Company Filing History:
Years Active: 2021
Title: Janis Timosenko: Innovator in Machine Learning-Based Material Analysis
Introduction
Janis Timosenko is a prominent inventor based in Upton, NY (US). She has made significant contributions to the field of material characterization through her innovative use of machine learning techniques. Her work focuses on the intersection of artificial intelligence and material science, paving the way for advancements in how materials are analyzed and understood.
Latest Patents
Janis Timosenko holds a patent for a "System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra." This patent describes a method that utilizes supervised machine learning to analyze spectrum data from materials. The process involves inputting spectrum data into a neural network, which has been trained to identify specific features of the material. The neural network processes the data and outputs values that correspond to the specified features. The training set for the neural network includes x-ray absorption spectroscopy data and electron energy loss spectra (EELS) data, enhancing the accuracy of the analysis.
Career Highlights
Janis Timosenko is affiliated with the State University of New York, where she contributes to research and development in material science. Her innovative approach to using machine learning in material characterization has garnered attention in academic and industrial circles.
Collaborations
Janis has collaborated with her coworker, Anatoly Frenkel, to further explore the applications of machine learning in material analysis. Their joint efforts aim to enhance the understanding of material properties through advanced analytical techniques.
Conclusion
Janis Timosenko's work exemplifies the potential of integrating machine learning with material science. Her contributions are paving the way for future innovations in the field, making significant strides in how materials are characterized and understood.