Auckland, New Zealand

Valery Feigin


Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 5(Granted Patents)


Company Filing History:


Years Active: 2020

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1 patent (USPTO):Explore Patents

Title: Valery Feigin: Innovator in Spatio/Spectro-Temporal Data Prediction

Introduction

Valery Feigin is a notable inventor based in Auckland, New Zealand. He has made significant contributions to the field of data prediction through his innovative patent. His work focuses on utilizing spatio/spectro-temporal data to enhance early classification of outcomes in various applications.

Latest Patents

Valery Feigin holds a patent for a "Method and system for predicting outcomes based on spatio/spectro-temporal data." This invention employs temporal or spatio/spectro-temporal data (SSTD) to classify outputs resulting from spatio-temporal patterns. The classification models are based on spiking neural networks (SNN), which are adept at learning and classifying SSTD. This invention has the potential to predict early events across multiple domains, including engineering, bioinformatics, neuroinformatics, ecology, medicine, and economics. The system features a personalized modeling approach for SSTD and early event prediction using an evolving spiking neural network reservoir architecture (eSNNr). It includes a spike-time encoding module that transforms continuous value input into spike trains, a recurrent 3D SNNr, and an eSNN for output classification.

Career Highlights

Valery Feigin is associated with Aut Ventures Limited, where he continues to develop and refine his innovative ideas. His work has garnered attention for its applicability in various fields, showcasing the versatility and importance of his inventions.

Collaborations

Valery has collaborated with notable individuals in his field, including Nikola Kirilov Kasabov and Zeng-Guang Hou. Their combined expertise contributes to the advancement of research and development in spatio/spectro-temporal data applications.

Conclusion

Valery Feigin's contributions to the field of data prediction through his innovative patent highlight the importance of spatio/spectro-temporal data in various applications. His work continues to pave the way for advancements in technology and research.

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