Bamdev Mishra

31 papers receiving 416 citations

Peers

Bamdev Mishra
Comparison fields: 5 of 59
  • Computational Mathematics 97
  • Computational Mechanics 232
  • Numerical Analysis 55
  • Computer Vision and Pattern Recognition 142
  • Signal Processing 50
Replace Kim Batselier with:
Kim Batselier Hong Kong
Chunfeng Cui China
Venkat Chandrasekaran United States
Silvia Gandy Japan
Adi Shraibman Israel
Minru Bai China
Guang‐Jing Song China
Ali Çivril United States
Max Simchowitz United States
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Citations per field
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Citations per year

Countries citing papers authored by Bamdev Mishra

Since Specialization
Citations

This map shows the geographic impact of Bamdev Mishra'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 Bamdev Mishra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bamdev Mishra more than expected).

Fields of papers citing papers by Bamdev Mishra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bamdev Mishra. 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 Bamdev Mishra. The network helps show where Bamdev Mishra may publish in the future.

Co-authors

The 22 scholars most cited alongside Bamdev Mishra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bamdev Mishra Line = papers co-authored together Bamdev Mishra links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201367
2 201365
3 201547
4 201941
5 201938
6
Low-rank tensor completion: a Riemannian manifold preconditioning approach
201634
7 201632
8 201517
9
Riemannian Stochastic Recursive Gradient Algorithm
201815
10 201715
11 20199
12
Inexact trust-region algorithms on Riemannian manifolds
20188
13
Riemannian stochastic variance reduced gradient
20177
14
A Riemannian approach to large-scale constrained least-squares with symmetries
20147
15
A Dual Framework for Low-rank Tensor Completion
20185
16 20233
17
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
20193
18
A Unified Framework for Structured Low-rank Matrix Learning
20173
19 20182
20 20222

About Bamdev Mishra

Bamdev Mishra is a scholar working on Computational Mechanics, Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mathematics and Computational Theory and Mathematics, having authored 34 papers that have together received 434 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (18 papers), Stochastic Gradient Optimization Techniques (11 papers), Tensor decomposition and applications (6 papers), Face and Expression Recognition (5 papers), Markov Chains and Monte Carlo Methods (4 papers), Advanced Optimization Algorithms Research (3 papers), Advanced Image Processing Techniques (3 papers) and Medical Image Segmentation Techniques (2 papers). The work is most often cited by research in Computational Mathematics (97 citations), Computational Mechanics (232 citations), Numerical Analysis (55 citations), Computer Vision and Pattern Recognition (142 citations) and Signal Processing (50 citations). Bamdev Mishra has collaborated with scholars based in India, Japan and United States. Frequent co-authors include Rodolphe Sepulchre, Hiroyuki Kasai, Gilles Meyer, Hiroyuki Sato, Francis Bach, Silvère Bonnabel, Junbin Gao, Anoop Kunchukuttan, Xia Hong and Yanfeng Sun. Their work appears in journals such as SIAM Journal on Optimization, Machine Learning, Transactions of the Association for Computational Linguistics, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Wireless Communications.

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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