Ciao! I am
- Short bio
I am a CSEF Fellow and a Postdoctoral Researcher at the Department of Economics and Statistics, University of Naples Federico II (Italy). I hold a Ph.D. in Economics, and my main research interests are in Machine Learning and Statistics.
In these years I've been working on two connected lines of research. The first one is on methodological statistics, with a particular focus on model-based clustering and criteria for selecting optimal clustering solutions. The second line of research is devoted to application of supervised and unsupervised learning methods to general problems in Economics, using state-of-the-art statistical methods (involving: standard ML tools, deep learning, NLP, and computer vision) to exploit new sources of data, like images and text.
I enjoy coding my own solutions, and I am fluent in several programming languages. Here is my current top-three: C, Python, R.
- Quadratic-scoring software · w/ P. Coretto
- JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality. SSRN Working paper · w/ M. Pagano, A. Scognamiglio, J. Tåg
- Newspaper articles digitalization for database construction · w/ M. Vasca, G. de Blasio, R. Nisticò
- Folklore studies · w/ G. Immordino, F. F. Russo
- (2023) Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score. Journal of Multivariate Analysis · w/ Pietro Coretto
- (2021) Illicit drugs seizures in 2013–2018 and characteristics of the illicit market within the Neapolitan area. Forensic Science International · w/ A. Silvestre, P. Basilicata, R. Guadagni, A. Simonelli, M. Pieri
A short, introductory course in Python programming. Level: undergraduate, 6 lectures, ~2h/lecture. The course is modeled on the Python tutorial, with a tilt toward data analysis and economics. The material is in italian, and currently available on Moodle Unina (I plan to make it available on git), and it includes:
- Slide with programming concepts
- Exercises and solutions
- Scripts and advanced solved exercises
Tools for Data Analysis
The course introduces elements of programming and statistical methods for data analysis and data science. It reviews and uses R, and Python programming languages as well as shell scripting, to interact with data (visualization, manipulation), automate tasks (web scraping, file management) and deploy machine learning methods. The course is hands-on: students get to work on mini-projects and practical exercises throughout the lectures; theory of the methods is touched upon and references for self-study are provided. The course is aimed at students willing to acquire programming skills to work with data (ideally, they have already taken statistics courses).