Xiaomei Liu

Title: Innovations of Xiaomei Liu

Introduction

Xiaomei Liu is an accomplished inventor based in Cary, NC (US). She has made significant contributions to the field of machine learning through her innovative patent. Her work focuses on enhancing predictive model selection, which is crucial for various applications in data analysis and artificial intelligence.

Latest Patents

Xiaomei Liu holds a patent titled "Analytic system for machine learning prediction model selection." This patent describes a sophisticated method for selecting an assessment dataset from an input dataset using a first stratified sampling process based on the value of an event assessment variable. The remaining input dataset is then allocated to a training/validation dataset, which is further partitioned into an oversampled training/validation dataset. This process utilizes an oversampling technique based on a predefined value of the event assessment variable. The patent outlines how a validation sample is selected from the oversampled dataset, ensuring that the validation and training samples are mutually exclusive. Ultimately, a predictive type model is trained using the selected training sample, and multiple predictive models are trained, validated, and scored to identify the best predictive model.

Career Highlights

Xiaomei Liu is currently employed at SAS Institute Inc., a leading company in analytics software and solutions. Her role involves leveraging her expertise in machine learning to develop innovative solutions that enhance predictive analytics capabilities.

Collaborations

Throughout her career, Xiaomei has collaborated with notable colleagues, including Yongjin Ma and Xinmin Wu. These collaborations have contributed to the advancement of research and development in machine learning and predictive modeling.

Conclusion

Xiaomei Liu's contributions to the field of machine learning through her patent and work at SAS Institute Inc. highlight her innovative spirit and dedication to advancing technology. Her work continues to influence the development of predictive analytics, making her a significant figure in her field.