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Research Intern

Causal Inference Research Intern, Signal Labs, June – August 2026 · Chicago, IL

Causal discovery on real-world observational data, and the use of LLM-elicited background knowledge as priors for structure learning.

CPIExtract: A software package to collect and harmonize small molecule and protein interactions

Published in (Submission, waiting for review) Bioinformatics Journal, 2025

Developed a pipeline to extract Compound-Protein Interaction (CPI) data from multiple sources and integrate them, to create augmented dataset to improve the performance of machine learning models.

Recommended citation: Piras, Andrea, et al. "CPIExtract: A software package to collect and harmonize small molecule and protein interactions." bioRxiv (2024).
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Relational Causal Discovery with Latent Confounders

Published in Conference on Uncertainty in Artificial Intelligence (UAI), 2025

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

Recommended citation: Negro, Matteo, Andrea Piras et al. "Relational Causal Discovery with Latent Confounders." The 41st Conference on Uncertainty in Artificial Intelligence.
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Narratives in the 2024 Moldovan Presidential Election: Stance, Coordination, and Influence

Published in IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2026

Quantifies coordinated amplification and heterogeneous peer influence in multi-platform election discourse under latent homophily, combining causal proxy modeling with network exposure embeddings.

CPIExtract

A software package to collect and harmonize small molecule and protein interactions

Teaching Assistant

University Course, University of Illinois Chicago, Department of Computer Science, 2025

Teaching Assistant for Graduate Course CS418 - Introduction to Data Science.