Company Filing History:
Years Active: 2017-2025
Title: Fan Zhang: Innovator in Machine Learning and Load Balancing
Introduction
Fan Zhang is a notable inventor based in Bellevue, WA, known for his substantial contributions to the fields of machine learning and networking technologies. With six patents to his name, he has made significant strides in optimizing inference processes and load balancing techniques.
Latest Patents
Among his latest innovations is a patent for performing inference using an adaptive, hybrid local/remote technique. This invention describes a hybrid inference facility that processes data items by forwarding them to a remote server while also conducting local machine learning analyses. The process generates inference results that are aggregated and managed through a queue system, ensuring efficient data handling and accuracy in results.
Another noteworthy patent is for a distributed load balancer, which employs a per-flow hashed multipath routing technique. This technology allows for smart routing of packet flows to various load balancer nodes. It enhances performance by enabling server nodes to effectively manage connection requests based on current load metrics, thereby optimizing resource utilization.
Career Highlights
Fan has had an impactful career, having worked with leading companies such as Amazon Technologies, Inc. and Echonous, Inc. His roles at these organizations have contributed to cutting-edge advancements in technology, helping to shape contemporary approaches in machine learning and network management.
Collaborations
Throughout his career, Fan has collaborated with notable professionals, including James Christopher Sorenson and Douglas Stewart Laurence. These partnerships have fostered an environment of innovation and creative problem-solving, allowing for the development of transformative technologies.
Conclusion
Fan Zhang continues to be an influential figure in technology, with his patents reflecting a keen understanding of machine learning and infrastructure management. His ongoing work and collaborations promise to drive further innovations in the industry, making significant impacts on how data processing and load balancing are approached in modern applications.