Christopher Musco

370 Jay St., Brooklyn, NY • Office 1105 • Zoom Room • cmusco [at] nyu.edu • CVGitHubGoogle Scholar

I am an Assistant Professor of Computer Science and Engineering at New York University's Tandon School of Engineering. My students and I are part of the Algorithms and Foundations Group, as well as the VIDA Research Center. Our research is funded by the Dept. of Energy and the National Science Foundation, including by a 2021 NSF CAREER Award.

My work is on the computational foundations of machine learning and data science. I study efficient methods for understanding large datasets, combining ideas from theoretical computer science with tools from computational and applied mathematics.

I was previously a Research Instructor at Princeton. I completed my PhD in computer science at MIT, where I was fortunate to be advised by Jon Kelner. Before MIT I was an engineer at Redfin and I studied Applied Math and Computer Science at Yale.

News, Events, Travel


We are restarting NYU's in-person CS Theory Seminar at Courant this semester. You can also sign up for the Tandon Algorithms and Foundations email list to receive announcements about upcoming talks.

Teaching


Fall 2022, 2021, 2020, 2019: NYU CS-GY 6763, Algorithmic Machine Learning and Data Science
Spring 2022: NYU CS-GY 6923, Machine Learning
Spring 2020: NYU CS-UY 4563, Introduction to Machine Learning
Fall 2018: Princeton COS 521 , Advanced Algorithm Design

Other: I am a judge and problem writer for the SIAM/MathWorks M3 Challenge, a math modeling competition for high schoolers (which I participated in as a student). I am happy to chat with students, coaches, or others about inspiring the next generation of applied mathematicians.

Students and Postdocs


Tyler Chen, Courant Instructor, incoming.
Majid Daliri, Ph.D., incoming.
Raphael A. Meyer, Ph.D., 2019 - present.
Apoorv Singh, Ph.D., 2020 - present.
R. Teal Witter (with Prof. Lisa Hellerstein), Ph.D., 2020 - present.
Aarshvi Gajjar (with Prof. Chinmay Hegde), Ph.D. 2021 - present.
Atsushi Shimizu, M.S., 2022 - present.
Danrong Li, M.S., 2022 - present.

List of previous students and postdocs

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 2023 (December 2022 application). You should have a strong mathematical background, including in probability and linear algebra.

If you are currently an undergraduate or masters student at NYU, the best way to start a collaboration, or to obtain a TA position for one of my classes, is to first complete my fall graduate class, CS-GY 6763.

Research


Active Learning for Single Neuron Models with Lipschitz Non-Linearities
Aarshvi Gajjar, Chinmay Hegde, Christopher Musco
Preprint.
Preliminary version to appear at NeurIPS 2022 Workshop on The Symbiosis of Deep Learning and Differential Equations II (DLDE 2022).

Near-Linear Sample Complexity for Lp Polynomial Regression
Raphael Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou
ACM-SIAM Symposium on Discrete Algorithms (SODA 2023).

A Tight Analysis of Hutchinson's Diagonal Estimator
Prathamesh Dharangutte, Christopher Musco
SIAM Symposium on Simplicity in Algorithms (SOSA 2023).

Active Linear Regression for lp Norms and Beyond
Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda
IEEE Symposium on Foundations of Computer Science (FOCS 2022).

How to Quantify Polarization in Models of Opinion Dynamics
Christopher Musco, Indu Ramesh, Johan Ugander, R. Teal Witter
International Workshop on Mining and Learning with Graphs at KDD (MLG 2022).

Chemically-informed data-driven optimization (ChIDDO): Leveraging physical models and Bayesian learning to accelerate chemical research
Daniel Frey, Ju Hee Shin, Christopher Musco, Miguel A. Modestino
Reaction Chemistry & Engineering 2022.

Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data
A Yi-Jen Chiang, Christopher Musco, Mengxi Wu
IEEE/Eurographics EuroVis 2022.

Sublinear Time Spectral Density Estimation
Vladimir Braverman, Aditya Krishnan, Christopher Musco
ACM Symposium on Theory of Computing (STOC 2022).
Slides: 50 Minutes.

A Sketch-based Index for Correlated Dataset Search
Aécio Santos, Aline Bessa, Christopher Musco, Juliana Freire
International Conference on Data Engineering (ICDE 2022).

Fast Regression for Structured Inputs
Raphael Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou
The International Conference on Learning Representations (ICLR 2022).

Error bounds for Lanczos-based Matrix Function Approximation
Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco
SIAM Journal on Matrix Analysis and Applications (SIMAX 2022).

Low-memory Krylov subspace methods for optimal rational matrix function approximation
Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco
Preprint.

Fast and Near-Optimal Diagonal Preconditioning
Arun Jambulapati, Jerry Li, Christopher Musco, Aaron Sidford, Kevin Tian
Preprint.

Dynamic Trace Estimation
Prathamesh Dharangutte, Christopher Musco
Advances in Information Processing Systems (NeurIPS 2021).

Finding an Approximate Mode of a Kernel Density Estimate
Jasper C.H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai
European Symposium on Algorithms (ESA 2021).

Correlation Sketches for Approximate Join-Correlation Queries
Aécio Santos, Aline Bessa, Fernando Chirigati, Christopher Musco, Juliana Freire
ACM Special Interest Group on Management of Data Conference. (SIGMOD 2021).

Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand
Sheng Wang, Yuan Sun, Christopher Musco, Zhifeng Bao
ACM Special Interest Group on Management of Data Conference. (SIGMOD 2021).

Graph Learning for Inverse Landscape Genetics
Prathamesh Dharangutte, Christopher Musco
AAAI Conference on Artificial Intelligence (AAAI 2021).
Short version in NeurIPS 2020 AI for Earth Sciences workshop.

Simple Heuristics Yield Provable Algorithms for Masked Low Rank Approximation
Cameron Musco, Christopher Musco, David P. Woodruff
Innovations in Theoretical Computer Science (ITCS 2021).

Hutch++: Optimal Stochastic Trace Estimation
Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff
SIAM Symposium on Simplicity in Algorithms (SOSA 2021).
Slides: 50 Minutes.
MATLAB Code.

The Statistical Cost of Robust Kernel Hyperparameter Turning
Raphael A. Meyer, Christopher Musco
Advances in Information Processing Systems (NeurIPS 2020).

Fourier Sparse Leverage Scores and Approximate Kernel Learning
Tamás Erdélyi, Cameron Musco, Christopher Musco
Advances in Information Processing Systems (NeurIPS 2020).
Invited for spotlight presentation.

Near Optimal Linear Algebra in the Online and Sliding Window Models
Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou
IEEE Symposium on Foundations of Computer Science (FOCS 2020).

Low-Rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers
Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020).

Sample Efficient Toeplitz Covariance Estimation
Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco
ACM-SIAM Symposium on Discrete Algorithms (SODA 2020).
Slides: 50 Minutes.

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).

Analyzing the Impact of Filter Bubbles on Social Network Polarization
Uthsav Chitra, Christopher Musco
ACM International Web Search and Data Mining Conference (WSDM 2020).
Preliminary version in KDD 2019 workshop.

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.

Inferring networks from random walk-based node similarities
Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis
Advances in Information Processing Systems (NeurIPS 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
Advances in Information Processing Systems (NeurIPS 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
Advances in Information Processing Systems (NeurIPS 2015).
Invited for full oral presentation.
Slides: 20 Minutes.
MATLAB Code.

Dimensionality Reduction for k-Means Clustering and Low-Rank Approximation
Michael B. Cohen, Samuel Elder, Cameron Musco, Christopher Musco, Mădălina Persu
ACM Symposium on Theory of Computing (STOC 2015).
Slides: 60 Minutes.

We posted a note titled Projection-Cost-Preserving Sketches: Proof Strategies and Constructions which presents simplified proofs of the results in this paper and our 2017 work.

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: 60 Minutes.

Software


Prototype code for some of the algorithms I work on is available through my GitHub page. Let me know if you have any questions about these implementations.

Past Students and Postdocs


Xinyu Luo, M.S., 2020 - 2022 (next stop: Ph.D. student at Purdue).
Indu Ramesh, M.S., 2020 - 2021 (next stop: Ph.D. student at NYU).
Mengxi Wu, M.S., 2020 - 2021 (next stop: Ph.D. student at USC).
Aline Bessa, Postdoc, 2021 (next stop: KNIME).
Prathamesh Dharangutte, M.S., 2019 - 2021 (next stop: Ph.D. student at Rutgers).