Namrata Vaswani

o a new K-12 initiative to provide school-year-long virtual Math support and extension, currently to elementary school students if one school in Des Moines

o (any grad student in ECpE, Math, Stat, or Comp Sci)

- March 2021: I am looking for Ph.D. students

o Strong background in undergrad level probability and linear algebra

o EE, CS, Math majors

o Math majors can join ECpE or Math (and still work with me if they want)

- June 2019: Named Anderlik Professor starting July 2019

- April 2019: Received the Iowa State University Mid-Career Achievement in Research Award for 2019

- March 2019: Received University of Maryland ECE Distinguished Alumni Award for 2019

- Jan 2019: Elevated to Fellow of the IEEE for contributions to dynamic high-dimensional structured data recovery!

- April 2015: IEEE Signal Processing Society (SPS) Best Paper Award awarded for our Modified-CS paper in IEEE Trans. Sig. Proc., Sept 2010 (announced in December 2014)

- June 2014: Iowa State Early Career Engineering Faculty Research Award

Biography: Namrata Vaswani is Anderlik 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 IEEE Signal Processing Society (SPS) Best Paper Award (2014) for her T-SP paper on Modified-CS co-authored with her graduate student Lu; the University of Maryland ECE Distinguished Alumni Award (2019), the Iowa State Mid-Career Achievement in Research Award (2014), and the Anderlik Professorship (July 2019-present). Vaswani is an IEEE Fellow (class of 2019) for contributions to dynamic high-dimensional structured data recovery.

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.

This page and the Publications page are not updated frequently. Please refer to her Google Scholar page or her CV for recent publications.

Full CV

Research

Alternating GD and Minimization (AltGDmin) for Communication-Efficient and Fast Federated Low Rank Matrix Recovery
Low Rank Column-wise Sensing and Low Rank Phase Retrieval
Robust Subspace Tracking / Dynamic Robust PCA / Recursive Sparse Recovery in Large but Structured Noise
Dynamic Compressive Sensing / Recursive recovery of sparse signal sequences: code for Modified-CS, KF-CS and LS-CS
Key papers and research summary (OLD)

Professional Service

o Short Courses and Tutorials

o Invited Tutorial on High-dimensional Probability for Data Science at 2021 Midwest Big Data symposium

§ slides

o Invited Tutorial at SPCOM 2018 at the Indian Institute of Science (IISc), Bangalore.

§ slides

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 Information Theory, 2021 - 2023

o Area Editor for Special Issues, IEEE Signal Processing Magazine, Jan 2018 - Dec 2020

o Associate Editor, IEEE Transactions on Signal Processing, 2009-2013, 2017-2018

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 and Boards

o Board of Governors, IEEE SPS, Jan 2021 – Dec 2023

o Chair, Women in Signal Processing (WiSP) Committee (Chair-Elect in 2017), Jan 2018 - Dec 2020

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 Member, Membership Board of IEEE Signal Processing Society (SPS), Jan 2018- present

o Steering Committee Member, Data Science Initiative of SPS, April 2017 - present

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

Graduate Students:

Alumni

Zhengyu Chen, Ph.D., 2022
Praneeth Narayanamurthy, Ph.D., 2021
Seyedehsara (Sara) Nayer, Ph.D., 2021
Han Guo, Ph.D., 2019; now at Adobe, USA
Jinchun Zhan, Ph.D., 2015 (B.S. from USTC, China), at Google, USA
Brian Lois, Ph.D., 2015 (co-advised with Prof Leslie Hogben from the Math dept at ISU), Data Science Manager, Capital One, Texas, USA; Earlier at AT&T, Dallas.
Chenlu Qiu, Ph.D., 2013, at Traffic Management Research Institute (TMRI), China
Man Basnet, Ph.D., 2013 (co-advised with Prof. Fritz Keinert from the Math dept at ISU), Lecturer at ISU Mathematics dept.
Wei Lu, Ph.D., 2011, Senior Algorithms Engineer at KLA-Tencor
Samarjit Das, Ph.D., 2010, R&D Group Manager at Bosch Research, USA (earlier: postdoc at the Robotics Institute at CMU)
Rituparna Sarkar, M.S., 2012
Fardad Raisali, M.S., 2012
Taoran Li, M.S, 2011

Current Ph.D. students

Komal Krishna Mogilipalepu, started Spring 2023
Ahmed Ali Abbasi, started Spring 2023
Ankit Pratap Singh, started Fall 2021
Silpa Babu, started Spring 2021

Funding:

Fully Decentralized (Attack-)Resilient Dynamic Low-Rank Matrix Learning, NSF- ECCS, Co-PI, Sept 2022 – Aug 2025. PI: Shana Moothedath
CIF: Small: Secure and Fast Federated Low-Rank Recovery from Few Column-wise Linear, or Quadratic, Projections, NSF - CISE - CCF/CIF, July 2021 - June 2024. Co-PI: Aditya Ramamoorthy
CIF: Small: Structured High-dimensional Data Recovery from Phaseless Measurements, NSF - CISE - CCF, October 2018 - September 2021. Co-PI: Chinmay Hegde, ECE, Iowa State.
KLA-Tencor Unrestricted Grant (Gift), Jan-Dec, 2018 - broadly for research on Outlier Detection via Dynamic Robust PCA
CIF: Small: Online Algorithms for Streaming Structured Big-Data Mining, NSF - CCF - CIF, 2015-2019
Distributed Recursive Robust Estimation: Theory, Algorithms and Applications in Single and Multi-Camera Computer Vision, NSF - ECCS, 2015-2019. Co-PI: Nicola Elia, ECE, Iowa State
Research Grant from Rockwell Collins and matching funds from Iowa Regents Innovation Fund, 2015-2017. Co-PI: Soumik Sarkar, Mech Eng, Iowa State
Co-PI on IDBR Type A - High-Throughput, Large-Scale Plant Phenotyping Platform, NSF - Division of Biological Infrastructure, March 2014- Feb 2017, PI: Liang Dong, ECE, Iowa State
CIF: Small: Recursive Robust Principal Components Analysis (PCA), NSF - CCF - CIF, Sept 2011 - Aug 2015. Co-PI: Fritz Keinert, Mathematics, Iowa State
RI: Small: Exploiting Correlated Sparsity Pattern Change in Dynamic Vision Problems, NSF (IIS - RI), Sept 2011 - Aug 2015.
Recursive Reconstruction of Sparse Signal Sequences, NSF - CCF - CIF, July 2009 - June 2012.
Change Detection in Nonlinear Systems and Applications in Shape Analysis, NSF - ECCS, August 2007 - July 2010.
Iowa State Univ. VPR Office, Faculty Grant Development Award, Summer 2006

Teaching:

Office Hours for Fall 2021:

Fall 2021: EE 623X: High-Dimensional Probability and Linear Algebra for Machine Learning

Primarily based on the High Dimensional Probability book by R. Vershynin, and applications of the results in ML.

Spring 2021: EE 425: Machine Learning: A Signal Processing Perspective
Spring 2021: EE 322 (Probabilistic Methods for Electrical Engineers)
Fall 2020: EE 322 (Probabilistic Methods for Electrical Engineers)
Spring 2020: EE 425X: Machine Learning: A Signal Processing Perspective
Fall 2019: EE 520 (Special topics in CSP: Foundations of Statistical ML)
Spring 2019: EE 425X: Machine Learning: A Signal Processing Perspective
Fall 2018: EE 322 (Probabilistic Methods for Electrical Engineers)

Spring 2018: EE 527 (Estimation and Detection Theory)
Fall 2017: EE 224 Recitations
Spring 2017: EE 322 (Probabilistic Methods for Electrical Engineers)
Fall 2016: EE 520 (Special Topics in CSP: Statistical Machine Learning)
Spring 2016: EE 527 (Estimation and Detection Theory)
Fall 2015: EE 520 (Special Topics in CSP: Statistical Machine Learning)
Spring 2015: Sabbatical
Fall 2014: EE 322 (Probabilistic Methods for Electrical Engineers)
Spring 2014: EE 527 (Estimation and Detection Theory) and EE 224 recitations
Fall 2013: EE 224 recitations
Spring 2013: EE 520 (Special Topics in CSP): Matrix Completion and Sparse Recovery
Fall 2012: EE 322 (Probabilistic Methods for Electrical Engineers)
Spring 2012: EE 424 (Introduction to DSP)
Spring 2012: EE 527 (Estimation and Detection Theory)
Fall 2011: EE 224 recitations and labs
Spring 2011: EE 322 (Probabilistic Methods for Electrical Engineers)
Fall 2010: EE 524 (Digital Signal Processing)
Spring 2010: EE 527 (Detection and Estimation Theory)
Spring 2010: EE 224 recitations
Fall 2009: EE 528 (Digital Image Processing)
Spring 2009: EE 520 (Special Topics in CSP: Compressive Sensing)
Spring 2009: EE 322 (Probabilistic Methods for Electrical Engineers)
Fall 2008: EE 322 (Probabilistic Methods for Electrical Engineers)
Spring 2008: EE 527 (Detection and Estimation Theory)
Fall 2007: EE 322 (Probabilistic Methods for Electrical Engineers)
Spring 2007: EE 528 (Digital Image Processing)
Fall 2006: EE 322 (Probabilistic Methods for Electrical Engineers)
Fall 2006: EE 597 (CSP Seminar)
Spring 2006: EE 224 (Signals and Systems) Lab, Sections B & F
Fall 2005: EE 520: Special Topics in Signal Processing for Image Analysis and Computer Vision