Biography
Anna Ma, Ph.D., is Associate Professor in the Department of Mathematics at the University of California, Irvine. Her research focuses on the design and analysis of iterative algorithms for large-scale data, developing tools in numerical linear algebra, signal processing, machine learning, and probability. Ma's work addresses problems in mathematical data science, including tensor methods, randomized algorithms for linear systems, and optimization with missing or incomplete data.
Ma has received several honors for her research contributions, including the ACHA Charles Chui Young Researcher Best Paper Award in 2022 and the Rose Hills Foundation Innovator Grant in 2024. She was selected as a Rising Star in Computational and Data Science in 2019 and received the UC Chancellor's Postdoctoral Fellowship at UC San Diego from 2018 to 2019. Ma has been an invited participant in research programs at the Institute for Advanced Study, the Institute for Pure and Applied Mathematics, and the Mathematical Sciences Research Institute, and has organized minisymposia at conferences including SIAM Mathematics of Data Science and the International Linear Algebra Society Conference.
Ma serves as co-editor of a topical collection on tensor methods in La Matematica and has been a contributing writer for the Girls' Angle Bulletin, a mathematics magazine aimed at fostering interest in mathematics among young women. She received her Ph.D. in Computational Science from Claremont Graduate University in 2018, where her dissertation focused on stochastic iterative algorithms for large-scale data.
Return to topEducation
- Ph.D. in Computational Science, Claremont Graduate University, 2018
- B.S. in Mathematics, University of California, Los Angeles, 2013
Distinctions
- US Junior Oberwolfach Fellow, Mathematisches Forschungsinstitut Oberwolfach, 2018
- ACHA Charles Chui Young Researcher Best Paper Award, 2022
- Rising Stars in Computational and Data Science, University of Texas, Austin, 2019
- Los Alamos National Laboratory Poster Award, Applied Computational Science and Engineering Student Conference (ACSESS), 2017
- Outstanding Poster Presentation, MAA Undergraduate Poster Session at the Joint Mathematics, 2013
- UC Chancellor's Postdoctoral Fellowship, University of California, San Diego, 2018-2019
- Dissertation Fellowship, Claremont Graduate University, 2017-2018
- Intellisis Fellowship, Computational Science Research Center at San Diego State University, 2017-2018
Areas of Expertise
- Mathematical Data Science
- Randomized Kaczmarz Methods
- Stochastic Iterative Algorithms
- Sparse Signal Recovery
- Tensor Linear Systems
- Quantile-Based Randomization
- Bayesian Hyperparameter Selection
Recent Publications
- Battaglia E., Ma A., “Quantile-RK and double quantile-RK error horizon analysis” (opens in new tab), Linear Algebra and Its Applications, vol. 736, pp. 284-308, 2026.
- E. Battaglia, A. Ma, “Reverse Quantile-RK and Its Application to Quantile-RK” (opens in new tab), Numerical Linear Algebra with Applications, vol. 32, no. 3, pp. e70024, 2025.
- H. Luo, A. Ma, “Frontal Slice Approaches for Tensor Linear Systems” (opens in new tab), Numerical Mathematics: Theory, Methods and Applications, vol. 18, no. 2, pp. 353–394, 2025.
- E. H. Bergou, S. Boucherouite, A. Dutta, X. Li, A. Ma, “A Note on the Randomized Kaczmarz Algorithm for Solving Doubly Noisy Linear Systems” (opens in new tab), SIAM Journal on Matrix Analysis and Applications, vol. 45, no. 2, pp. 992–1006, 2024.
- R. Grotheer, S. Li, A. Ma, D. Needell, J. Qin, “Iterative singular thresholding algorithms for tensor recovery” (opens in new tab), Inverse Problems and Imaging, vol. 18, no. 4, pp. 889–907, 2024.
- K. Dover, Z. Cang, A. Ma, Q. Nie, R. Vershynin, “AVIDA: An alternating method for visualizing and integrating data” (opens in new tab), Journal of Computational Science, vol. 68, pp. 101998, 2023.
- R. Grotheer, S. Li, A. Ma, D. Needel, J. Qin, “Stochastic natural thresholding algorithms” (opens in new tab), Conference Record Asilomar Conference on Signals Systems and Computers, IEEE, pp. 832–836, 2023. Presented at 2023 57th Asilomar Conference on Signals, Systems, and Computers.
- J. Haddock, A. Ma, E. Rebrova, “On subsampled quantile randomized Kaczmarz” (opens in new tab), 2023 59th Annual Allerton Conference on Communication Control and Computing Allerton 2023, IEEE, pp. 1–8, 2023. Presented at 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
- C. Huynh, A. Ma, M. Strand, “Block-missing data in linear systems: An unbiased stochastic gradient descent approach” (opens in new tab), 2023 59th Annual Allerton Conference on Communication Control and Computing Allerton 2023, IEEE, pp. 1–7, 2023. Presented at 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
- A. Ma, D. Stöger, Y. Zhu, “Robust recovery of low-rank matrices and low-tubal-rank tensors from noisy sketches” (opens in new tab), SIAM Journal on Matrix Analysis and Applications, vol. 44, no. 4, pp. 1566–1588, 2023.
- A. Ma, “Natural Language Processing, Part 2: Language Models” (opens in new tab), Girls' Angle Bulletin, vol. 16, no. 4, 2023.
- R. Grotheer, S. Li, A. Ma, D. Needell, J. Qin, “Iterative hard thresholding for low cp-rank tensor models” (opens in new tab), Linear and Multilinear Algebra, vol. 70, no. 22, pp. 7452–7468, 2022.
- E. Lybrand, A. Ma, R. Saab, “On the number of faces and radii of cells induced by Gaussian spherical tessellations” (opens in new tab), Applied and Computational Harmonic Analysis, vol. 56, pp. 176–188, 2022.
Most Cited Publications
- A. Ma, D. Needell, A. Ramdas, “Convergence properties of the randomized extended Gauss–Seidel and Kaczmarz methods” (opens in new tab), SIAM Journal on Matrix Analysis and Applications, vol. 36, no. 4, pp. 1590–1604, 2015.
- A. Ma, D. Molitor, “Randomized Kaczmarz for tensor linear systems” (opens in new tab), BIT Numerical Mathematics, vol. 62, pp. 1–24, 2021.
- J. Haddock, A. Ma, “Greed works: An improved analysis of sampling Kaczmarz–Motzkin” (opens in new tab), SIAM Journal on Mathematics of Data Science, vol. 3, no. 1, pp. 342–368, 2021.
- D. Needell, A. Ma, “Stochastic Gradient Descent for Linear Systems with Missing Data” (opens in new tab), Numerical Mathematics: Theory, Methods and Applications, vol. 12, no. 1, pp. 1-20, 2019.
- A. Ma, Y. Zhou, C. Rush, D. Baron, D. Needell, “An approximate message passing framework for side information” (opens in new tab), IEEE Transactions on Signal Processing, vol. 67, no. 7, pp. 1875–1888, 2019.
- A. Ma, D. Needell, A. Ramdas, “Iterative methods for solving factorized linear systems” (opens in new tab), SIAM Journal on Matrix Analysis and Applications, vol. 39, no. 1, pp. 104–122, 2018.
- Y. Van Gennip, B. Hunter, A. Ma, D. Moyer, R. De Vera, A. L. Bertozzi, “Unsupervised record matching with noisy and incomplete data” (opens in new tab), International Journal of Data Science and Analytics, vol. 6, no. 2, pp. 109–129, 2018.
Contact Information
Website: https://anna-math.github.io/
Email: anna.ma@uci.edu
Address: 540F Rowland Hall
Return to topThis profile was created with the help of AI.
Last updated on 10/2/2026.