Company Filing History:
Years Active: 2025
Title: Sumitra Ganesh: Innovator in Data Querying and Reinforcement Learning
Introduction
Sumitra Ganesh is a notable inventor based in Short Hills, NJ (US). He has made significant contributions to the fields of data querying and reinforcement learning, holding a total of 3 patents. His innovative work focuses on enhancing the efficiency and accuracy of data retrieval processes.
Latest Patents
One of Sumitra's latest patents is titled "Method and system for verification with large language models for data querying." This invention provides a method for verifying structured query language (SQL) queries. The process includes receiving a request to retrieve data from a database, identifying the intention behind the request, and generating an SQL query. It also involves predicting the output of the query and determining if it aligns with the original intention. If there is a mismatch, a second SQL query is generated to ensure accurate data retrieval.
Another significant patent is "Systems and methods for risk-sensitive reinforcement learning." This invention outlines a method for training a risk-sensitive reinforcement learning policy. It includes receiving training data from a data source, along with a training budget that comprises episodes, a risk aversion coefficient, and an end state. The method calculates a correction factor to minimize stochasticity based on the risk aversion coefficient.
Career Highlights
Sumitra Ganesh is currently employed at JPMorgan Chase Bank, N.A. His role at the bank allows him to apply his innovative ideas in a practical setting, contributing to advancements in financial technology and data management.
Collaborations
Some of Sumitra's coworkers include Nelson Vadori and Maria Manuela Veloso, who collaborate with him on various projects within the bank.
Conclusion
Sumitra Ganesh's contributions to data querying and reinforcement learning demonstrate his commitment to innovation and excellence in technology. His patents reflect a deep understanding of complex systems and a drive to improve data management processes.