Location History:
- München, DE (2015)
- Cambridge, MA (US) (2018)
Company Filing History:
Years Active: 2015-2018
Title: Maximilian Nickel: Innovator in Data Processing and Machine Learning
Introduction
Maximilian Nickel is a prominent inventor based in Cambridge, MA, known for his contributions to the fields of data processing and machine learning. With a total of 2 patents, he has made significant strides in optimizing methods for deriving predictions from data and calculating relation indicators between entities.
Latest Patents
One of his latest patents involves a method for calculating a relation indicator for a relation between entities based on an optimization procedure. This innovative method combines the strong relational learning ability and scalability of the RESCAL model with the linear regression model. It is designed to determine relations between various objects, such as entries in a database, medical treatments, and production processes, particularly in the context of the Internet of Things.
Another notable patent focuses on deriving predictions from existing data using information extraction and machine learning. This system allows for the independent optimization of both approaches and can also incorporate deductive reasoning. The combined functionalities can process various sets of data, leading to significantly improved results applicable across multiple technical fields, including medical and genetic research.
Career Highlights
Maximilian Nickel is currently associated with Siemens Aktiengesellschaft, where he continues to innovate and contribute to advancements in technology. His work has positioned him as a key figure in the development of systems that enhance data mining and processing capabilities.
Collaborations
He has collaborated with notable professionals in his field, including Volker Tresp and Xueyan Jiang, further enriching his research and development efforts.
Conclusion
Maximilian Nickel's work exemplifies the intersection of innovation and technology, particularly in the realms of data processing and machine learning. His patents reflect a commitment to improving how we understand and utilize data in various applications.