Ensieh Iranmehr is a staff researcher at INL, where she has been contributing since November 2020. Her research focuses on applying machine learning to enhance the applications of magnetoresistive sensors and advance neuromorphic computation modeling. At INL, Ensieh has contributed to multiple projects involving signal processing and machine learning techniques for spintronic sensors and Raman spectroscopy. She has developed innovative unsupervised algorithms for signal pattern extraction, focusing on features such as shape and frequency, as well as advanced methods for automated Raman spectral analysis to achieve precise material identification and quantification. Her expertise also extends to edge computing, where she has contributed to solutions for image processing and computer vision.

Ensieh has a Ph.D. in Digital Systems from the electrical engineering department of the Sharif University of Technology, Tehran, Iran. Her works revolve around artificial neural networks, machine learning, neuromorphic engineering, and digital systems. For her PhD project, she has proposed a new neuromorphic structure of a spiking neural network inspired by biological studies called the ILS-based Reservoir Network. She also has an MSc in Digital Electronic Engineering from the electrical engineering department of the Amirkabir University of Technology, Tehran, Iran. Her MSc project revolved around artificial neural networks, image processing, and parallel processing."