Cameron Musco

Cameron Musco is an Assistant Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. He is affiliated with the Theory Group and conducts research at the intersection of theoretical computer science, numerical linear algebra, and machine learning. His work focuses on randomized algorithms, streaming, and distributed computation, with applications in data science.

Education:

• PhD in Computer Science, MIT (2019)
• BS in Computer Science and Applied Mathematics, Yale University

Research Interests: Musco's research emphasizes algorithm design for large-scale data analysis, including fast randomized methods for linear algebraic problems. He explores topics such as low-memory computation, matrix approximations, and graph algorithms, driven by applications in machine learning and distributed systems.

Publications: His recent work spans advancements in hierarchical matrix approximation, graph-based nearest neighbor search, and fair resource allocation, reflecting expertise in both theoretical foundations and practical algorithmic innovation.

Awards: He has received an NSF Career Award, Google Research Scholar Award, and recognition for anti-racism leadership. He actively reviews for top conferences in theoretical computer science and machine learning.

Advising & Grants: Musco advises multiple PhD students and has grants from NSF and Google. His lab collaborates on projects like low-rank matrix approximation and causal discovery.

Labs/Teams: He is part of the Theoretical Computer Science Group and the Center for Data Science at UMass.