Manlius, NY, United States of America

Leann Thayaparan


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: Leann Thayaparan: Innovator in Demand Modeling

Introduction

Leann Thayaparan is a notable inventor based in Manlius, NY (US), recognized for her contributions to demand modeling through innovative machine learning techniques. With a total of two patents to her name, she has made significant strides in optimizing demand prediction for retail items.

Latest Patents

Thayaparan's latest patents include the "Optimized Tree Ensemble Based Demand Model." This invention focuses on generating and training an optimized demand model to predict the demand for various items. The model utilizes a plurality of trees, each containing levels of splits and nodes that correspond to demand features influencing item demand. The process involves selecting the most impactful demand features and optimizing them to enhance the model's accuracy. Another patent under her name is also titled "Optimized Tree Ensemble Based Demand Model," which emphasizes training a tree ensemble machine learning model. This model comprises multiple trees that are trained using upper bounds for each tree, ultimately generating an optimized demand model when all leaf nodes are reached.

Career Highlights

Throughout her career, Leann Thayaparan has worked with prestigious organizations, including Oracle International Corporation and the Massachusetts Institute of Technology. Her experience in these institutions has allowed her to refine her skills and contribute to groundbreaking advancements in demand modeling.

Collaborations

Thayaparan has collaborated with talented individuals such as Kiran V Panchamgam and Setareh Borjian, enhancing her work through shared expertise and innovative ideas.

Conclusion

Leann Thayaparan's work in demand modeling showcases her innovative spirit and dedication to advancing technology in retail. Her patents reflect her commitment to improving predictive accuracy through machine learning, making her a significant figure in her field.

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