Namrata Vaswani is an Indian-American electrical engineer known for her research in compressed sensing, robust principal component analysis, signal processing, statistical learning theory, and computer vision.[1] She is a Joseph and Elizabeth Anderlik Professor in Electrical and Computer Engineering at Iowa State University, and (by courtesy) a professor of mathematics at Iowa State.[2]

Education and career

Namrata Vaswani earned a bachelor's degree in electrical engineering at the Indian Institute of Technology Delhi in 1999. She completed a Ph.D. in electrical and computer engineering in 2004 at the University of Maryland, College Park.[2] Her doctoral advisor was Rama Chellappa,[3] and her dissertation was Change detection in stochastic shape dynamical models with applications in activity modeling and abnormality detection.[4]

After postdoctoral research at the Georgia Institute of Technology, she joined the Iowa State faculty in 2005. She was given her courtesy appointment in mathematics in 2013,[2] and the Anderlik Professorship in 2019.[2][5] She also chairs the Women in Signal Processing Committee of the IEEE Signal Processing Society.[1]

Recognition

In 2018, Namrata Vaswani was named a Fellow of the IEEE "for contributions to dynamic structured high-dimensional data recovery".[6]. In 2019 she was named a distinguished alumni of the University of Maryland Electrical and Computer Engineering Department.[1]

Achievements

First, Namrata Vaswani was the first author who developed a dynamic RPCA method [7] in the L+S decomposition framework in 2010 just after the work of Candès et al. [8] in 2009 on RPCA via decomposition into low-rank and sparse matrices. She immediately understood the interest to develop a provable solution to the dynamic RPCA problem, and provided a usable dynamic RPCA method for real-time computer vision applications. Practically, she was a precursor and a pionner in this kind of dynamic RPCA methods.

Second, Namrata Vaswani progressively improved over the years the original ReProCS by addressing both its performance guarantees and its memory efficiency.[9][10][11] The last version of ReProCS called PracReProCS is the top method on the large-scale dataset CDnet 2014 (which is a reference in the field of change detection) in the category of dynamic RPCA methods provided with performance guarantees.[12] In 2018, Prof. Namrata Vaswani designed MEROP [13] which is a fast and memory-efficient algorithm for RPCA. In addition, the code of PracReProCS and MEROP is publicly available for the scientific community. By this way, she shows her interest for a sharable and reproducible research.

Third, Namrata Vaswani provided the first valuable unified synthesis/review on dynamic RPCA/subspace tracking algorithms in a mature paper,[14] and she is also a GE of two special issues [15] [16] in RPCA/dynamic RPCA via L+S decomposition showing by these activities her international leadership in this field. In addition, she also provided a very valuable invited talk at the workshop RSL-CV 2017 in conjunction with ICCV 2017 as well as many invited talks in seminars and an invited short-course at IIIT-Delhi showing her investment to diffuse as well as possible her research. She received the IEEE Signal Processing Society (SPS) Best Paper Award in 2014 for her paper on dynamic compressive sensing.

References

  1. 1 2 3 Jiang, Marakby, Vaswani, and Wu named 2019 ECE Distinguished Alumni, University of Maryland Institute for Systems Research, June 6, 2019
  2. 1 2 3 4 Curriculum vitae (PDF), retrieved 2019-09-03
  3. ↑ Namrata Vaswani at the Mathematics Genealogy Project
  4. ↑ WorldCat catalog entry for Change detection in stochastic shape dynamical models with applications in activity modeling and abnormality detection, retrieved 2019-09-03
  5. ↑ "Three Cyclone Engineers named Anderlik Professors", College of Engineering News, Iowa State University, 5 August 2019, retrieved 2019-09-03
  6. ↑ Albanese, Giovanni Jr. (December 17, 2018), "Outstanding Indian American Engineers Named IEEE Fellows for 2019", India West
  7. ↑ C. Qiu; N. Vaswani (2010). "Real-time Robust Principal Components Pursuit". International Conference on Communication Control and Computing. arXiv:1010.0608.
  8. ↑ Emmanuel J. Candes; Xiaodong Li; Yi Ma; John Wright (2009). "Robust Principal Component Analysis?". Journal of the ACM. 58 (3): 1–37. doi:10.1145/1970392.1970395. S2CID 7128002.
  9. ↑ H. Guo; C. Qiu; N. Vaswani (2014). "Practical ReProCS for separating sparse and low-dimensional signal sequences from their sum — Part 1". 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 4161–4165. doi:10.1109/ICASSP.2014.6854385. ISBN 978-1-4799-2893-4. S2CID 29223.
  10. ↑ H. Guo; C. Qiu; N. Vaswani (2014). "Practical ReProCS for Separating Sparse and Low-dimensional Signal Sequences from their Sum - Part 2". GlobalSIP 2014.
  11. ↑ H. Guo; C. Qiu; N. Vaswani (2014). "An Online Algorithm for Separating Sparse and Low-dimensional Signal Sequences from their Sum". IEEE Transactions on Signal Processing. 62 (16): 4284–4297. arXiv:1310.4261. Bibcode:2014ITSP...62.4284G. doi:10.1109/TSP.2014.2331612. S2CID 6704261.
  12. ↑ N. Vaswani; T. Bouwmans; S. Javed; P. Narayanamurthy (June 2018). "Robust PCA and Robust Subspace Tracking: A Comparative Evaluation". IEEE Statistical Signal Processing Workshop, SSP 2018.
  13. ↑ P. Narayanamurthy; N. Vaswani (April 2018). "A Fast and Memory-efficient Algorithm for Robust PCA (MEROP)". IEEE International Conference on Acoustics, Speech, and Signal, ICASSP 2018.
  14. ↑ N. Vaswani; T. Bouwmans; S. Javed; P. Narayanamurthy (July 2018). "Robust Subspace Learning: Robust PCA, Robust Subspace Tracking and Robust Subspace Recovery". IEEE Signal Processing Magazine. 35 (4): 32–55. arXiv:1711.09492. Bibcode:2018ISPM...35d..32V. doi:10.1109/MSP.2018.2826566. S2CID 3691367.
  15. ↑ T. Bouwmans; N. Vaswani; P. Rodriguez; R. Vidal; Z. Lin (December 2018). "Introduction to the Special Issue on Robust Subspace Learning and Tracking: Theory, Algorithms, and Applications". IEEE Journal of Selected Topics in Signal Processing.
  16. ↑ N. Vaswani; Y. Chi; T. Bouwmans (July 2018). "Special Issue on "Rethinking PCA for Modern Datasets: Theory, Algorithms, and Applications"". Proceedings of the IEEE. doi:10.1109/JPROC.2018.2853498. S2CID 51935138.
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