Laya Das

762 citations
28 papers · 515 indexed · h-index 13

Impact in

Papers in

Laya Das

28 papers receiving 504 citations

Peers

Laya Das
Comparison fields: 5 of 87
  • Control and Systems Engineering 229
  • Safety, Risk, Reliability and Quality 36
  • Statistical and Nonlinear Physics 43
  • Civil and Structural Engineering 75
  • Artificial Intelligence 99
Replace Xiaohong Wang with:
Xiaohong Wang China
Sanjiv Sharma United Kingdom
Feiyun Cong China
James Z. Hare United States
Stefan Byttner Sweden
Rong Jia China
Amin Ramezani Iran
Zhanpeng Fang China
Laya Das relative to Xiaohong Wang China Xiaohong Wang's profile →
Citations per field
00.5×3.1×
Xiaohong Wang · 1×
Citations per year

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-authors

The 16 scholars most cited alongside Laya Das, 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 Laya Das Line = papers co-authored together Laya Das links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020119
2 202368
3 201952
4 202042
5 202035
6 201821
7 201920
8 201918
9 201518
10 201718
11 202417
12 201714
13 202212
14 202211
15 20159
16 20228
17 20195
18 20175
19 20195
20 20155

About Laya Das

Laya Das is a scholar working on Control and Systems Engineering, Statistical and Nonlinear Physics, Artificial Intelligence, Signal Processing and Civil and Structural Engineering, having authored 28 papers that have together received 515 indexed citations. Recurring topics across this work include Fault Detection and Control Systems (8 papers), Neural Networks and Applications (4 papers), Control Systems and Identification (4 papers), Advanced Control Systems Optimization (3 papers), Advanced Graph Neural Networks (3 papers), Sparse and Compressive Sensing Techniques (3 papers), Water Systems and Optimization (2 papers) and High voltage insulation and dielectric phenomena (2 papers). The work is most often cited by research in Control and Systems Engineering (229 citations), Safety, Risk, Reliability and Quality (36 citations), Statistical and Nonlinear Physics (43 citations), Civil and Structural Engineering (75 citations) and Artificial Intelligence (99 citations). Laya Das has collaborated with scholars based in India, United States and Switzerland. Frequent co-authors include Balasubramaniam Natarajan, Babji Srinivasan, Sai Munikoti, Venkat Venkatasubramanian, Raghunathan Rengaswamy, Deepesh Agarwal, Mahantesh Halappanavar, Blazhe Gjorgiev, Giovanni Sansavini and Dinesh Garg. Their work appears in journals such as Computers & Chemical Engineering, IEEE Transactions on Industrial Informatics, ISA Transactions, Reliability Engineering & System Safety and AIChE Journal.

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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