San Diego, CA, United States of America

Shreya Kadambi

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Shreya Kadambi: Innovator in Location Estimation Technology

Introduction

Shreya Kadambi is a prominent inventor based in San Diego, California. She has made significant contributions to the field of location estimation and mapping through her innovative work at Qualcomm Incorporated. Her research focuses on utilizing machine learning models to enhance the accuracy of device location predictions in spatial environments.

Latest Patents

Shreya Kadambi holds a patent for "Unsupervised location estimation and mapping based on multipath measurements." This patent outlines methods, apparatus, and systems for predicting a device's location using a machine learning model. The process involves measuring multiple signals received from a network entity, generating a channel state information (CSI) measurement that includes a multipath component, and identifying the positions of anchors in a spatial environment. The device's location is then estimated based on these identified positions.

Career Highlights

Throughout her career, Shreya has demonstrated a strong commitment to advancing technology in the field of location estimation. Her work at Qualcomm has positioned her as a leader in this innovative area, contributing to the development of more accurate and efficient location-based services.

Collaborations

Shreya has collaborated with notable colleagues, including Arash Behboodi and Joseph Binamira Soriaga, to further enhance her research and development efforts. These collaborations have allowed her to leverage diverse expertise and drive innovation in her projects.

Conclusion

Shreya Kadambi's contributions to location estimation technology exemplify her dedication to innovation and excellence. Her patent and collaborative efforts at Qualcomm Incorporated highlight her role as a key player in advancing machine learning applications in spatial environments.

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