I am a Research Instructor in Princeton's Computer Science Department. 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 machine learning and data science. I study efficient methods for processing and understanding large datasets. My research often combines ideas from theoretical computer science with tools from computational and applied mathematics.
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 Mathematics and Computer Science at Yale.
Fall 2019: NYU CS-GY 9223I, Algorithmic Machine Learning and Data Science
Fall 2018: Princeton COS 521 , Advanced Algorithm Design
Spring 2016: MIT 6.854/18.415, Advanced Algorithms (teaching assistant)
Spring 2016: MIT 6.S977, Technical Communication for Graduate Students (workshop leader)
Other: I am a judge for and supporter of SIAM's M3 Challenge, which is a math modeling competition for high schoolers (which I participated in as a student). I am happy to chat with students, coaches, or other academics looking to get involved and inspire the next generation of applied mathematicians! At MIT I was an advisor with the EECS Communication Lab. The lab offers peer reviewing and coaching on research papers, grant proposals, fellowship applications, CVs, talks, and any other writing task or presentation. MIT affiliates can take advantage of this valuable resource by scheduling an appointment.
Understanding Filter Bubbles and Polarization in Social Networks Uthsav Chitra, Christopher Musco KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM 2019).
Sample Efficient Toeplitz Covariance Estimation Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco Preprint 2019.
Low-Rank Approximation from Communication Complexity Cameron Musco, Christopher Musco, David P. Woodruff Preprint 2019.
Faster Spectral Sparsification in Dynamic Streams Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri Preprint 2019.
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh ACM Symposium on Theory of Computing (STOC 2019). Slides: 60 Minutes. Video: 30 Minutes.
Learning Networks from Random Walk-Based Node Similarities Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis. Conference on Neural Information Processing Systems (NIPS 2018). MATLAB Code.
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).
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.
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 new students in 2020 (December 2019 application). 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.
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.