Laya Das

762 total citations
28 papers, 515 citations indexed

About

Laya Das is a scholar working on Control and Systems Engineering, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Laya Das has authored 28 papers receiving a total of 515 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Control and Systems Engineering, 9 papers in Artificial Intelligence and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Laya Das's work include Fault Detection and Control Systems (8 papers), Neural Networks and Applications (4 papers) and Control Systems and Identification (4 papers). Laya Das is often cited by papers focused on Fault Detection and Control Systems (8 papers), Neural Networks and Applications (4 papers) and Control Systems and Identification (4 papers). Laya Das collaborates with scholars based in India, United States and Switzerland. Laya Das's co-authors include Balasubramaniam Natarajan, Babji Srinivasan, Sai Munikoti, Venkat Venkatasubramanian, Raghunathan Rengaswamy, Deepesh Agarwal, Mahantesh Halappanavar, Giovanni Sansavini, Blazhe Gjorgiev and Dinesh Garg and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, Applied Energy and AIChE Journal.

In The Last Decade

Laya Das

28 papers receiving 504 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laya Das India 13 229 135 99 75 55 28 515
Zhanpeng Fang China 10 156 0.7× 208 1.5× 99 1.0× 67 0.9× 54 1.0× 29 672
Ali Moradi Amani Australia 15 323 1.4× 428 3.2× 105 1.1× 47 0.6× 132 2.4× 61 764
Ji Han China 13 177 0.8× 357 2.6× 52 0.5× 116 1.5× 41 0.7× 48 640
Lingen Luo China 16 227 1.0× 488 3.6× 46 0.5× 69 0.9× 37 0.7× 66 674
Rong Jia China 15 230 1.0× 326 2.4× 84 0.8× 36 0.5× 57 1.0× 86 628
U. Qasim Pakistan 10 118 0.5× 233 1.7× 84 0.8× 66 0.9× 184 3.3× 32 502
Ruoli Tang China 18 337 1.5× 459 3.4× 179 1.8× 33 0.4× 85 1.5× 55 1.0k

Countries citing papers authored by Laya Das

Since Specialization
Citations

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

Fields of papers citing papers by Laya Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laya Das

This figure shows the co-authorship network connecting the top 25 collaborators of Laya Das. A scholar is included among the top collaborators of Laya Das 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 Laya Das. Laya Das 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
1.
Das, Laya, Blazhe Gjorgiev, & Giovanni Sansavini. (2025). An improved anomaly detection model for automated inspection of power line insulators. Engineering Applications of Artificial Intelligence. 158. 111431–111431. 1 indexed citations
2.
Das, Laya, Blazhe Gjorgiev, & Giovanni Sansavini. (2024). Uncertainty-aware deep learning for monitoring and fault diagnosis from synthetic data. Reliability Engineering & System Safety. 251. 110386–110386. 17 indexed citations
3.
Das, Laya, et al.. (2023). A testbed for studying the interactions between human operators and advanced control systems. Computers & Chemical Engineering. 178. 108377–108377. 1 indexed citations
4.
Munikoti, Sai, Deepesh Agarwal, Laya Das, Mahantesh Halappanavar, & Balasubramaniam Natarajan. (2023). Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 15051–15071. 68 indexed citations
5.
Munikoti, Sai, Deepesh Agarwal, Laya Das, & Balasubramaniam Natarajan. (2022). A general framework for quantifying aleatoric and epistemic uncertainty in graph neural networks. Neurocomputing. 521. 1–10. 12 indexed citations
6.
Venkatasubramanian, Venkat, et al.. (2022). A unified theory of emergent equilibrium phenomena in active and passive matter. Computers & Chemical Engineering. 164. 107887–107887. 8 indexed citations
7.
Munikoti, Sai, Deepesh Agarwal, Laya Das, Mahantesh Halappanavar, & Balasubramaniam Natarajan. (2022). Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications. arXiv (Cornell University). 1 indexed citations
8.
Munikoti, Sai, Laya Das, & Balasubramaniam Natarajan. (2021). Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks. 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 3245–3251. 3 indexed citations
9.
Munikoti, Sai, Laya Das, & Balasubramaniam Natarajan. (2020). Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks. arXiv (Cornell University). 42 indexed citations
10.
Das, Laya, et al.. (2020). Hidden representations in deep neural networks: Part 2. Regression problems. Computers & Chemical Engineering. 139. 106895–106895. 35 indexed citations
11.
Das, Laya, et al.. (2019). On developing a framework for detection of oscillations in data. ISA Transactions. 89. 96–112. 5 indexed citations
12.
Munikoti, Sai, Laya Das, Balasubramaniam Natarajan, & Babji Srinivasan. (2019). Data-Driven Approaches for Diagnosis of Incipient Faults in DC Motors. IEEE Transactions on Industrial Informatics. 15(9). 5299–5308. 52 indexed citations
13.
Das, Laya, et al.. (2019). Hidden representations in deep neural networks: Part 1. Classification problems. Computers & Chemical Engineering. 134. 106669–106669. 18 indexed citations
14.
Das, Laya, Dinesh Garg, & Babji Srinivasan. (2019). NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid. Applied Energy. 257. 113966–113966. 20 indexed citations
15.
Joshi, Amit M., Laya Das, Balasubramaniam Natarajan, & Babji Srinivasan. (2019). Effect of Transformation in Compressed Sensing of Smart Grid Data. 177–182. 5 indexed citations
16.
Das, Laya, et al.. (2018). A novel approach for benchmarking and assessing the performance of state estimators. ISA Transactions. 80. 137–145. 1 indexed citations
17.
Das, Laya, Raghunathan Rengaswamy, & Babji Srinivasan. (2017). Data mining and control loop performance assessment: The multivariate case. AIChE Journal. 63(8). 3311–3328. 14 indexed citations
18.
Das, Laya, et al.. (2017). A novel approach to evaluate state estimation approaches for anaerobic digester units under modeling uncertainties: Application to an industrial dairy unit. Journal of environmental chemical engineering. 5(4). 4004–4013. 5 indexed citations
19.
Das, Laya, Babji Srinivasan, & Raghunathan Rengaswamy. (2015). Multivariate Control Loop Performance Assessment With Hurst Exponent and Mahalanobis Distance. IEEE Transactions on Control Systems Technology. 24(3). 1067–1074. 18 indexed citations
20.
Das, Laya, Babji Srinivasan, & Raghunathan Rengaswamy. (2014). Data driven approach for performance assessment of linear and nonlinear Kalman filters. 116. 4127–4132. 4 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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