Irina Galiash

Title: Irina Galiash: Innovator in Neural Network Training for NMR Measurements

Introduction

Irina Galiash is a prominent inventor based in Adliswil, Switzerland. She has made significant contributions to the field of neural networks, particularly in the analysis of nuclear magnetic resonance (NMR) measurements. Her innovative approach has the potential to enhance the accuracy and efficiency of signal identification in complex NMR spectra.

Latest Patents

Irina Galiash holds a patent for a system and method designed to provide provisional training data that enables a neural network to identify signals in NMR measurements. This patent, which is a crucial advancement in the field, outlines a method for training a neural network using a variety of computed NMR raw spectra. Each raw spectrum is associated with a different NMR active molecule that has a known number of protons. The process involves broadening the peaks of the raw spectra to generate broadened spectra, computing their integral functions, and identifying signal intervals where the integral function increases in multiples of the value associated with a single proton. The resulting spectra, along with their associated labels, serve as the training data set for the neural network.

Career Highlights

Irina Galiash is currently employed at Bruker Switzerland AG, where she continues to develop her innovative ideas and contribute to advancements in NMR technology. Her work is characterized by a strong focus on integrating machine learning techniques with traditional scientific methods to improve data analysis processes.

Collaborations

Irina collaborates with notable colleagues, including Christine Bolliger and Oliver Horlacher, who contribute to her research and development efforts. Their teamwork fosters a collaborative environment that enhances the innovation process.

Conclusion

Irina Galiash is a trailblazer in the field of neural network training for NMR measurements, with her patent showcasing her innovative approach to signal analysis. Her contributions are paving the way for future advancements in this critical area of research.