Open to embarking on new challenging projects in NLP and cybersecurity. Feel free to reach out if you think I can help you :)

Also available via Toptal.

I tackled a multitude of ML problems in the intersection of NLP and cybersecurity.

I worked on topics such as website categorization (using multilingual text and images), DNS tunneling detection, and IoT security. Heavy emphasis on developing production-ready ML models, and following MLOps best practices. Using state-of-the-art Machine Learning models (e.g., Transformers).

I was part of the BigDAMA project, a fundamental research project focused on big data analytics for network traffic monitoring and analysis.

My research topics are mostly related to how to represent network traffic for detecting attacks at the network level (i.e., which features to use). Experimented with both classical features (what have people been using and why?), and feature learning approaches using Deep Learning techniques, and in particular representing traffic in 2-dimensional spaces.

I was part of the SUMMA project, an H2020 project for media monitoring with multiple industry and academic international partners.

I specifically tackled the problem of named entity recognition in which, given a text with some entity mentions (e.g., David and Victoria), the goal is to find the corresponding entities in Wikipedia (e.g., David Beckham and Victoria Beckham). We used both classical methods (such as SVM) and modern Deep Learning approaches.