Company Filing History:
Years Active: 2024-2025
Title: Ruochen Zha: Innovator in Machine Learning Model Development
Introduction
Ruochen Zha is a prominent inventor based in Pasadena, CA (US). He has made significant contributions to the field of machine learning through his innovative patent. His work focuses on developing automated systems that enhance the efficiency and effectiveness of machine learning model creation and refinement.
Latest Patents
Ruochen Zha holds a patent for "Automated systems for machine learning model development, analysis, and refinement." This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features. Ruochen Zha has 1 patent to his name.
Career Highlights
Ruochen Zha is currently employed at ZestFinance, Inc., where he continues to push the boundaries of machine learning technology. His work has been instrumental in developing systems that streamline the process of creating and testing machine learning models.
Collaborations
Ruochen has collaborated with notable colleagues, including David Sheehan and Siavash Yasini. Their combined expertise contributes to the innovative projects at ZestFinance, Inc.
Conclusion
Ruochen Zha is a key figure in the advancement of machine learning technologies, with a focus on automated systems for model development. His contributions are shaping the future of how machine learning models are created and tested.