Location History:
- Sunnyvale, CA (US) (2022)
- Cupertino, CA (US) (2021 - 2024)
Company Filing History:
Years Active: 2021-2025
Title: Mridul Jain: Innovator in Distributed Machine Learning
Introduction
Mridul Jain is a prominent inventor based in Cupertino, California, known for his significant contributions to the field of distributed machine learning. With a total of 13 patents to his name, Jain has made remarkable strides in enhancing the efficiency and effectiveness of machine learning models.
Latest Patents
Among his latest patents, Jain has developed a "Method and system for distributed deep machine learning." This invention focuses on estimating parameters associated with machine learning models across a cluster of nodes. The process involves dividing a set of data into sub-sets, which are then allocated to corresponding nodes for parameter estimation. The estimated values from these nodes are aggregated to refine the machine learning model.
Another notable patent is "Traversing an adjacency list on distributed processors." This invention describes a distributed system that utilizes multiple processors to execute iterations until a stopping condition is met. The system processes input nodes, determines output nodes using adjacency rows, and updates input nodes for subsequent iterations, thereby optimizing the overall computational process.
Career Highlights
Mridul Jain has worked with notable companies such as Walmart Apollo, LLC and Verizon Media Inc. His experience in these organizations has allowed him to apply his innovative ideas in real-world applications, further solidifying his reputation as a leading inventor in his field.
Collaborations
Throughout his career, Jain has collaborated with talented individuals, including Saigopal Thota and Gajendra Alias Nishad Kamat. These collaborations have contributed to the development of cutting-edge technologies in machine learning.
Conclusion
Mridul Jain's work in distributed machine learning exemplifies the impact of innovation on technology. His patents not only advance the field but also pave the way for future developments in machine learning applications.