## Professional Experience

## Academic Qualifications

## Awards

## Research Interests

## Professional Association

## Courses Taught

## Publications

2020 |
K. Gupta, B. Bhowmick and A. Majumdar, Systems and Methods for Coupled Representation using Transform Learning for Solving Inverse Problems, US Patent App. 16/502,760, 2020 |

J. Gubbi, K. Seemakurthy, N. K. Sandeep, A. Varghese, S. S. Deshpande, M. G. Chandra, P. Balamuralidhar and A. Majumdar, Method and System for solving Inverse Problems using Deep Dictionary Learning, US Patent App. 16/504,196, 2020 | |

2019 |
K. Gupta, B. Bhowmick and A. Majumdar, Systems and Methods for Solving Inverse Problems Using a Coupled Autoencoder, US Patent 10,360,665, 2019 |

T. Bose, A. Majumdar and T. Chattopadhyay, Method and System for Incorporating Regression into Stacked Auto Encoder (SAE), US Patent App. 16/265,906, 2019 | |

2018 |
K. Kumar, M. G. Chandra, A. A. Kumar and A. Majumdar, Anomaly Detection in Industrial Internet-of-Things, (India) filed. |

2017 |
M. Jain, S. Deb and A. Majumdar, Smartphone Based Health Monitoring Using the Inbuilt Camera, (India) filed. Application No. – 201711028803 |

M. Jain, A. V. Subramanyam, S. Deb and A. Majumdar, Cuff-less Blood Pressure Estimation Solution Using Electrocardiogram and Photoplethysmogram, (India) filed. Application No. – 201711028803 |

2018 |
A. Majumdar, Compressed Sensing for Engineers, CRC Press, 2018. |

M. Vatsa, R. Singh and A. Majumdar, Deep Learning in Biometrics CRC Press, 2018. | |

2015 |
A. Majumdar, Compressed Sensing for Magnetic Resonance Image Reconstruction Cambridge University Press, 2015. |

A. Majumdar and R. K. Ward Ed, MRI: Physics, Reconstruction and Analysis CRC Press, 2015. | |

R. Singh, M. Vatsa, A. Majumdar and A. Kumar Ed, Machine Intelligence and Signal Processing Springer, 2015. | |

2014 |
A. Majumdar, Advances in online dynamic MRI reconstruction Frontiers of Medical Imaging, C.H. Chen, World Scientific Publishing, 2014. |

2013 |
A. Majumdar and R. K. Ward, Sparsity Based Reconstruction Techniques in Single Channel and Multi-channel MRI Recent Advances in Medical Imaging Technologies, Troy Farncombe and Kris Iniewski, CRC Press, 2013. |

2011 |
A. Majumdar, R. K. Ward and P. Nasiopoulos, Distributed Face Recognition Face Recognition: Methods, Applications and Technology, Adamo Quaglia and Calogera M. Epifano Ed., Nova Publishers, NY, 2011. |

2011 |
A. Majumdar, R. K. Ward and P. Nasiopoulos, Distributed Face Recognition Face Recognition: Methods, Applications and Technology, Adamo Quaglia and Calogera M. Epifano Ed., Nova Publishers, NY, 2011. |

2010 |
A. Majumdar and R. K. Ward, DCompressive Classification for Face Recognition Face Recognition, Ed. Milos Oravec. Intech Publishers, pp. 47-64, 2010. |

A. Majumdar, Multi Font Bangla Basic Character Recognition via Multiresolution Transforms Digitizing the legacy of Indian Languages, Ed. Salonee Priya, ICFAI University Press, pp. 158-174, 2010. | |

2009 |
A. Majumdar and R. K. Ward, Multiresolution Methods in Face Recognition/a> Recent Advances in Face Recognition, Eds. M. S. Bartlett, K. Delac and M. Grgic, I-Tech Education and Publishing, Vienna, Austria, pp. 79-96, 2009. |

2022 |
Sarita Poonia, Anurag Goel, Smriti Chawla, Namrata Bhattacharya, Priyadarshini Rai, Yi Fang Lee, Yoon Sim Yap, Jay West, Ali Asgar Bhagat, Juhi Tayal, Anurag Mehta, Gaurav Ahuja, Angshul Majumdar, Naveen Ramalingam, and Debarka Sengupta Marker-free characterization full-length transcriptomes of single live circulating tumor cell, , Genome Research. |

Shalini Sharma and Angshul Majumdar A Deep State Space Model for Predicting Cryptocurrency Price , Information Sciences. | |

Angshul Majumdar, Disaggregating a New Appliance On-the-fly without Data Acquisition and Re-training , IEEE Transactions on Instrumentation and Measurement. | |

Ronita Bardhan, Pooja Gupta and Angshul Majumdar, GeoInFuse - A data-driven information fusion for intra-urban form classification in data-scarce heterogeneous cities , CITIES: The International Journal of Urban Policy and Planning. | |

Anurag Goel and Angshul Majumdar, K-means Embedded Deep Transform Learning for Hyperspectral Band Selection , IEEE Geoscience and Remote Sensing Letters. | |

A. Mongia, S. Jain, E. Chouzenoux and A. Majumdar, DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19, Journal of Computational Biology. | |

Angshul Majumdar, Trainingless Energy Disaggregation without Plug-level Sensing, IEEE Transactions on Instrumentation & Measurement. | |

Divyanshu Talwar, Aanchal Mongia, Emilie Chouzenoux and Angshul Majumdar, Binary matrix completion on graphs: Application to collaborative filtering Digital Signal Processing, Vol. 122, 2022 (I.F. 3.381). | |

2021 |
Shikha Singh, Angshul Majumdar, Emilie Chouzenoux and Giovanni Chierchia, Multi-label Deep Convolutional Transform Learning for Non-intrusive Load Monitoring , ACM Transactions on Knowledge Discovery from Data. |

Anurag Goel and Angshul Majumdar, Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification , IEEE Geoscience and Remote Sensing Letters. | |

Shikha Singh and Angshul Majumdar, Multi-label Deep Blind Compressed Sensing for Low-frequency Non-intrusive Load Monitoring , IEEE Power Engineering Letters. | |

Angshul Majumdar, Solving Inverse Problems with Autoencoders on Learnt Graphs , Signal Processing. | |

Aanchal Mongia and Angshul Majumdar, Matrix Completion on Learnt Graphs: Application to Collaborative Filtering , Expert Systems with Applications. | |

Aanchal Mongia, Angshul Majumdar and Emilie Chouzenoux, Computational prediction of Drug-Disease association based on Graph-regularized one bit Matrix completion , IEEE/ACM Transactions on Computational Biology and Bioinformatics. | |

A. Mongia, S. K. Saha, E. Chouzenoux and A. Majumdar, A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials , Nature Scientific Reports. | |

S. Sharma, V. Elvira, E. Chouzenoux and A. Majumdar, Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting , Neurocomputing. | |

Angshul Majumdar, Kernelized Linear Autoencoder , Neural Processing Letters. | |

Shalini Sharma and Angshul Majumdar, Sequential Transform Learning , Transactions on Knowledge Discovery from Data. | |

2020 |
Pooja Gupta, Angshul Majumdar, E. Chouzenoux and G. Chierchia, SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems , Expert Systems With Applications. |

Rachesh Sharma, Neetesh Pandey, Aanchal Mongia, Shreya Mishra, Angshul Majumdar, Vibhor Kumar, FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles , NAR Genomcs and Bioinformatics. | |

S. Sharma, A. Majumdar, Unsupervised Detection of Non-Technical Losses via Recursive Transform Learning , IEEE Transactions on Power Delivery. | |

S. Singh, A. Majumdar and S. Makonin, Compressive Non-Intrusive Load Monitoring , BuildSys'20, short paper. | |

S. Sharma, A. Majumdar, V. Elvira and E. Chouzenoux, Blind Kalman Filtering for Short-term Load Forecasting , IEEE Transactions on Power Systems. | |

P. Rai, D. Sengupta and A. Majumdar, SelfE: Gene Selection via Self Expression for Single-Cell Tata , IEEE Transactions on Computational Biology and Bioinformatics (I.F. 2.8). | |

A. Majumdar, Graph Transform Learning , Neural Networks, Vol. 128, pp. 248-253, 2020. (I.F. 5.7) | |

P. Gupta, J. Maggu, A. Majumdar, E. Chouzenoux and G. Chierchia, DeConFuse: A Deep Convolutional Transform based Unsupervised Fusion Framework , EURASIP Journal on Advances in Signal Processing (T.F. 1.7) | |

J. Maggu, A. Majumdar, E. Chouzenoux and G. Chierchia, Deeply Transformed Subspace Clustering , Signal Processing, Vol. 174, 107628, 2020. (I.F. 4.0) | |

V. Singhal and A. Majumdar, A domain adaptation approach to solve inverse problems in imaging via coupled deep dictionary learning , Pattern Recognition (accepted), (I.F. 5.9) | |

A. Mongia and A. Majumdar, Drug-Target Interaction prediction using Multi Graph Regularized Nuclear Norm Minimization , PLOS ONE, vol. 15, no. 1, p.e0226484, 2020. (I.F. 2.7) | |

V. Singhal and A. Majumdar, Reconstructing Multi-echo Magnetic Resonance Images via Structured Deep Dictionary Learning , Neurocomputing (accepted), (I.F. 4.0) | |

J. Maggu, A. Majumdar and E. Chouzenoux, Transformed Subspace Clustering , IEEE Transactions on Knowledge and Data Engineering (accepted), (I.F. 4.0) | |

A. Mongia, N. Jhamb, E. Chouzenoux and A. Majumdar, Deep Latent Factor Model for Collaborative Filtering , Signal Processing, Vol. 169, 107366, 2020 (I.F. 4.0) | |

A. Mongia, D. Sengupta and A. Majumdar, deepMC: deep Matrix Completion for imputation of single cell RNA-seq data , Journal of Computational Biology (accepted) (I.F. 1.2) | |

S. Singh and A. Majumdar, Non-intrusive load Monitoring via Multi-label Sparse Representation based Classification , IEEE Transactions on Smart Grids, vol. 11, no. 2, pp. 1799-1801, 2020 (I.F. 10.5) | |

2019 |
A. Majumdar and M. Gupta, Recurrent Transform Learning , Neural Networks, vol. 118, pp.271-279, 2019 (I.F. 5.7). |

A. Mongia and A. Majumdar, Matrix Completion on Multiple Graphs: Application in Collaborative Filtering , Signal Processing, Vol. 165, pp. 144-148, 2019. (I.F. 4.0). | |

M. Gaur, S. Makonin, I. V. Bajić and A. Majumdar, Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings , IEEE Access, vol. 7, pp. 62721-62733, 2019 (I.F. 3.5). | |

J. Maggu, H. Agarwal and A. Majumdar, Label Consistent Transform Learning for Hyperspectral Image Classification , IEEE Geosciences and Remote Sensing Letters, Vol. 16 (9), pp. 1502-1506, 2019 (I.F. 2.9). | |

A. Mongia, D. Sengupta and A. Majumdar, McImpute: Matrix completion based imputation for single cell RNA-seq , Frontiers in Genetics, Vol. 10, 2019. (I.F. 4.1). | |

V. Singhal and A. Majumdar, Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11 (12), 5019 – 5028, 2019 (I.F. 2.7). | |

A. Gogna and A. Majumdar, Discriminative Autoencoder for Feature Extraction: Application to Character Recognition, Neural Processing Letters, Vol. 49 (3), pp 1723–1735, 2019 (I.F. 1.7). | |

A. Majumdar, Blind Denoising Autoencoder, IEEE Transactions on Neural Networks and Learning Systems, Vol. 30 (1), pp. 312-317, 2019 (I.F. 7.9). | |

V. Singhal, J. Maggu and A. Majumdar, Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning, IEEE Transactions on Smart Grid, Vol. 10 (3), pp. 2969-2978, 2019 (I.F. 6.6). | |

S. Singh and A. Majumdar, Analysis Co-Sparse Coding for Energy Disaggregation, IEEE Transactions on Smart Grid, Vol. 10 (1), pp. 462-470, 2019. (I.F. 6.6). | |

2018 |
A. Majumdar, Compressed Sensing for Engineers, CRC Press, 2018. |

M. Vatsa, R. Singh and A. Majumdar, Deep Learning in Biometrics CRC Press, 2018. | |

D. Talwar, A. Mongia, D. Sengupta and A. Majumdar, AutoImpute: Autoencoder based imputation of single-cell RNA-seq data Nature Scientific Reports, vol. 8, no. 1, pp. 1-11, 2018 (I.F. 4.1). | |

A. Majumdar, Graph Structured Autoencoder Neural Networks, Vol. 106, pp. 271-280, 2018 (I.F. 7.1). | |

M. Gupta and A. Majumdar, Disaggregating Transform Learning for Non-Intrusive Load Monitoring IEEE ACCESS, vol. 6, pp. 46256 – 46265, 2018 (I.F. 3.2). | |

D. J. Lewis, V. Singhal and A. Majumdar, Solving Inverse Problems in Imaging via Deep Dictionary Learning, IEEE Access, Vol. 7, 37039 - 37049 (I.F. 3.2). | |

A. Majumdar, An Autoencoder Based Formulation for Compressed Sensing Reconstruction, Magnetic Resonance Imaging, Vol. 52, pp. 62-68, 2018 (I.F. 2.5). | |

J. Maggu, P. Singh and A. Majumdar, Multi-echo Reconstruction from Partial K-space Scans via Adaptively Learnt Basis, Magnetic Resonance Imaging, Vol. 52, pp. 62-68, 2018 (I.F. 2.5). | |

2017 |
V. Singhal, A. Majumdar and R. K. Ward, Semi-supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals from Compressive Measurements, IEEE ACCESS, Vol. 6 (1), pp. 545-553. (I.F. 3.2). |

K. Gupta and A. Majumdar, Imposing Class-wise Feature Similarity in Stacked Autoencoders by Nuclear Norm Regularization Neural Processing Letters, (I.F. 1.7). | |

J. Maggu and A. Majumdar, Kernel Transform Learning, Pattern Recognition Letters, Vol. 117, pp. 117-122, 2017 (I.F. 1.9). | |

V. Singhal, H. Agrawal, S. Tariyal and A. Majumdar, Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification IEEE Transactions on Geosciences and Remote Sensing, Vol. 55 (9), pp. 5274-5283, 2017. (I.F. 4.9). | |

A. Gogna and A. Majumdar, Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework, Knowledge Based Systems, Vol. 125, pp. 83-95, 2017. (I.F. 4.5). | |

S. Singh and A. Majumdar, Deep Sparse Coding for Non-Intrusive Load Monitoring IEEE Transactions on Smart Grid, Vol. 9 (5), pp. 4669 - 4678 (I.F. 6.6). | |

V. Singal and A. Majumdar, Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning, Neural Processing Letters, pp. 1- 16, 2017, (I.F. 1.7). | |

I. Manjani, S. Tariyal, M. Vatsa, R. Singh, A. Majumdar, Detecting Silicone Mask based Presentation Attack via Deep Dictionary Learning, IEEE Transactions on Information Forensics and Security, Vol. 12 (7), pp. 1713-1723, 2017 (I.F. 4.3). | |

A. Majumdar, Causal MRI Reconstruction via Kalman Prediction and Compressed Sensing Correction Magnetic Resonance Imaging, Vol. 39, pp. 64-70, 2017 (I.F. 2.2). | |

A. Sankaran, M. Vatsa, R. Singh and A. Majumdar, Group Sparse Autoencoder, Image and Vision Computing, Vol. 60, pp. 64-74, 2017 (I.F. 1.7). | |

M. Gulati, S. S. Ram, A. Majumdar and A. Singh, Single Point Conducted EMI Sensor With Intelligent Inference for Detecting IT Appliances, IEEE Transactions on Smart Grid, Vol. 9 (4), pp. 3716-3726, 2018 (I.F. 6.6). | |

A. Gogna and A. Majumdar, DiABlO: Optimization based design for improving diversity in recommender system Information Sciences, Vol. 378, pp. 59-74, 2017 (I.F. 4.8). | |

J. Mehta and A. Majumdar, RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction, Pattern Recognition, Vol. 63, pp. 499-510, 2017 (I.F. 3.3). | |

A. Majumdar, A. Gogna and R. K. Ward, Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals, IEEE Transactions on Biomedical Engineering, Vol. 64 (9), pp. 2196 – 2205, 2017 (I. F. 2.5). | |

W Singh, A Shukla, S Deb, A Majumdar, Energy Efficient EEG Acquisition and Reconstruction for a Wireless Body Area Network, Integration, the VLSI Journal, Vol. 58, pp. 295-302, 2017. | |

A. Majumdar, M. Vatsa and R. Singh, Face Recognition via Class Sparsity based Supervised Encoding IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39 (6), pp. 1273-1280, 1 2017. (I.F. 8.3). | |

A. Sankaran, G. Goswami, R. Singh, M. Vatsa and A. Majumdar, Class Sparsity Signature based Restricted Boltzmann Machines Pattern Recognition, Vol. 61, pp. 674-685, 2017. (I.F. 3.3). | |

2016 |
S. Tariyal, A. Majumdar, R. Singh and M. Vatsa, Deep Dictionary Learning, IEEE ACCESS, Vol. 4, pp. 10096 – 10109, 2016. (I. F. 1.3). |

N. Kohli, M. Vatsa, R. Singh, A. Noore and A. Majumdar, Hierarchical Representation Learning for Kinship Verification, IEEE Transactions on Image Processing, Vol. 26 (1), pp. 289-302, 217. (I.F. 3.7). | |

S. Tariyal, H. Agrawal and A. Majumdar, Removing Sparse Noise from Hyperspectral Images with Sparse and Low-rank Penalties, SPIE Journal of Electronic Imaging (accepted) (I.F. 0.7). | |

H. Agrawal and A. Majumdar, Hyperspectral Unmixing in the Presence of Mixed Noise using Joint-Sparsity and Total-Variation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9 (9), pp. 4257 – 4266, 2016. (I.F. 3.0). | |

N. Kohli, M. Vatsa, R. Singh, A. Noore and A. Majumdar, Hyperspectral Image Denoising Using Spatio-Spectral Total Variation, IEEE Geosciences and Remote Sensing Letters, Vol. 13 (3), pp. 442-446, 2016. (I.F. 2.2). | |

A. Majumdar, N. Ansari, H. Agarwal and P. Biyani, Impulse Denoising for Hyper-Spectral Images: A Blind Compressed Sensing Approach, Signal Processing, Vol. 119, pp. 136-141, 2016. (I.F. 2.2) | |

G. Goswami, P. Mittal, A. Majumdar, R. Singh and M. Vatsa, Group Sparse Representation based Classification for Multi-feature Multimodal Biometrics, Information Fusion, Vol. 32 (B), pp. 3 - 12. (I.F. 10.7) | |

2015 |
A. Gogna and A. Majumdar, A Comprehensive Recommender System Model: Improving Accuracy for both Warm and Cold Start Users, IEEE ACESS, Vol. 2803 – 2813 (I.F. 1.2). |

A. Gogna and A. Majumdar, Blind Compressive Sensing Formulation Incorporating Metadata for Recommender System Design, APSIPA Transactions on Signal and Information Processing (Cambridge Journal), Vol. 4, 2015. | |

P. Khurana, P. Bhattacharjee and A. Majumdar, Matrix Factorization from Non-linear Projections: Application in Estimating T2 Maps from Few Echoes, Magnetic Resonance Imaging, Vol. 33 (7), pp. 927-931, 2015. (I.F. 2.0). | |

A. Gogna and A. Majumdar, Matrix Completion Incorporating Auxiliary Information for Recommender System Design, Expert Systems with Applications, Vol. 24 (14), pp. 5789-5799, 2015. (I.F. 2.9). | |

A. Majumdar and R. K. Ward, Energy Efficient EEG Sensing and Transmission for Wireless Body Area Networks: A Blind Compressed Sensing Approach, Biomedical Signal Processing and Control, Vol. 20, pp. 1-9, 2015. (I.F. 1.5). | |

H. Aggarwal and A. Majumdar, Exploiting Spatio-Spectral Correlation for Impulse Denoising in Hyperspectral Images, SPIE Journal of Electronic Imaging, Vol. 24(1), 013027, 2015 (I.F. 0.7). | |

A. Shukla and A. Majumdar, Exploiting Inter-channel Correlation in EEG Signal Reconstruction, Biomedical Signal Processing and Control, Vol. 18 (4), pp. 49–55, 2015 (I.F. 1.5). | |

S. S. Ram and A. Majumdar, High-resolution radar imaging of moving humans using doppler processing and compressed sensing, IEEE Transactions on Aerospace and Electronic Systems, Vol. 51, pp. 1279-1287, 2015 (I.F. 1.3). | |

A. Shukla and A. Majumdar, Row-sparse Blind Compressed Sensing for Reconstructing Multi-channel EEG signals, Biomedical Signal Processing and Control, Vol. 18 (4), pp. 174–178, 2015 (I.F. 1.5). | |

A. Majumdar, Improving Synthesis and Analysis Prior Blind Compressed Sensing with Low-rank Constraints for Dynamic MRI Reconstruction, Magnetic Resonance Imaging, Vol. 33(1), pp. 174-179, 2015 (I.F. 2.0). | |

2014 |
A. Majumdar and R. Ward, Exploiting Sparsity and Rank Deficiency for MR Image Reconstruction from Multiple Partial K-Space Scans, IEEE Canadian Journal of Electrical and Computer Engineering, Vol. 37 (4), pp. 228, 235, 2014 (invited). |

A. Majumdar, A. Gogna and R. Ward, Low-rank Matrix Recovery Approach For Energy Efficient EEG Acquisition for Wireless Body Area Network, Sensors, Special Issue on State-of-the-art Sensor Technologies in Canada, Vol. 14(9), pp. 15729-15748, 2014 (I.F. 2.0). | |

A. Majumdar and R. K. Ward, Non-Convex Row-sparse MMV Analysis Prior Formulation For EEG Signal Reconstruction, Biomedical Signal Processing and Control, Vol. 13, pp. 142–147, 2014 (I.F. 1.5). | |

2013 |
A. Majumdar, K. Chaudhury and R. Ward, Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model, Sensors, Special Issue on Magnetic Resonance Sensors, Vol. 13(12), pp. 16714-16735, 2013. (I.F. 2.0) |

M. Mohsina and A. Majumdar, Gabor Based Analysis Prior Formulation For EEG Signal Reconstruction, Biomedical Signal Processing and Control, Vol. 8 (6), pp. 951–955, 2013 (I.F. 1.5). | |

A. Majumdar, Motion Predicted Online Dynamic MRI Reconstruction from Partially Sampled K-Space Data, Magnetic Resonance Imaging, Vol. 31 (9), pp. 1578–1586, 2013. (I.F. 2.0) | |

H. S. Chen, A. Majumdar and P. Kozlowski, Compressed Sensing CPMG with Group-Sparse Reconstruction for Myelin Water Imaging, Magnetic Resonance in Medicine, Vol. 71 (3), pp. 1166-1171, 2013, (I. F. 3.0). | |

A. Majumdar and R. K. Ward, Rank Awareness in Group-sparse Recovery of Multi-echo MR Images, Sensors, Special Issue on Medical and Biomedical Imaging, Vol. 13 (3), pp. 3902-3921, 2013. (I.F. 2.0) | |

A. Majumdar, Improved Dynamic MRI Reconstruction by Exploiting Sparsity and Rank-Deficiency, Magnetic Resonance Imaging, Vol. 31(5), pp. 789-95, 2013. (I.F. 2.0) | |

A. Majumdar, R. K. Ward and T. Aboulnasr, Non-Convex Algorithm for Sparse and Low-Rank Recovery: Application to Dynamic MRI Reconstruction, Magnetic Resonance Imaging, Vol. 31 (3), pp. 448 – 455, 2013. (I.F. 2.0) | |

A. Majumdar, R. K. Ward and T. Aboulnasr, Algorithms to Approximately Solve NP Hard Row-Sparse MMV Recovery Problem: Application to Compressive Color Imaging, IEEE Journal on Emerging and Selected Topics in Circuits and Systems , Special Issue on Circuits, Systems and Algorithms for Compressive Sensing, Vol. 2 (3), pp. 362-369. 2013 (I.F. 1.5). | |

2012 |
A. Majumdar, R. K. Ward and T. Aboulnasr, Compressed Sensing Based near Real-Time Online Dynamic MRI Reconstruction, IEEE Transactions on Medical Imaging, Vol. 31 (2), pp. 2253 – 2266, 2012. (I.F. 3.7) |

A. Majumdar and R. K. Ward, Causal dynamic MRI reconstruction via nuclear norm minimization Magnetic Resonance Imaging, Vol. 30(10), 1483-94, pp. 2012. (I.F. 2.0) | |

A. Majumdar and R. K. Ward, Iterative Estimation of MRI Sensitivity Maps and Image based on SENSE Reconstruction (iSENSE) Concepts in Magnetic Resonance: Part A, Vol. 40 (6), pp. 269-280, 2012. (I.F. 1.7) | |

A. Majumdar, FOCUSS Based Schatten-p Norm Minimization for Real-Time Reconstruction of Dynamic Contrast Enhanced MRI IEEE Signal Processing Letters, Vol. 9(5), pp. 315-318, 2012. (I.F. 1.4) | |

A. Majumdar and R. K. Ward, Calibration-less Multi-Coil MR Image Reconstruction Magnetic Resonance Imaging, Vol. 30(7), pp. 1032-45, 2012. (I.F. 2.0) | |

A. Majumdar and R. K. Ward, Nuclear Norm Regularized SENSE Reconstruction Magnetic Resonance Imaging, Vol. 30 (2), pp. 213–221, 2012. (I.F. 2.0) | |

A. Majumdar and R. K. Ward, On the Choice of Compressed Sensing Priors: An Experimental Study Signal Processing: Image Communication, Vol. 27 (9), pp. 1035–1048, 2012. (I.F. 1.4) | |

A. Majumdar and R. K. Ward, Exploiting Rank Deficiency and Transform Domain Sparsity for MR Image Reconstruction Magnetic Resonance Imaging, Vol. 30 (1), pp. 9–18, 2012. (I.F. 2.0) | |

2011 |
A. Majumdar and R. K. Ward, An Algorithm for Sparse MRI Reconstruction by Schatten p-norm Minimization Magnetic Resonance Imaging, Vol. 29(3), pp. 408-17, 2011. (I.F. 2.0) |

A. Majumdar, Accelerating Multi-Echo T2 Weighted MR Imaging: Analysis Prior Group Sparse Optimization Journal of Magnetic Resonance, Vol. 210 (1), pp. 90-97, 2011. (I.F. 2.1) | |

A. Majumdar and R. K. Ward, Joint Reconstruction of Multi-echo MR Images Using Correlated Sparsity Magnetic Resonance Imaging, Vol. 29 (7), pp. 899-906, 2011. (I.F. 2.0) | |

A. Majumdar and R. K. Ward, Some Empirical Advances in Matrix Completion Signal Processing, Vol. 91 (5), pp. 1334-1338, 2011. (I.F. 2.2) | |

A. Majumdar and R. K. Ward, Increasing Energy Efficiency in Sensor Networks: Blue Noise Sampling and Non-Convex Matrix Completion International Journal of Sensor Networks, Vol. 9, (3/4), pp. 158-169, 2011. (I.F. 1.4) | |

2010 |
A. Majumdar and R. K. Ward, Improved Group Sparse Classifier Pattern Recognition Letters, Vol. 31 (13), pp. 1959-1964, 2010. (I.F. 1.5) |

A. Majumdar and R. K. Ward, Compressed Sensing of Color Images Signal Processing, Vol. 90 (12), 3122-3127, 2010. (I.F. 2.2) | |

A. Majumdar and R. K. Ward, Robust Classifiers for Data Reduced via Random Projections IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 40 (5), pp. 1359 - 1371. (I.F. 3.0) | |

2009 |
A. Majumdar and R. K. Ward, Fast Group Sparse Classification IEEE Canadian Journal of Electrical and Computer Engineering, Vol. 34 (4), pp. 136-144, 2009, (Invited). |

A. Majumdar and R. K. Ward, Image Compression by Block PCA Coding in Curvelet Domain Journal of Signal, Image and Video Processing, Vol. 3 (1), pp. 27-34(8), 2009. (I.F. 0.6) |