Namrata Vaswani

Title(s):

Endowed Anderlik Professor

Office

3121 Coover
2520 Osborn Dr.
Ames, IA 500111046

Information

Education

Ph.D., Electrical and Computer Engineering, University of Maryland (2004)

B.Tech., Electrical Engineering, Indian Institute of Technology (1999)

Research Areas

Core Area(s): Communications and signal processing, statistical and sequential signal processing, recursive sparse reconstruction and compressive sensing, medical imaging

Department’s Strategic Area(s): Data, decisions, networks & autonomy; bioengineering

Additional Information

Publications

Google Scholar Profile: https://scholar.google.com/citations?user=s-dQPO8AAAAJ&hl=en

  • Nayer, N. Vaswani, “Fast and sample-efficient federated low rank matrix recovery from column-wise linear and quadratic projections”, IEEE Trans. Information Theory, 2023
  • Babu, S. Lingala, N. Vaswani, “Fast Low Rank Column-wise Compressive Sensing for Accelerated Dynamic MRI”,IEEE Trans. Computational Imaging, 2023
  • Nayer*, N. Vaswani, “Sample-Efficient Low Rank Phase Retrieval”, IEEE Trans. Information Theory, Dec., 2021.
  • Das, A. Ramamoorthy, N. Vaswani, “Efficient and Robust Distributed Matrix Computations via Convolutional Coding”, IEEE Trans. Information Theory, Sept., 2021.
  • Narayanamurthy*, N. Vaswani, “Fast Robust Subspace Tracking via PCA in Sparse Data-Dependent Noise”, IEEE Journal on Selected Topics in Information Theory (JSAIT), special issue on Estimation and Inference, Nov. 2020
  • Narayanamurthy*, N. Vaswani, “Provable Dynamic Robust PCA or Robust Subspace Tracking”, IEEE Trans. Information Theory, to appear, 2018.
  • Narayanamurthy*, N. Vaswani, “Nearly Optimal Robust Subspace Tracking and Dynamic Robust PCA”, Proc. Intl. Conf. Machine Learning (ICML), 2018.
  • Vaswani and P. Narayanamurthy*, “Static and Dynamic Robust Principal Component Analysis (PCA) and Matrix Completion: A Review”, Proceedings of the IEEE, August 2018 (special issue on Rethinking PCA for Modern Datasets).
  • Vaswani and H. Guo*, “Correlated-PCA: Principal Components’ Analysis when Data and Noise are Correlated”, Proc. Neural Info. Proc. Systems (NIPS), 2016.
  • Qiu*, N. Vaswani, B. Lois* and L. Hogben, “Recursive Robust PCA or Recursive Sparse Recovery in Large but Structured Noise”, IEEE Trans. Information Theory, August, 2014.

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