Tan Nhat Pham

449 total citations
9 papers, 282 citations indexed

About

Tan Nhat Pham is a scholar working on Computational Mechanics, Mathematical Physics and Artificial Intelligence. According to data from OpenAlex, Tan Nhat Pham has authored 9 papers receiving a total of 282 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computational Mechanics, 2 papers in Mathematical Physics and 2 papers in Artificial Intelligence. Recurrent topics in Tan Nhat Pham's work include Sparse and Compressive Sensing Techniques (3 papers), Numerical methods in inverse problems (2 papers) and Artificial Intelligence in Healthcare (1 paper). Tan Nhat Pham is often cited by papers focused on Sparse and Compressive Sensing Techniques (3 papers), Numerical methods in inverse problems (2 papers) and Artificial Intelligence in Healthcare (1 paper). Tan Nhat Pham collaborates with scholars based in Australia and Vietnam. Tan Nhat Pham's co-authors include Son Vu Truong Dao, Ly Van Tran, Tuan Minh Le, Nima Amjady, Nargiz Sultanova, Syed Islam, Minh N. Dao, Rakibuzzaman Shah, Guoyin Li and Ibrahim Anwar Ibrahim and has published in prestigious journals such as Journal of Cleaner Production, European Journal of Operational Research and IEEE Access.

In The Last Decade

Tan Nhat Pham

6 papers receiving 268 citations

Peers

Tan Nhat Pham

PS

AI

HIM

AC

CVPR

Anupam Singh India

G. R. Sreekanth India

Md. Ashiqul Islam Bangladesh

S. Nandhini India

Shweta Lamba India

Mujtaba Husnain Pakistan

Jivan S. Parab India

K. Aditya Shastry India

Sultana Umme Habiba Bangladesh

Md. Toukir Ahmed United States

Anupam Singh India

Tan Nhat Pham

96 ×0.6

PS

35 ×0.5

AI

18 ×0.3

HIM

46 ×0.8

AC

23 ×1.4

CVPR

Citations per year, relative to Tan Nhat Pham Tan Nhat Pham (= 1×) peers Anupam Singh

Countries citing papers authored by Tan Nhat Pham

Since Specialization

Citations

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

Fields of papers citing papers by Tan Nhat Pham

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tan Nhat Pham. 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 Tan Nhat Pham. The network helps show where Tan Nhat Pham may publish in the future.

Co-authorship network of co-authors of Tan Nhat Pham

This figure shows the co-authorship network connecting the top 25 collaborators of Tan Nhat Pham. A scholar is included among the top collaborators of Tan Nhat Pham 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 Tan Nhat Pham. Tan Nhat Pham is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Pham, Tan Nhat, Minh N. Dao, Nima Amjady, & Rakibuzzaman Shah. (2025). A proximal splitting algorithm for generalized DC programming with applications in signal recovery. European Journal of Operational Research. 326(1). 42–53. 1 indexed citations

2.

Pham, Tan Nhat, Rakibuzzaman Shah, Nima Amjady, & Syed Islam. (2024). Prediction of fire danger index using a new machine learning based method to enhance power system resiliency against wildfires. IET Generation Transmission & Distribution. 18(23). 4008–4022. 3 indexed citations

3.

Ibrahim, Ibrahim Anwar, Tan Nhat Pham, Rakibuzzaman Shah, M. J. Hossain, & Syed Islam. (2024). Techno-economic assessment of an industrial prosumer with biomass investment and time varying tariffs: An Australian case study. Journal of Cleaner Production. 481. 143957–143957.

4.

Pham, Tan Nhat, Minh N. Dao, Andrew Eberhard, & Nargiz Sultanova. (2024). Bregman Proximal Linearized ADMM for Minimizing Separable Sums Coupled by a Difference of Functions. Journal of Optimization Theory and Applications. 203(2). 1622–1658. 1 indexed citations

5.

Pham, Tan Nhat, Rakibuzzaman Shah, Minh N. Dao, Nargiz Sultanova, & Syed Islam. (2024). Low and medium voltage distribution network planning with distributed energy resources: a survey. Electrical Engineering. 107(2). 1797–1828.

6.

Pham, Tan Nhat, Minh N. Dao, Rakibuzzaman Shah, et al.. (2023). A proximal subgradient algorithm with extrapolation for structured nonconvex nonsmooth problems. Numerical Algorithms. 94(4). 1763–1795. 2 indexed citations

7.

Pham, Tan Nhat, Ly Van Tran, & Son Vu Truong Dao. (2021). A Multi-Restart Dynamic Harris Hawk Optimization Algorithm for the Economic Load Dispatch Problem. IEEE Access. 9. 122180–122206. 4 indexed citations

8.

Le, Tuan Minh, et al.. (2020). A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic. IEEE Access. 9. 7869–7884. 106 indexed citations

9.

Pham, Tan Nhat, Ly Van Tran, & Son Vu Truong Dao. (2020). Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection. IEEE Access. 8. 189960–189973. 165 indexed citations

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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