Professor of ECE and courtesy Professor of Mathematics
Iowa State University
3121 Coover Hall, Ames IA 50011, Email: namrata AT iastate DOT edu Phone: 515-294-4012
News (Nov 2018): Elected to Fellow of the IEEE for contributions to dynamic high-dimensional structured data recovery!
Biography: Namrata Vaswani is a Professor of Electrical and Computer Engineering, and (by courtesy) of Mathematics, at Iowa State University. She received a Ph.D. in 2004 from the University of Maryland, College Park and a B.Tech. from Indian Institute of Technology (IIT-Delhi) in India in 1999. Her research interests lie at the intersection of statistical machine learning / data science, computer vision, and signal processing. She is a recipient of the Harpole-Pentair Assistant Professorship (2008-09); the Iowa State Early Career Engineering Faculty Research Award at Iowa State (2014); the University of Maryland ECE Distinguished Alumni Award (2019) and the IEEE Signal Processing Society (SPS) Best Paper Award (2014) for her T-SP paper on Modified-CS co-authored with her graduate student Lu. Vaswani is an IEEE Fellow as of January 1 2019.
Professional Service and Talks/Tutorials: Prof. Vaswani taught an invited short-course on PCA and Robust PCA for Modern Datasets at IIIT-Delhi under the Global Initiative of Academic Networks (GIAN) program of Government of India in December 2017. She recently also gave an invited talk at the International Conference on Computer Vision (ICCV) workshop on Robust Subspace Learning. She has given invited seminars at universities around the world including a department colloquium at UIUC and a department seminar at CMU. Vaswani has served the SPS and IEEE in various capacities. She is an Area Editor for IEEE Signal Processing Magazine and has served twice as an Associate Editor for IEEE Transactions on Signal Processing. She is the Lead Guest Editor for a Proceedings IEEE Special Issue on Rethinking PCA for Modern Datasets, and of a Signal Processing Magazine Feature Cluster on Exploiting Structure in High-dimensional Data Recovery, both of which will appear in 2018. She is also the Chair of the Women in Signal Processing (WiSP) Committee, a steering committee member of SPS's Data Science Initiative, and an elected member of the SPTM and IVMSP Technical Committees.
Research: Vaswani's recent research has focused on provably correct and practically useful online (recursive) algorithms for various structured (big) data recovery problems. She has worked on (a) dynamic compressive sensing (CS), (b) dynamic robust principal component analysis (RPCA), and most recently on (c) Phaseless PCA and Subspace Tracking (structured phase retrieval). Online algorithms are needed for real-time applications, and even for offline applications, they are typically faster and need less storage compared to batch techniques. Most importantly, her work on these problems has shown that online solutions provide a natural way to exploit temporal dependencies in a dataset, without increasing algorithm complexity; and that exploiting such dynamics provably results in either reduced sample complexity (in case of dynamic CS) or improved outlier tolerance (in case of dynamic RPCA). The former implies proportionally reduced acquisition time for applications such as MRI where data is acquired one sample at a time. The latter implies increased robustness to outliers such as large-sized or slow changing foreground occlusions in videos. All theoretical claims are backed up by extensive experimental evaluations for various video analytics applications and medical imaging applications. In the past she has also worked on particle filtering (sequential Monte Carlo) algorithms, and in computer vision.
o Short Courses and Tutorials
o Invited Tutorial at SPCOM 2018 at the Indian Institute of Science (IISc), Bangalore.
o Invited Short-Course Lecturer for a Global Initiative of Academic Networks (GIAN) course sponsored by the Government of India at IIIT-Delhi, December 2017
o PCA and Robust PCA for Modern Datasets: Theory, Algorithms, and Applications
o Tutorial at ICASSP 2017
o Big Data Mining in Large but Structured Noise
o Editorial Work
o Associate Editor, IEEE Transactions on Signal Processing, 2009-2013, 2017-present
o Lead Guest Editor, Proceedings of the IEEE Special Issue
o Rethinking PCA for Modern Datasets, August 2018
o Lead Guest Editor, IEEE Signal Processing Magazine Feature Articles Cluster
o Exploiting Structure in High-dimensional Data Recovery, 2018 (to appear)
o Guest Editor, IEEE Journal of Special Topics in Signal Processing (JSTSP) Special Issue on
o Robust Subspace Learning and Tracking: Theory, Algorithms, and Applications (Lead Guest Editor: Thierry Bouwmans), to appear in 2019
o Committee Chair
o Chair, Women in Signal Processing (WiSP) Committee (Chair-Elect in 2017), Jan 2018 - present
o Symposium and Workshop Organization (as co-Chair)
o Mini-Symposium on Compressed Sensing and Matrix Completion (co-organizer: Simon Foucart, TAMU), International Linear Algebra Society (ILAS)
o Symposium on Big Data Analysis and Challenges in Medical Imaging (Lead organizer: Anubha Gupta), GlobalSIP 2016
o Workshop on Robust Subspace Learning and Computer Vision (Lead organizer: Thierry Bouwmans), ICCV 2015
o Symposium on Information Processing for Big Data, GlobalSIP, 2014
o Key Committees/Boards
o Elected Member of
o SPTM (Signal Processing Theory and Methods) Technical Committee of SPS, Jan 2016 - Dec 2018
o IVMSP (Image, Video, and Multimedia Signal Processing) Technical Committee of SPS, Jan 2015 - Dec 2017
o Tutorials Chair for IEEE Intl. Conf. Image Proc. (ICIP) 2008