Montreal, Canada

Kartik Ahuja

This inventor holds 1 USPTO granted patent. Top assignees: International Business Machines Corporation, Universitéde Montreal. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Kartik Ahuja. Retrieved from https://idiyas.com/inventor/kartik-ahuja-t7ha1ff9

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

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Average Co-Inventor Count = 1.0


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Kartik Ahuja: Innovator in Machine Learning Explanations

Introduction

Kartik Ahuja is a notable inventor based in Montreal, Canada. He has made significant contributions to the field of machine learning, particularly in generating explanations for complex models. His innovative approach aims to enhance the interpretability of machine learning outputs, making them more accessible and understandable.

Latest Patents

Kartik Ahuja holds 1 patent for his work titled "Generating locally invariant explanations for machine learning." This patent discloses techniques for generating explanations for machine learning models. The process involves identifying an ML model, its output, and a set of constraints. Ahuja's techniques generate neighborhoods related to the ML model based on these constraints. He constructs predictors for each neighborhood and combines them to create comprehensive explanations for the model's outputs.

Career Highlights

Throughout his career, Kartik Ahuja has worked with prestigious organizations, including IBM and Université de Montreal. His experience in these institutions has allowed him to collaborate with leading experts in the field of machine learning and artificial intelligence.

Collaborations

One of his notable coworkers is Amit Dhurandhar, with whom he has likely shared insights and expertise in their research endeavors.

Conclusion

Kartik Ahuja's contributions to machine learning and his innovative patent demonstrate his commitment to advancing technology in this field. His work not only enhances the functionality of machine learning models but also improves their interpretability for users.

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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