Ottawa, Canada

Trent A Gray-Donald


Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Trent A Gray-Donald: Innovator in Tree-Based Model Evaluation

Introduction

Trent A Gray-Donald is a notable inventor based in Ottawa, Canada. He has made significant contributions to the field of computer science, particularly in the evaluation of tree-based models. His innovative work has led to the development of a patented system that enhances the sensitivity and fairness of these models.

Latest Patents

Trent holds a patent titled "System and methods for evaluating the sensitivity and fairness bounds of tree-based models." This patent describes a process that facilitates abnormal document self-discovery. The system comprises a memory that stores computer-executable components and a processor that executes these components. Key components of the system include a fairness component, an identification component, a removal component, and an evaluation component. The fairness component calculates the fairness of a dataset for a tree-based model, while the identification component identifies root-to-leaf paths in the model for various records. The removal component ensures that records with similar paths are appropriately managed.

Career Highlights

Trent A Gray-Donald is currently employed at International Business Machines Corporation (IBM). His work at IBM has allowed him to collaborate with other talented professionals in the field, contributing to advancements in technology and innovation.

Collaborations

Some of Trent's notable coworkers include Manish Anand Bhide and Ravi Chandra Chamarthy. Their collaborative efforts have further enriched the research and development environment at IBM.

Conclusion

Trent A Gray-Donald is a distinguished inventor whose work in evaluating tree-based models has made a significant impact in the field of computer science. His innovative patent and contributions to IBM highlight his dedication to advancing technology and improving model fairness.

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