, I would encourage you to apply to NYU Tandon's PhD program in Computer Science. I will be taking students beginning in 2019 (December 2018 application deadline). You should have a strong mathematical background, including in probability theory and linear algebra.

If you are interested in algorithms, machine learning, or data science in general, New York City is a unique environment for pursuing a PhD. Beyond NYU's broad investment in data science, the city offers unmatched access to top academic institutions,industry research labs, and startups.

I am also happy to advise masters or undergraduate research at Princeton or NYU. Reach out to learn about existing projects or let me know what topics you are interested in exploring.

Software


Prototype code for some of the algorithms I work on is 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.