Redmond, WA, United States of America

Oleg Isakov


Average Co-Inventor Count = 8.0

ph-index = 1

Forward Citations = 15(Granted Patents)


Company Filing History:


Years Active: 2014

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1 patent (USPTO):Explore Patents

Title: Oleg Isakov: Innovator in Online Learning Algorithms

Introduction

Oleg Isakov is a notable inventor based in Redmond, WA, recognized for his significant contributions to the field of online learning algorithms. With one patent to his name, Isakov's innovative approach enhances the efficiency and effectiveness of machine learning processes.

Latest Patents

Oleg Isakov holds a patent for the "Parallelization of Online Learning Algorithms." This invention encompasses methods, systems, and media designed for a dynamic batch strategy utilized in the parallelization of online learning algorithms. The dynamic batch strategy introduces a merge function that is based on the threshold level difference between the original model state and an updated model state, as opposed to a constant or pre-determined batch size. This system allows for the reading of a batch of incoming streaming data, retrieval of any missing model beliefs from partner processors, and training on the incoming data. The process continues until the measured difference in states surpasses a set threshold level, merging differences based on specific attributes to update a global model state.

Career Highlights

Oleg Isakov is affiliated with Microsoft Technology Licensing, LLC, where he applies his expertise to advance innovative solutions in technology. His work prominently reflects the growing trend of applying dynamic strategies to enhance machine learning capabilities.

Collaborations

Throughout his career, Oleg has worked alongside talented colleagues including Taha Bekir Eren and Weizhu Chen, fostering a collaborative environment that contributes to meaningful advancements in their respective fields.

Conclusion

Oleg Isakov stands out as a forward-thinking inventor whose contributions to the realm of online learning algorithms pave the way for future innovations in machine learning. His patent exemplifies a commitment to enhancing technology and showcases the potential for dynamic approaches to solve complex problems in the field.

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