University of Massachusetts Amherst | Computer Science Building, Office 234 (Zoom Link) | cmusco at cs dot umass dot edu

I am an Assistant Professor in UMass Amherst's College of Information and Computer Sciences.

I study algorithms, working at the intersection of theoretical computer science, numerical linear algebra, optimization, and machine learning. I am especially interested in randomized methods 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 Math at Yale and worked as a software developer at Redfin.

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

**COMPSCI 514**: Algorithms for Data Science, Spring 2020. (Past editions: Fall 2019.)

**COMPSCI 891M**: Theory Seminar.

**Spiking Neural Networks Through the Lens of Streaming Algorithms**

Yael Hitron, Cameron Musco, and Merav Parter

International Symposium on Distributed Computing (DISC) 2020.

**Near Optimal Linear Algebra in the Online and Sliding Window Models**

Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, and Samson Zhou

IEEE Symposium on Foundations of Computer Science (FOCS) 2020.

**Fourier Sparse Leverage Scores and Approximate Kernel Learning
**

Tamás Erdélyi, Cameron Musco, and Christopher Musco

In submission.

**Node Embeddings and Exact Low-Rank Representations of Complex Networks**

Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, and Charalampos E. Tsourakakis

In submission.

**Efficient Intervention Design for Causal Discovery with Latents**

Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, and Cameron Musco

International Conference on Machine Learning (ICML) 2020.

**InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity**

Sudhanshu Chanpuriya and Cameron Musco

Knowledge Discovery and Data Mining (KDD) 2020.

**Low-Rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers**

Hannah Lawrence, Jerry Li, Cameron Musco, and Christopher Musco

International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020.

Video of Hannah's (remote) talk at ICASSP.

**Importance Sampling via Local Sensitivity**

Anant Raj, Cameron Musco, and Lester Mackey

International Conference on Artificial Intelligence and Statistics (AISTATS) 2020.

**Random Sketching, Clustering, and Short-Term Memory in Spiking Neural Networks**

Yael Hitron, Nancy Lynch, Cameron Musco, and Merav Parter

Innovations in Theoretical Computer Science (ITCS) 2020.

**Sample Efficient Toeplitz Covariance Estimation**

Yonina Eldar, Jerry Li, Cameron Musco, and Christopher Musco

ACM-SIAM Symposium on Discrete Algorithms (SODA) 2020.

Slides from my talk at Cornell. Video from my talk at DIMACS.

**Fast and Space Efficient Spectral Sparsification in Dynamic Streams**

Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, Aaron Sidford, Jakab Tardos

ACM-SIAM Symposium on Discrete Algorithms (SODA) 2020.

Slides from my talk at Cornell.

**Toward a Characterization of Loss Functions for Distribution Learning**

Nika Haghtalab, Cameron Musco, and Bo Waggoner

Conference on Neural Information Processing Systems (NeurIPS) 2019.

**Low-Rank Approximation from Communication Complexity**

Cameron Musco, Christopher Musco, and David P. Woodruff

In Submission.
Slides from my talks at ITA/UMass.

**Learning to Prune: Speeding up Repeated Computations**

Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, and Ellen Vitercik

Conference on Learning Theory (COLT) 2019.

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

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

ACM Symposium on Theory of Computing (STOC) 2019.

Slides and video from Chris's talk at Simons.

**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 (NeurIPS) 2018.

Code repository.

**Minimizing Polarization and Disagreement in Social Networks**

Cameron Musco, Christopher Musco, and Charalampos E. Tsourakakis

The Web Conference (WWW) 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.

**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 (NeurIPS) 2017.

Code repository. Slides and video from my talk at Simon's discussing this line of work.

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

Cameron Musco and David P. Woodruff

Conference on Neural Information Processing Systems (NeurIPS) 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.

**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.

**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 (NeurIPS) 2015.

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

Slides and video from my talk at NeurIPS. 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. A note containing simplified proofs for common projection-cost-preserving sketches.

**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.

** Distributed House-Hunting in Ant Colonies**

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

ACM Symposium on Principles of Distributed Computing (PODC) 2015.

**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 (SICOMP).**

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

Here are a few writeups, notes, and talks.

Projection-cost-preserving sketch note, containing simplified proofs for common projection-cost-preserving sketching techniques, following the work in our STOC 2015 and SODA 2017 papers.

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.

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.

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.

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 (7 years...) out of date. But I promise I'll get to it.

I had a lot of fun helping build the first version of One Button Wenzel, a site that sold buffalo chicken sandwiches. It evolved into Crunchbutton, then devolved back into One Button Wenzel. Here is a screenshot of the original site in all 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.