I am professor in machine learning with interest and experience in different areas of ML.

My current research focuses on structural causal models and causal abstraction. I am interested in understanding how multiple causal models at different levels of abstraction can be related to each other. My reseach in this area ranges from theoretical studies aimed at formally defining and characterizing forms of abstraction to methodological work concerned with learning relationships of abstraction from data and exploiting them in real-world settings.

Previously, I worked on applications of reinforcement learning and Bayesian models to computer security, theoretical analysis of unsupervised algorithms via bounds and information-theoretic methods, evaluation of societal aspects of machine learning, fairness in data aggregation.

I am generally interested in the systematization of machine learning, and its intersections with other fields.

Academic article

• Zennaro, Fabio Massimo; Erdodi, Laszlo Tibor (2023). Modelling penetration testing with reinforcement learning using capture-the-flag challenges: Trade-offs between model-free learning and a priori knowledge. (external link)
• Sommervoll, Åvald Åslaugson; Erdodi, Laszlo Tibor; Zennaro, Fabio Massimo (2023). Simulating all archetypes of SQL injection vulnerability exploitation using reinforcement learning agents. (external link)
• Erdodi, Laszlo; Sommervoll, Åvald Åslaugson; Zennaro, Fabio Massimo (2021). Simulating SQL injection vulnerability exploitation using Q-learning reinforcement learning agents. (external link)
• Yazidi, Anis; Ivanovska, Magdalena; Zennaro, Fabio Massimo et al. (2021). A new decision making model based on Rank Centrality for GDM with fuzzy preference relations. (external link)
• Erdodi, Laszlo; Zennaro, Fabio Massimo (2021). The Agent Web Model - Modelling web hacking for reinforcement learning. (external link)
• Zennaro, Fabio Massimo; Ivanovska, Magdalena (2019). Counterfactually Fair Prediction Using Multiple Causal Models. (external link)
• Zennaro, Fabio Massimo; Ivanovska, Magdalena; Jøsang, Audun (2019). An empirical evaluation of the approximation of subjective logic operators using Monte Carlo simulations. (external link)
• Zennaro, Fabio Massimo; Chen, Ke (2018). Towards understanding sparse filtering: A theoretical perspective. (external link)

Academic lecture

• Zennaro, Fabio Massimo (2020). A Left Realist Critique of the Political Value of Adopting Machine Learning Systems in Criminal Justice. (external link)
• Egiazarov, Alexander; Zennaro, Fabio Massimo; Mavroeidis, Vasileios (2020). Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions. (external link)
• Egiazarov, Alexander; Mavroeidis, Vasileios; Vishi, Kamer et al. (2019). Firearm Detection and Segmentation using an Ensemble of Semantic Neural Networks. (external link)
• Zennaro, Fabio Massimo (2019). Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings. (external link)
• Zennaro, Fabio Massimo; Ivanovska, Magdalena (2018). Counterfactually Fair Prediction Using Multiple Causal Models. (external link)
• Zennaro, Fabio Massimo; Ivanovska, Magdalena (2018). Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation. (external link)

Academic chapter/article/Conference paper

• Zennaro, Fabio Massimo; Jøsang, Audun (2020). Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems. (external link)
• Zennaro, Fabio Massimo (2020). Analyzing and Storing Network Intrusion Detection Data Using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings. (external link)

See a complete overview of publications in Cristin.