Dr. Hasan Al-Marzouqi received his Bachelor’s degree (with honors) and his M.Sc. degree, both in Electrical and Computer Engineering from Vanderbilt University, Nashville, Tennessee, in 2004 and 2006, respectively. He received his Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology in 2014.
Dr. Al-Marzouqi is a Senior Member of IEEE and a member of the IEEE Signal Processing Society. His current research interests include deep learning, artificial intelligence, digital rock physics, and bioinformatics.
Sedenion Valued Neural Networks
In this project, a sedenion-valued neural network (Mc-SVNN) and its learning algorithm are proposed and used to solve several multi-time-steps and multi-task forecasting problems. The Mc-SVNN contains two components: a sedenion-valued neural network that represents the cognitive component, and a metacognitive component, which serves to self-regulate the learning algorithm. At each epoch, the metacognitive component decides what, how, and when learning occurs. The algorithm deletes unnecessary samples and stores only those that are used. This decision is determined by the sedenion magnitude and the 15 sedenion phases. The Mc-SVNN is applied to four real-world forecasting problems: USD-to-euro currency exchange rate forecasting, the sunspot number time series, power demand forecasting, traffic forecasting, and daily temperature prediction in Abu Dhabi. Compared to existing methods, the Mc-SVNN demonstrates superior performance in time-series forecasting while using a smaller number of parameters
Forecasting of Gridded Geo-Spatial Processes
We developed systems for solving several space-time forecasting problems like weather forecasting and traffic forecasting. Proposed systems incorporate ideas from multi-scale segmentation networks like U-Nets and achieved advanced positions in reputable international research competitions. For example, we achieved the 4th position in the NeurIPS 2021 Traffic4cast challenge and the 4th position in the ACM CIKM 2021 Weather4cast competition.
Semantic Labelling of Remote Sensing Images
Semantic segmentation necessitates approaches that learn high-level characteristics while dealing with enormous amounts of data. Convolutional neural networks (CNNs) can learn unique and adaptive features to achieve this aim. However, these networks cannot analyze an entire scene efficiently due to the large size and high spatial resolution of remote sensing images. Recently, deep transformers have proven their capability to record global interactions between different objects in the image. In this project, we propose a new segmentation model that combines convolutional neural networks with transformers and show that this mixture of local and global feature extraction techniques provides significant advantages in remote sensing segmentation. In addition, the proposed model includes two fusion layers designed to efficiently represent multi-modal inputs and output of the network. The input fusion layer extracts feature maps summarizing the relationship between image content and elevation maps (DSM). The output fusion layer uses a novel multi-task segmentation strategy where class labels are identified using class-specific feature extraction layers and loss functions. Finally, a fast-marching method is used to convert unidentified class labels to their closest known neighbors. Our results demonstrate that the proposed methodology improves segmentation accuracy compared to state-of-the-art techniques.
Several Ph.D. projects and a Post-doc position are available