Parami Wijesinghe
Papers
1
Total Citations
55
H-Index
1
About
Parami Wijesinghe is a leading researcher in neuromorphic computing, with a primary focus on advancing liquid state machines (LSMs)—a bio-inspired spiking neural network model. Her seminal work, "Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines" (2019), has garnered 55 citations, establishing her as a key contributor to the field. In this study, she systematically investigated how ensembles of liquid reservoirs can improve the computational accuracy and robustness of LSMs, addressing critical bottlenecks in temporal data processing. Her contributions extend to optimizing the random interlinked reservoir structures, enabling more efficient robot control, sequence generation, and action recognition. By bridging theoretical analysis with practical performance gains, Wijesinghe’s work has influenced both the design of neuromorphic hardware and the application of spiking neural networks in real-world scenarios. Her research is particularly notable for its impact on low-power, event-driven computing systems, making her a vital figure in the push toward energy-efficient artificial intelligence.
Research Focus
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Top Papers
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