This inventor holds 1 USPTO granted patent. Top assignees: Arizona State University, Mayo Foundation for Medical Education and Research. Active years: 2024.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:


Years Active: 2024
Title: Hyunsoo Yoon: Innovator in Machine Learning and MRI Technology
Introduction
Hyunsoo Yoon is a prominent inventor based in Tempe, AZ (US). He has made significant contributions to the field of medical imaging through his innovative approaches that integrate machine learning with traditional mechanistic models. His work focuses on enhancing the accuracy and effectiveness of biological feature mapping using advanced imaging techniques.
Latest Patents
Hyunsoo Yoon holds 1 patent for his invention titled "Methods for using machine learning and mechanistic models for biological feature mapping with multiparametric MRI." This patent describes systems and methods for generating and implementing a hybrid model that combines machine learning and mechanistic approaches. The goal is to produce biological feature maps based on multiparametric magnetic resonance imaging data. The hybrid model is capable of learning individual-specific relationships between imaging features and biological features, such as tumor cell density.
Career Highlights
Throughout his career, Hyunsoo Yoon has worked with esteemed institutions, including Arizona State University and the Mayo Foundation for Medical Education and Research. His experience in these organizations has allowed him to develop and refine his innovative techniques in medical imaging and machine learning.
Collaborations
Hyunsoo has collaborated with notable colleagues, including Nathan Gaw and Andrea Hawkins-Daarud. These partnerships have contributed to the advancement of his research and the successful implementation of his patented technologies.
Conclusion
Hyunsoo Yoon's work exemplifies the intersection of technology and medicine, showcasing how innovations in machine learning can enhance biological feature mapping. His contributions are paving the way for more accurate medical diagnoses and treatments.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile