Yan Wang

Title: Innovations by Yan Wang

Introduction

Yan Wang is an accomplished inventor based in Athens, GA (US). He has made significant contributions to the field of predictive modeling, particularly in risk assessment technologies. With a total of 2 patents to his name, his work is recognized for its innovative approach to utilizing time series data.

Latest Patents

Yan Wang's latest patents focus on techniques for prediction models using time series data. These patents involve a lagged prediction model trained for risk assessment and other purposes. For instance, a risk assessment computing system receives a risk assessment query for a target entity and provides an input predictor record for the target entity to a lagged prediction model. The input predictor record includes a first group of lagged values from a first time-series attribute associated with the target entity. The lagged prediction model is trained by implementing a group feature selection technique configured to select the first time-series attribute as input and to deselect a second time-series attribute associated with the target entity. The risk assessment computing system computes an output risk indicator from the input predictor record and transmits the output risk indicator to a remote computing system. The output risk indicator can be used to control access by the target entity to one or more interactive computing environments.

Career Highlights

Yan Wang is currently employed at Equifax Inc., where he continues to develop innovative solutions in the field of risk assessment and predictive modeling. His expertise in this area has positioned him as a valuable asset to his organization.

Collaborations

Yan collaborates with Xiao Huang, who is also a key contributor in their projects. Together, they work on advancing the methodologies and applications of predictive modeling.

Conclusion

Yan Wang's contributions to predictive modeling and risk assessment highlight his innovative spirit and dedication to advancing technology. His work continues to influence the field and offers valuable insights into the use of time series data for risk assessment.