Morteza Mardani

2.9k total citations · 2 hit papers
34 papers, 1.2k citations indexed

About

Morteza Mardani is a scholar working on Computational Mechanics, Computer Networks and Communications and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Morteza Mardani has authored 34 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computational Mechanics, 8 papers in Computer Networks and Communications and 8 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Morteza Mardani's work include Sparse and Compressive Sensing Techniques (19 papers), Microwave Imaging and Scattering Analysis (6 papers) and Medical Imaging Techniques and Applications (5 papers). Morteza Mardani is often cited by papers focused on Sparse and Compressive Sensing Techniques (19 papers), Microwave Imaging and Scattering Analysis (6 papers) and Medical Imaging Techniques and Applications (5 papers). Morteza Mardani collaborates with scholars based in United States, Iran and United Kingdom. Morteza Mardani's co-authors include Georgios B. Giannakis, Gonzalo Mateos, John M. Pauly, Shreyas Vasanawala, Joseph Y. Cheng, Lei Xing, Enhao Gong, Greg Zaharchuk, Feiyu Chen and Christopher M. Sandino and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Morteza Mardani

34 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI

2018 355 citations Morteza Mardani, Enhao Gong et al. IEEE Transactions on Medical Imaging profile →
FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators

2023 98 citations Thorsten Kurth, Shashank Subramanian et al. profile →

Peers

Countries citing papers authored by Morteza Mardani

Since Specialization

Citations

This map shows the geographic impact of Morteza Mardani's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Morteza Mardani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Morteza Mardani more than expected).

Fields of papers citing papers by Morteza Mardani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Morteza Mardani. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Morteza Mardani. The network helps show where Morteza Mardani may publish in the future.

Co-authorship network of co-authors of Morteza Mardani

This figure shows the co-authorship network connecting the top 25 collaborators of Morteza Mardani. A scholar is included among the top collaborators of Morteza Mardani based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Morteza Mardani. Morteza Mardani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI 2024 ·IEEE Transactions on Medical Imaging ·(unknown), Morteza Mardani, Congyu Liao, Zhitao Li, Shreyas Vasanawala, John M. Pauly	3
2	FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators breakdown → 2023 ·Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, (unknown), Karthik Kashinath, Anima Anandkumar	98
3	Wasserstein GANs for MR Imaging: From Paired to Unpaired Training 2020 ·IEEE Transactions on Medical Imaging ·Lei Ke, Morteza Mardani, John M. Pauly, Shreyas Vasanawala	49
4	Compressed Sensing: From Research to Clinical Practice With Deep Neural Networks: Shortening Scan Times for Magnetic Resonance Imaging 2020 ·IEEE Signal Processing Magazine ·Christopher M. Sandino, Joseph Y. Cheng, Feiyu Chen, Morteza Mardani, John M. Pauly, Shreyas Vasanawala	101
5	Neural FFTs for Universal Texture Image Synthesis 2020 ·Neural Information Processing Systems ·Morteza Mardani, Guilin Liu, Ayşegül Dündar, (unknown), Andrew Tao, Bryan Catanzaro	7
6	Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging 2020 ·PubMed ·Ukash Nakarmi, Joseph Y. Cheng, (unknown), Morteza Mardani, John M. Pauly, Leslie Ying, Shreyas Vasanawala	10
7	VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis. 2019 ·arXiv (Cornell University) ·(unknown), Morteza Mardani, Joseph Y. Cheng, Shreyas Vasanawala, John M. Pauly	1
8	Deep Generative Adversarial Neural Networks for Compressive Sensing MRI breakdown → 2018 ·IEEE Transactions on Medical Imaging ·Morteza Mardani, Enhao Gong, Joseph Y. Cheng, Shreyas Vasanawala, Greg Zaharchuk, Lei Xing, John M. Pauly	355
9	Neural Proximal Gradient Descent for Compressive Imaging 2018 ·arXiv (Cornell University) ·Morteza Mardani, Qingyun Sun, (unknown), Vardan Papyan, Hatef Monajemi, John M. Pauly, David L. Donoho	34
10	Online Categorical Subspace Learning for Sketching Big Data with Misses 2017 ·IEEE Transactions on Signal Processing ·Yanning Shen, Morteza Mardani, Georgios B. Giannakis	13
11	Recurrent generative adversarial neural networks for compressive imaging 2017 ·Morteza Mardani, Enhao Gong, Joseph Y. Cheng, John M. Pauly, Lei Xing	14
12	Deep-Learning Based Prediction of Achievable Dose for Personalizing Inverse Treatment Planning 2016 ·International Journal of Radiation OncologyBiologyPhysics ·Morteza Mardani, Peng Dong, Lei Xing	21
13	Estimating Traffic and Anomaly Maps via Network Tomography 2015 ·IEEE/ACM Transactions on Networking ·Morteza Mardani, Georgios B. Giannakis	47
14	Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors 2015 ·IEEE Transactions on Signal Processing ·Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis	127
15	Online sketching for big data subspace learning 2015 ·Morteza Mardani, Georgios B. Giannakis	1
16	Classification of streaming big data with misses 2014 ·2014 48th Asilomar Conference on Signals, Systems and Computers ·(unknown), Morteza Mardani, Georgios B. Giannakis	3
17	Imputation of streaming low-rank tensor data 2014 ·Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis	3
18	Rank minimization for subspace tracking from incomplete data 2013 ·Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis	10
19	Dynamic Anomalography: Tracking Network Anomalies Via Sparsity and Low Rank 2012 ·IEEE Journal of Selected Topics in Signal Processing ·Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis	85
20	Optimization of wireless multi-hop networks with random access 2011 ·Morteza Mardani, Seung-Jun Kim, Georgios B. Giannakis	1

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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