Summary

Ph.D. student in Computer Science at the University of Illinois Chicago, specializing in causality and graph-based learning. His research focuses on modeling causal relationships in relational data using graph representations. Lead author of a paper accepted at UAI 2025 introducing RelFCI—an extension of the FCI algorithm that accounts for latent confounders and non-i.i.d. structures.

Education

• Ph.D. in Computer Science
  University of Illinois Chicago

  GPA:

  Courses: Causal Inference and Learning
• M.S. in Computer Science

  2024-12-31
  University of Illinois Chicago

  GPA: 4.0

  Courses: Big Data Mining, Machine Learning, Computer Security, Natural Language Processing
• M.S. in Computer Science and Engineering
  Politecnico di Milano

  GPA: 3.82

  Courses: Data Mining, Information Systems, Software Engineering 2
• B.S. in Engineering of Computing Systems

  2022-09-01
  Politecnico di Milano

  GPA: 3.76

  Courses: Algorithms, Computer Architectures, Bioinformatics

Work Experience

• Teaching Assistant

  2025-01-01 - 2025-05-01
  University of Illinois Chicago

  Teaching Assistant for Introduction to Data Science (CS418).
• Graduate Hourly Assistant

  2024-01-01 - 2025-12-31
  University of Illinois Chicago

  Developing an algorithm for causal discovery on relational data using graph representations.
• Visiting Researcher

  2023-08-01
  Northeastern University - Brigham and Women’s Hospital

  Master’s thesis on data engineering and representation learning in the biomedical domain.
• Research Scholar

  2020-09-01
  NECSTLab - Politecnico di Milano

  Contributed to multiple research projects in computing and engineering.

Skills

Publications

• Relational Causal Discovery with Latent Confounders
  UAI 2025

  Introduced RelFCI, an algorithm extending FCI to relational data with latent confounders and non-i.i.d. assumptions.