Stony Brook, NY, United States of America

Bhavya Ghai

This inventor holds 2 USPTO granted patents. Top assignee: International Business Machines Corporation. Active years: 2024-2025.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Bhavya Ghai. Retrieved from https://idiyas.com/inventor/bhavya-ghai

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: 2024-2025

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2 patents (USPTO):Explore Patents

Title: Bhavya Ghai: Innovator in Machine Learning

Introduction

Bhavya Ghai is a notable inventor based in Stony Brook, NY (US). He has made significant contributions to the field of machine learning, holding 2 patents that showcase his innovative approach to optimizing learning systems.

Latest Patents

One of Bhavya Ghai's latest patents is titled "Active learning using causal network feedback." This invention involves identifying an unlabeled datapoint from a training dataset and issuing a query for its label. The process includes generating a causal network based on labeled datapoints and modifying the query strategy based on feedback. Another significant patent is "Optimizing a machine learning system," which describes a method for enhancing machine learning systems by comparing attribute rankings from an annotator with those generated by classifiers. This optimization process ensures that the machine learning systems are effectively aligned with the initial predictions.

Career Highlights

Bhavya Ghai is currently employed at International Business Machines Corporation, commonly known as IBM. His work at IBM focuses on advancing machine learning technologies and improving their efficiency and accuracy.

Collaborations

Some of Bhavya's coworkers include Qingzi Vera Liao and Yunfeng Zhang, who contribute to the collaborative environment at IBM.

Conclusion

Bhavya Ghai's innovative work in machine learning and his patents reflect his commitment to advancing technology in this field. His contributions are paving the way for more efficient and effective machine learning systems.

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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