- Research areas
Artificial Intelligence Researcher with computer science engineering background and working experience focusing on development of cutting-edge software solutions leveraging artificial intelligence.
I graduated in Computer Science Engineering at Tor Vergata University of Rome, specialising in Data Science and Engineering. During my Master’s degree, I acquired knowledge about methodologies on big data management, machine learning and deep learning. In my degree thesis (“A new agent-based NAS (Neural architecture search)”), I focused on NAS techniques capable of automating the network architecture engineering choosing the best configuration.
Before joining LeonardoLabs, I worked as a Junior Data Scientist in a research foundation in Turin, where I focused on natural language processing (NLP) techniques applied to two European projects. This work experience allowed me to face NLP multilingual problems, thus starting a research work described in the paper “Multilingual Text Classification form Twitter During Emergencies” presented at 2021 ICCE conference .
At LeonardoLabs I am involved in several artificial intelligence research areas, ranging from building knowledge graphs exploiting NLP techniques to applying generative adversarial networks (GANs) for face enhancement problems.
Generative Adversarial Networks (GANs):
– GANs for face enhancement\ problem
Natural Language Processing:
– Multilingual language models
– Creation of knowledge graphs from unstructured data (information extraction using NLP) and structured data (data integration from existing sources)
– Question answering over knowledge graphs
– Relation extraction
 S. Piscitelli, E. Arnaudo and C. Rossi, "Multilingual Text Classification from Twitter during Emergencies," 2021 IEEE International Conference on Consumer Electronics (ICCE), 2021, pp. 1-6, doi: 10.1109/ICCE50685.2021.9427581.