Thesis Proposals

MAster Degree in Computer science - Data science - CYBERSECURITY - Computational Finance

• Hypernetworks for graph-structured data 

Graph-structured data is ubiquitous in various domains, including social networks, biological networks, and knowledge graphs. Graphs are dynamic, heterogeneous, and can vary significantly in size, making traditional neural network architectures less suitable for effective learning. Hypernetworks, a class of neural networks that generate weights for other networks, have shown promise in capturing complex patterns in high-dimensional data. Hypernetworks offer a unique approach by generating weights dynamically, potentially addressing these challenges. However, their application to graph-structured data is an emerging area of research that requires in-depth exploration. The goal of the thesis is to explore the application of Hypernetworks to graph-structured data, aiming to develop innovative techniques that enhance scalability, flexibility, and performance in graph learning tasks.

• Deep Learning Models for Neuroimaging Analysis 

Neuroimaging plays a crucial role in understanding the structure, function, and disorders of the brain. With the advent of Deep Learning techniques, there has been a paradigm shift in the field of neuroimaging analysis. Deep Learning models have demonstrated exceptional capabilities in extracting intricate patterns from neuroimaging data, leading to more accurate diagnoses and personalized treatment strategies. This thesis project aims to explore the application of Deep Learning models in neuroimaging analysis, focusing on developing models that can handle diverse imaging modalities, interpret complex brain structures, and provide reliable diagnostic insights. 

• Forecasting Stock Prices Using Deep Learning

Deep learning, particularly Recurrent Neural Networks (RNNs), has emerged as a potent tool for various predictive tasks, yet its application to stock price prediction presents unique challenges due to the intricate nature of financial markets. While existing deep learning methods exhibit promise, their effectiveness in capturing the nuanced patterns of stock price movements requires further exploration. This thesis aims to investigate the application of RNNs and other deep learning techniques in stock price prediction and explore novel approaches to enhance their predictive accuracy.