Kyiv, Ukraine

Anastasiia Tamazlykar

USPTO Granted Patents = 1 

Average Co-Inventor Count = 7.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: Innovations by Anastasiia Tamazlykar in Machine Learning Interpretation.

Introduction

Anastasiia Tamazlykar is an accomplished inventor based in Kyiv, Ukraine. She has made significant contributions to the field of machine learning, particularly in the interpretation of model predictions. Her innovative approach has garnered attention in the tech community.

Latest Patents

Anastasiia holds a patent for "Systems and methods for machine learning model interpretation." This patent describes systems and methods for interpreting machine learning model predictions. An example method includes providing a machine learning model configured to receive a plurality of features as input and provide a prediction as output. The plurality of features includes an engineered feature that combines two or more parent features. The method also involves calculating a Shapley value for each feature in the plurality of features and allocating a respective portion of the Shapley value for the engineered feature to each of the two or more parent features. She has 1 patent to her name.

Career Highlights

Anastasiia is currently employed at DataRobot, Inc., where she continues to develop her expertise in machine learning and artificial intelligence. Her work focuses on enhancing the interpretability of machine learning models, which is crucial for their adoption in various industries.

Collaborations

Anastasiia has collaborated with notable professionals in her field, including Mark Benjamin Romanowsky and Jared Bowns. These collaborations have further enriched her work and contributed to advancements in machine learning interpretation.

Conclusion

Anastasiia Tamazlykar is a pioneering inventor whose work in machine learning model interpretation is shaping the future of artificial intelligence. Her contributions are vital for making machine learning more accessible and understandable.

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