I am an Assistant Professor in Computer Engineering specializing in AI, TinyML, and Deep Learning. I have a strong background in computer vision, game development, and research in neural networks. My work focuses on advancing technology in areas like IoT, healthcare, and education.
This paper proposes a novel idea of combining filter pruning with tensor decomposition to reduce the computational complexity of deep neural networks.
This paper proposes a storage-efficient ensemble classification to overcome the low inference accuracy of binary neural networks (BNNs).
This paper proposes a novel two-stage framework for emotion recognition using EEG data that outperforms state-of-the-art models while keeping the model size small and computationally efficient.
This Master thesis focuses on integrating low-cost EEG headsets with IoT devices to control electronic devices through brain signals.
This dissertation explores the application of neural networks in embedded systems, highlighting techniques for optimizing performance and efficiency in resource-constrained environments.