Apoorv Vikram Singh
Ph.D.
Computer Science

I graduated from NYU in August 2026 with my PhD in computer science under the supervision of Christopher Musco. At NYU, I was a part of the Algorithms and Foundations Group.

Before joining NYU, I was a visiting researcher at the MODAL team in INRIA Lille where I worked with Dr Hemant Tyagi and Dr Mihai Cucuringu (Univ of Oxford). Before that I was a Project Associate in the Dept. of Computer Science and Automation, Indian Institute of Science, where I worked with Dr Anand Louis and Dr Amit Deshpande (Microsoft Research, India). I obtained my undergraduate degree from IIIT Bangalore.

Research Interests: My research lies at the intersection of numerical linear algebra, statistics, theoretical computer science, and machine learning. I develop fast, scalable algorithms with rigorous theoretical guarantees, with an emphasis on methods that remain effective on large, real-world datasets. My interests include randomized and sublinear algorithms, spectral and eigenvalue problems, moment matching, differential privacy, spectral graph theory, and information retrieval.

I am currently on the job market and am interested in both academic and industry opportunities. On the academic side, I am seeking postdoctoral positions in numerical linear algebra, spectral graph theory, differential privacy, and related areas. In industry, I am interested in applied research and research engineering roles focused on large-scale information retrieval, machine learning and data science, numerical algorithms, and optimization.

Publications

• Fast Schatten $p$-Norms From Rational Approximations
  Izzy Detherage, Apoorv Vikram Singh, Nicholas West
  Preprint 2026
• ACME: Approximate Chebyshev Moment Estimation for Differentially Private Synthetic Data
  Lucas Rosenblatt, Apoorv Vikram Singh, Christopher Musco
  TPDP 2026
• Sharper Bounds for Chebyshev Moment Matching, with Applications
  Cameron Musco, Christopher Musco, Lucas Rosenblatt, Apoorv Vikram Singh
  COLT 2025 [ArXiv] [Slides]
  Preliminary version abstract accepted at TPDP 2024
• Faster Spectral Density Estimation and Sparsification in the Nuclear Norm
  Yujia Jin, Ishani Karmarkar, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh
  COLT 2024 [ArXiv] [Slides]
• Moments, Random Walks, and Limits for Spectrum Approximation
  Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh
  COLT 2023 [ArXiv] [Slides]
• Regularized Spectral Methods for Clustering Signed Networks
  Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem, Hemant Tyagi
  JMLR 2021 [ArXiv]
• Approximation Algorithms for Cost-Balanced Clustering
  Amit Deshpande, Anand Louis, Deval Patel, Apoorv Vikram Singh
  Preprint 2019 [Link]
• On Euclidean $k$-Means Clustering with $\alpha$-Center Proximity
  Amit Deshpande, Anand Louis, Apoorv Vikram Singh
  AISTATS 2019 [ArXiv] [Slides]

News

• Defended my Ph.D. thesis titled Robust Moment Matching: Theory, Algorithms and Applications (July 2026).
• Gave a talk on Sharper Bounds for Chebyshev Moment Matching, with Applications at COLT 2025 in Lyon, and at Yale Student Theory Day.
• Gave a talk on moments, random walks, and limits for spectrum approximation at the Stanford CS Theory Lunch Seminar, and at Brown CS Theory Seminar (video).
• Gave a talk on our work on Chebyshev moment matching at the NYC Graduate Student TCS Day.
• Attended the Sublinear Graph Simplification Workshop at the Simons Institute.
• Attended STOC 2024 (Vancouver, Canada) and COLT 2024 (Edmonton, Canada).
• Attended the Sketching and Algorithm Design at the Simons Institute.
• Attended STOC 2023 (Orlando, Florida, USA) and COLT 2023 (Bangalore, Karnataka, India).
• June 11, 2023: Attended the Workshop on Modern Techniques in Graph Algorithms organized by DIMACS, Rutgers.
• June 26, 2022: Attended the Swedish Summer School on Theoretical Computer Science organized by KTH.
• Attended the summer school on New tools for optimal mixing of Markov chains: Spectral independence and entropy decay at UCSB.