Santa Barbara, CA, United States of America

Filip Jankovic

USPTO Granted Patents = 1 

Average Co-Inventor Count = 5.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Filip Jankovic: Innovator in Self-Supervised Learning

Introduction

Filip Jankovic is a notable inventor based in Santa Barbara, California. He has made significant contributions to the field of machine learning, particularly in the area of self-supervised learning. His innovative approach addresses the challenges posed by missing data in datasets, which is a common issue in various applications.

Latest Patents

Jankovic holds a patent for "Systems and methods for self-supervised learning based on naturally-occurring patterns of missing data." This patent describes a method that involves accessing a set of data records for multiple users, which represent physical statistics measured over time. The method includes generating masked data records by masking a subset of the data according to a pattern of natural missingness. Furthermore, it involves generating learned representations from these masked records and fine-tuning a machine learning model to perform downstream tasks.

Career Highlights

Filip Jankovic is currently employed at Evidation Health, Inc., where he applies his expertise in machine learning to develop innovative solutions. His work focuses on enhancing the capabilities of machine learning systems to better handle incomplete data, thereby improving the accuracy and reliability of predictive models.

Collaborations

Jankovic collaborates with talented professionals in his field, including Luca Foschini and Raghunandan Melkote Kainkaryam. These collaborations foster a creative environment that drives innovation and leads to the development of cutting-edge technologies.

Conclusion

Filip Jankovic is a pioneering inventor whose work in self-supervised learning is shaping the future of machine learning. His contributions are vital in addressing the complexities of missing data, making significant strides in the field.

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