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Christopher Musco is an Associate Professor in the Department of Computer Science at New York University. His research spans efficient algorithms in computational mathematics and theoretical computer science, with a focus on algorithmic aspects of modern machine learning, including fast inference and retrieval. He is involved with NYU's Theoretical Computer Science Group and has previously served as a Research Instructor at Princeton University. Musco completed his PhD at the Massachusetts Institute of Technology under the guidance of Jon Kelner. His educational background includes studies in Applied Mathematics and Computer Science at Yale University. He actively organizes events such as the MathWorks Math Modeling Challenge for high school students and is a visiting researcher at various institutions, including the Simons Institute for the Theory of Computing at UC Berkeley. Musco's teaching focuses on Algorithm Design, Machine Learning, and Data Science, with courses taught at both NYU and Princeton.
New York University • Brooklyn, NY
Teaching courses on algorithms and machine learning, while conducting research in theoretical computer science.
Princeton University • Princeton, NJ
Conducted research and taught various undergraduate courses.
Open Program in Biomedical Sciences (Vilcek Institute) covers departments like Biochemistry, Pathology, Neuroscience, Microbiology, etc.