Ida Mele

Ph.D. Engineering in Computer Science
Researcher at IASI-CNR
Institute for System Analysis and Computer Science  “A. Ruberti”, Rome
National Research Council, Italy

Areas of Expertise:
Data Analytics
Social Network Analysis
Information Retrieval

Short Bio:
Ida Mele is a researcher at the Institute for System Analysis and Computer Science, National Research Council of Italy (IASI-CNR) in Rome, Italy.

Previously, she was a postdoctoral researcher at ISTI-CNR (Pisa, Italy), USI (Lugano, Switzerland), and MPII (Saarbruecken, Germany). She got her Ph.D. in Engineering in Computer Science from Sapienza University of Rome with a Thesis on “Web Usage Mining and its Applications to Web Search and Recommendation”. Part of her Ph.D. research was carried out during internships at Yahoo Research (Barcelona, Spain) and MPII (Saarbruecken, Germany).

Ida Mele served as a teaching assistant for classes of “Data Analytics”, “Databases”, and “Information Retrieval” at USI and “Information Retrieval” at Sapienza University of Rome.

Her research interests are Web Mining, Information Retrieval, Recommendation Systems, and Social Network Analysis.

Sylllabus

Social Data Analytics
The purpose of this course is to provide techniques, methodologies, and tools for examining social data, finding patterns and drawing conclusions from them.

Social data mostly consists of unstructured content (e.g., status updates, tweets, comments) as well as structured networks (e.g., friendship connections). Given its easy access and large size, firms and agencies are eager to discover insightful information from such data with the purpose of better understanding user trends and adjusting their services or marketing strategies accordingly.

This course will cover the most important aspects of social data. It will focus on machine learning techniques with examples of real-life applications. Besides, it will show techniques for graph mining and social network analysis. Due to the huge size of social data, the course will also present scalable techniques that allow managing large-scale datasets.

At the end of the course, the students will be able to conduct a data-driven project, i.e., collecting and processing social data, detecting the best methodology and suitable tools for analyzing such data to derive valuable insight from it.