Taimoor Akhtar

623 total citations
20 papers, 383 citations indexed

About

Taimoor Akhtar is a scholar working on Computational Theory and Mathematics, Water Science and Technology and Artificial Intelligence. According to data from OpenAlex, Taimoor Akhtar has authored 20 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computational Theory and Mathematics, 9 papers in Water Science and Technology and 8 papers in Artificial Intelligence. Recurrent topics in Taimoor Akhtar's work include Advanced Multi-Objective Optimization Algorithms (9 papers), Hydrology and Watershed Management Studies (9 papers) and Metaheuristic Optimization Algorithms Research (6 papers). Taimoor Akhtar is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (9 papers), Hydrology and Watershed Management Studies (9 papers) and Metaheuristic Optimization Algorithms Research (6 papers). Taimoor Akhtar collaborates with scholars based in Singapore, Canada and United States. Taimoor Akhtar's co-authors include Christine A. Shoemaker, Jiashi Feng, Ilija Ilievski, Wei Xia, Prasad Daggupati, Narayan Kumar Shrestha, Christoph Schürz, Muhammad Zia ur Rahman Hashmi, Fahad Saeed and Raghavan Srinivasan and has published in prestigious journals such as Scientific Reports, Journal of Hydrology and Hydrology and earth system sciences.

In The Last Decade

Taimoor Akhtar

18 papers receiving 373 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Taimoor Akhtar Singapore 10 141 136 64 64 50 20 383
Gholamreza Azizyan Iran 13 214 1.5× 117 0.9× 107 1.7× 15 0.2× 52 1.0× 30 630
Zhonghua Tang China 14 197 1.4× 73 0.5× 46 0.7× 11 0.2× 14 0.3× 28 472
Naser Moosavian Canada 10 186 1.3× 116 0.9× 36 0.6× 19 0.3× 15 0.3× 22 443
Fuchao Liu China 12 82 0.6× 65 0.5× 81 1.3× 63 1.0× 104 2.1× 67 607
D.K. Subramanian India 12 138 1.0× 61 0.4× 25 0.4× 16 0.3× 14 0.3× 33 402
Youlin Lu China 12 230 1.6× 102 0.8× 60 0.9× 29 0.5× 59 1.2× 17 891
Meng Jin China 9 72 0.5× 30 0.2× 12 0.2× 43 0.7× 11 0.2× 28 399
Xiao-xue Hu China 7 93 0.7× 24 0.2× 76 1.2× 9 0.1× 54 1.1× 11 332
Nan Wei China 10 62 0.4× 11 0.1× 22 0.3× 142 2.2× 42 0.8× 20 539

Countries citing papers authored by Taimoor Akhtar

Since Specialization
Citations

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

Fields of papers citing papers by Taimoor Akhtar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Taimoor Akhtar

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

All Works

20 of 20 papers shown
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Akhtar, Taimoor, et al.. (2024). Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments. Theoretical and Applied Climatology. 155(6). 5525–5542. 11 indexed citations
3.
Xia, Wei, Taimoor Akhtar, Wei Lü, & Christine A. Shoemaker. (2024). Enhanced watershed model evaluation incorporating hydrologic signatures and consistency within efficient surrogate multi-objective optimization. Environmental Modelling & Software. 175. 105983–105983. 3 indexed citations
4.
Akhtar, Taimoor, et al.. (2023). Efficient multi-objective optimization through parallel surrogate-assisted local search with tabu mechanism and asynchronous option. Engineering Optimization. 56(7). 1081–1097. 3 indexed citations
5.
Xia, Wei, Taimoor Akhtar, & Christine A. Shoemaker. (2022). A novel objective function DYNO for automatic multivariable calibration of 3D lake models. Hydrology and earth system sciences. 26(13). 3651–3671. 4 indexed citations
6.
Akhtar, Taimoor, Narayan Kumar Shrestha, Pranesh Kumar Paul, et al.. (2022). A Long-term Global Comparison of IMERG and CFSR with Surface Precipitation Stations. Water Resources Management. 36(14). 5695–5709. 15 indexed citations
7.
Ali, Shahid, et al.. (2022). Past and future changes toward earlier timing of streamflow over Pakistan from bias-corrected regional climate projections (1962–2099). Journal of Hydrology. 617. 128959–128959. 4 indexed citations
8.
Akhtar, Taimoor, et al.. (2021). Integrating $$\varepsilon $$-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems. Journal of Global Optimization. 82(4). 965–992. 11 indexed citations
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Shrestha, Narayan Kumar, Taimoor Akhtar, Ramesh Rudra, et al.. (2020). Can-GLWS: Canadian Great Lakes Weather Service for the Soil and Water Assessment Tool (SWAT) modelling. Journal of Great Lakes Research. 47(1). 242–251. 7 indexed citations
11.
Shrestha, Narayan Kumar, et al.. (2020). Advancing model calibration and uncertainty analysis of SWAT models using cloud computing infrastructure: LCC-SWAT. Journal of Hydroinformatics. 23(1). 1–15. 12 indexed citations
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Shoemaker, Christine A. & Taimoor Akhtar. (2019). An adaptive population-based candidate search algorithm with surrogates for global multi objective optimization of expensive functions. AIP conference proceedings. 2070. 20047–20047. 1 indexed citations
15.
Akhtar, Taimoor & Christine A. Shoemaker. (2019). Combining local surrogates and adaptive restarts for global optimization of moderately expensive functions. AIP conference proceedings. 2070. 20048–20048.
16.
Ilievski, Ilija, Taimoor Akhtar, Jiashi Feng, & Christine A. Shoemaker. (2017). Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 85 indexed citations
17.
Shoemaker, Christine A., Min Pang, Taimoor Akhtar, & David Bindel. (2016). Applications of New Surrogate Global Optimization Algorithms including Efficient Synchronous and Asynchronous Parallelism for Calibration of Expensive Nonlinear Geophysical Simulation Models.. AGU Fall Meeting Abstracts. 2016. 1 indexed citations
18.
Ilievski, Ilija, Taimoor Akhtar, Jiashi Feng, & Christine A. Shoemaker. (2016). Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates. arXiv (Cornell University). 31(1). 822–829. 48 indexed citations
19.
Akhtar, Taimoor, et al.. (2016). SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems. Journal of Global Optimization. 66(3). 417–437. 31 indexed citations
20.
Akhtar, Taimoor & Christine A. Shoemaker. (2015). Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection. Journal of Global Optimization. 64(1). 17–32. 112 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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