Raghu Kulkarni

Title: Innovations by Inventor Raghu Kulkarni

Introduction

Raghu Kulkarni is an accomplished inventor based in Buffalo Grove, IL (US). He has made significant contributions to the field of data science and machine learning, holding a total of 4 patents. His work focuses on reducing bias in data science models and enhancing the interpretability of machine learning algorithms.

Latest Patents

One of his latest patents is titled "Computing system and method for creating a data science model having reduced bias." This innovative computing platform is designed to train an initial model object using a machine learning process. It identifies bias in the model and produces an updated version that mitigates this bias through a series of transformations of input variables.

Another notable patent is "System and method for utilizing grouped partial dependence plots and game-theoretic concepts and their extensions in the generation of adverse action reason codes." This framework interprets machine learning models by determining the contribution of groups of input variables to the model's output. It employs clustering algorithms to group input variables and calculates scores for each group, which can then be ranked and combined into a hybrid ranking.

Career Highlights

Raghu has worked with prominent companies such as Discover Financial Services LLC and Capital One Financial Corporation. His experience in these organizations has allowed him to apply his innovative ideas in real-world applications, contributing to advancements in financial technology.

Collaborations

Throughout his career, Raghu has collaborated with talented individuals, including Alexey Miroshnikov and Konstandinos Kotsiopoulos. These collaborations have fostered a creative environment that has led to the development of groundbreaking technologies.

Conclusion

Raghu Kulkarni's contributions to the field of data science and machine learning are noteworthy. His innovative patents and career achievements reflect his dedication to advancing technology and reducing bias in data-driven models.