Renqiang Min

Title: Renqiang Min: Innovator in Statistical Dependencies and Markov Random Fields

Introduction

Renqiang Min is a notable inventor based in Plainsboro, NJ (US). He has made significant contributions to the field of statistical modeling and feature interactions. His work focuses on identifying combinatorial feature interactions and capturing statistical dependencies between categorical variables.

Latest Patents

Renqiang Min holds a patent for a system and method titled "Sparse higher-order Markov random field." This invention provides a framework for identifying combinatorial feature interactions, which includes capturing statistical dependencies between categorical variables. The statistical dependencies are stored in a computer-readable storage medium. A model is selected based on these dependencies using a neighborhood estimation strategy. This strategy generates sets of arbitrarily high-order feature interactions using at least one rule forest and optimizes one or more likelihood functions. A damped mean-field approach is applied to the model to obtain parameters of a Markov random field (MRF). The invention produces a sparse high-order semi-restricted MRF by adding a hidden layer to the MRF. It also models indirect long-range dependencies between feature groups and outputs a combinatorial dependency structure between variables.

Career Highlights

Renqiang Min is currently employed at NEC Laboratories America, Inc. His work at this organization has allowed him to further develop his expertise in statistical modeling and feature interactions.

Collaborations

Renqiang Min collaborates with Yanjun Qi, contributing to advancements in their field through shared knowledge and innovative ideas.

Conclusion

Renqiang Min's contributions to the field of statistical dependencies and Markov random fields highlight his innovative spirit and dedication to advancing technology. His patent reflects a significant step forward in understanding combinatorial feature interactions.