Fabrizio Russoمشاهده پروفایل
پژوهشگر
Fabrizio Russo is a postdoctoral Department of Computing researcher at Imperial College London , specializing in AI systems that integrate causal reasoning and computational argumentation frameworks. His work focuses on enhancing decision-making processes through transparent causal discovery techniques and explainable machine learning architectures. PhD in Safe and Trusted AI (2025) from Imperial College London via UKRI CDT Former Head of Data Science at 4most Europe (2014-2020) Co-organizer of Imperial College's Explainable AI Seminars Series (2020-present) His research combines causal inference with argumentation-based reasoning to create contestable AI systems that enable human-machine collaboration in critical decision-making scenarios. Key methodologies include: Constraint-based causal structure learning Shapley value-based feature selection Causal graph injection into neural networks Interactive argumentation frameworks Recent publications demonstrate applications in financial risk assessment (FICO HELOC), socioeconomic modeling (Adult dataset), and housing market predictions (Boston/California datasets). His work emphasizes regulatory compliance and transparency in AI deployments. Scientific contributions include: 2023 AISTATS Top-Reviewer Award Foundational work on causal injection techniques Development of argumentation-based explanation systems As a Graduate Teaching Assistant for Introduction to Machine Learning (2021-2022), he mentored students in algorithmic foundations and supervised implementation of explainable AI systems. His GitHub repositories contain open-source implementations of causal injection frameworks.









