Alamo, CA, United States of America

Arash Nourian


Average Co-Inventor Count = 6.5

ph-index = 3

Forward Citations = 21(Granted Patents)


Company Filing History:


Years Active: 2023-2024

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4 patents (USPTO):Explore Patents

Title: Arash Nourian: Innovator in Machine Learning Solutions

Introduction

Arash Nourian is a prominent inventor based in Alamo, CA (US), known for his contributions to the field of machine learning. With a total of 4 patents to his name, he has made significant strides in developing innovative solutions that enhance the functionality and understanding of machine learning models.

Latest Patents

Nourian's latest patents include groundbreaking work on managing missing values in datasets for machine learning models. This patent outlines a comprehensive method that involves importing datasets with missing values, computing data statistics, verifying and updating these values, and recommending models and hyperparameters to effectively handle special or missing values. Another notable patent focuses on meaningfully explaining black-box machine learning models. This invention provides insights into machine learning models trained for risk analysis, analyzing features based on constraints and displaying visual indicators that summarize the model's performance.

Career Highlights

Arash Nourian is currently employed at Fair Isaac Corporation, where he applies his expertise in machine learning to develop advanced solutions. His work has been instrumental in pushing the boundaries of what is possible in the realm of data analysis and model interpretation.

Collaborations

Nourian collaborates with talented professionals in his field, including Longfei Fan and Jari Koister. These partnerships enhance the innovative capacity of his projects and contribute to the advancement of machine learning technologies.

Conclusion

Arash Nourian's contributions to machine learning through his patents and work at Fair Isaac Corporation highlight his role as a key innovator in the industry. His efforts in managing missing values and explaining complex models are paving the way for more effective and transparent machine learning applications.

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