Klaus Thielen

Title: Klaus Thielen: Innovator in Predictive Modeling

Introduction

Klaus Thielen is a notable inventor based in Munich, Germany. He has made significant contributions to the field of predictive modeling, holding a total of 5 patents. His work focuses on improving machine learning systems through innovative methods that address data challenges.

Latest Patents

One of Klaus Thielen's latest patents is titled "Method and system for adaptively imputing sparse and missing data for predictive models." This patent describes an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data. Another significant patent is "Method and system for adaptively removing outliers from data used in training of predictive models." This patent outlines an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental event conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

Career Highlights

Klaus Thielen is currently employed at Oracle International Corporation, where he continues to develop innovative solutions in data science and predictive analytics. His expertise in handling complex data issues has made him a valuable asset to his team.

Collaborations

Klaus has collaborated with several talented individuals in his field, including Michael Zoll and Yaser I Suleiman. Their combined efforts contribute to advancing the technology and methodologies in predictive modeling.

Conclusion

Klaus Thielen's work exemplifies the importance of innovation in addressing data challenges within machine learning. His patents reflect a commitment to enhancing predictive models, making significant strides in the field.