Ensieh Iranmehr is a research engineer at INL. She joined INL Technology Engineering Group in November 2020. She focuses on using machine learning techniques to process sensor signals and images. She works on several projects focusing on analyzing the sensor signals including a TMR-based spintronic sensor, microphone, and Raman spectrum. She also works on some edge computing projects. During her work at INL, she has developed unsupervised algorithms for extracting signal patterns, including patterns based on shape and frequency, as well as supervised algorithms for Raman spectra analysis.
Ensieh has 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 Ph.D. 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 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.
Developing a structural-based local learning rule for classification tasks using ionic liquid space-based reservoir
NEURAL COMPUTING & APPLICATIONS, 2022
ILS-based Reservoir Computing for Handwritten Digits Recognition
8th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 2020
Sound Source Localization in Wide-Range Outdoor Environment Using Distributed Sensor Network
IEEE Sensors Journal 20 (4), 2020
Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space
Frontiers in Neuroscience, 2019
Unsupervised Feature Selection for Phoneme Sound Classification using Particle Swarm Intelligence
5th Iranian joint congress on Fuzzy and Intelligent systems (CFIS 2017), 2017