**Namrata
Vaswani
**Professor of Electrical and Computer Engineering and Anderlik
Professor of Engineering

Courtesy Professor of Mathematics

Iowa State University

3121 Coover Hall, Ames IA 50011

**Phone:**
515-294-4012. **Email:**
firstname@iastate.edu

Publications: Scholar |

Always looking for

1. Ph.D. student(s) and or a postdoc

2. CyMath volunteers: ISU Math/Stat and Engineering grad students, postdocs, faculty (CyMath is a K-12 Math mentoring/tutoring initiative)

**Biography**

Namrata Vaswani is a Professor of Electrical
and Computer Engineering, and the Anderlik Professor of Engineering at Iowa State
University. She also holds a courtesy
professorship in the department of Mathematics. 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, and signal processing, and imaging
(MRI and video analytics). Vaswani is also the director of the CyMath
graduate student lead K-8 math mentoring/tutoring program at Iowa State.

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
(2019), the Anderlik Professorship (July
2019-present). Vaswani is an AAAS Fellow (class of 2023) and an IEEE Fellow (class of 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 has served as an Area Editor for IEEE Signal Processing Magazine, and an
Associate Editor for the IEEE Transactions on Information Theory and the 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.
She served as 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.

**Alumni**- Zhengyu Chen, Ph.D., 2022
- Praneeth Narayanamurthy,
**Ph.D.**, 2021, Postdoc at USC - Seyedehsara (Sara) Nayer,
**Ph.D.**, 2021, ASML, USA - 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) - MS graduates
- Rituparna Sarkar,
**M.S.**, 2012 - Fardad Raisali,
**M.S.**, 2012 - Taoran Li,
**M.S**, 2011 **Current Ph.D. students**- Ahmed Ali Abbasi, started Spring 2023
- Ankit Pratap Singh, started Fall 2021
- Silpa Babu, started Spring 2021
- Current M.S. students
- Komal Krishna Mogilipalepu, started Spring 2023

- On Impact of Grade School Math Tutoring by MI-STEM Graduate Students and Faculty, ISU VPR Office-Community Vitality RIR, June 2024-May 2025. Co-PI: Mohamed Selim
- CIF: Small: Efficient and Secure Federated Structure Learning from Bad Data, NSF-CISE, June 2024 - May 2027. Co-PI: none.
- Fully Decentralized (Attack-)Resilient Dynamic Low-Rank Matrix Learning, NSF-Engineering-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:**

- Fall 2024:
- Fall 2024, 2023, 2022, 2021: EE 623:
**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 2022: Sabbatical (FPDA)
- Spring 2024, 2023, 2021, 2020, 2019: 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

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