Zahra is a third-year Ph.D. student at the University of Twente, Data Management & Biometrics (DMB) group. During her Ph.D., she focuses on Deep Learning and, particularly, sparse neural networks. She seeks to develop algorithms to solve different tasks efficiently in terms of computational costs and data requirements.
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Artificial neural networks (ANNs) have gained huge attention over the last few years due to their promising results in a large variety of tasks. However, deep neural networks (DNNs) require plenty of annotated data and are recognized as being computationally demanding. Therefore, deep learning models are not well-suited to applications with limited computational resources, battery life, and labeled instances. Current solutions to reduce computation and annotation costs mostly focus on inference efficiency, while being resource-intensive during training. Zahra aims to address these challenges by developing cost-effective neural networks that can achieve decent performance on various complex tasks using minimum computational resources and labeled samples, both during training and inference of the network.
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