Company Filing History:
Years Active: 2023
Title: The Innovations of Evan Eino Estola
Introduction
Evan Eino Estola is an accomplished inventor based in Brooklyn, NY. He has made significant contributions to the field of technology, particularly in the area of bias monitoring in machine learning models. With a total of 2 patents, Estola is recognized for his innovative approaches to ensuring fairness in data-driven systems.
Latest Patents
Estola's latest patents focus on systems and methods for automatic bias monitoring of cohort models and the un-deployment of biased models. These patents disclose methods for monitoring models for bias, which are crucial in today's data-centric world. In one implementation, a system is designed to automatically assess a deployed model for the selection of a cohort. This system includes a processing device programmed to apply the deployed model to data representing a first plurality of individuals, which includes at least one characteristic of these individuals. Based on this application, a subset of individuals is selected as a cohort. The system then receives data representing a second plurality of individuals labeled as within the cohort and compares the selected subset with this second group along the same characteristic. Finally, it determines whether the comparison results in a significant difference, exceeding a predetermined threshold.
Career Highlights
Evan Eino Estola is currently employed at Flatiron Health, Inc., where he continues to develop innovative solutions in healthcare technology. His work focuses on improving the accuracy and fairness of machine learning applications in medical settings.
Collaborations
Estola collaborates with notable colleagues, including Benjamin Edward Birnbaum and Joshua Daniel Haimson. Their combined expertise contributes to the advancement of technology in healthcare and beyond.
Conclusion
Evan Eino Estola is a pioneering inventor whose work in bias monitoring systems is shaping the future of technology. His contributions are vital for ensuring fairness in machine learning applications, making a significant impact in the industry.