Dallas, TX, United States of America

Rakesh Palacherla

This inventor holds 1 USPTO granted patent. Top assignee: Securonix, Inc.. Active years: 2022.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Rakesh Palacherla. Retrieved from https://idiyas.com/inventor/rakesh-palacherla

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile


% Patents Active = 100.0

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: Rakesh Palacherla: Innovator in Machine Learning Explanations

Introduction

Rakesh Palacherla is a notable inventor based in Dallas, TX (US). He has made significant contributions to the field of machine learning, particularly in the area of prediction explanations for ensemble learning.

Latest Patents

Rakesh holds a patent for a method titled "Prediction explainer for ensemble learning." This innovative approach explains the reasons behind predictions made by a machine learning ensemble. The method assesses the probability of an outcome for a target observation after training on multiple training observations. It determines the similarity between the target observation and each training observation, selects the most similar training observations, ranks them by similarity, and evaluates the significance of features in relation to the prediction.

Career Highlights

Rakesh is currently employed at Securonix, Inc., where he applies his expertise in machine learning and data analysis. His work focuses on enhancing the interpretability of machine learning models, which is crucial for various applications in data-driven decision-making.

Collaborations

Rakesh collaborates with talented professionals in his field, including Igor Baikalov and Joseph Samuel Miller. These partnerships contribute to the advancement of innovative solutions in machine learning.

Conclusion

Rakesh Palacherla's work exemplifies the intersection of technology and innovation in machine learning. His contributions are paving the way for more interpretable and reliable predictive models.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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