As already mentioned in the “about me” section of this website, my research interests are rooted in high-performance and highly-distributed systems. More specifically, I focused my research activities on the models, approaches and algorithms to ease the task of efficiently using parallel and distributed systems, from different viewpoints.

In the following, you can find some topics, organized in areas, on which it is possible to develop a thesis. Some of those are also published on the Thesis boards (for the BSc and the MSc) provided by the Department of Computer Science of the University of Pisa.

Parallel and Distributed platforms for AI and/or BigData processing

AI technologies represent a real revolution in the way problems and tasks are nowadays faced. By relying on the availability of a massive amount of data, Machine Learning technologies allow deep levels of automation, making it possible for humans to let machines complete tasks that were not possible to delegate before. To make this possible it is fundamental to extract from the data their intrinsic training value. As a matter of fact, this requires a huge amount of computing capacity, hardly possible by relying only on traditional computing architectures and devices. There are a number of proposals in the research community of parallel and distributed platforms to overcome such a limitation.

Thesis projects:

• Optimization of the data exchange in deep learning frameworks for distributed AI workloads (available an in-depth description here)
• Exploitation of FPGAs for AI applications in conversational agents
• Improving Big Graph partitioning in GraphX, the use case of Pinterest pins and boards