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About

Davide Nitti is a leading researcher at the intersection of probabilistic programming, robotics, and artificial intelligence, with a primary focus on enabling intelligent systems to reason and act in complex, dynamic, and hybrid relational domains—environments that combine continuous and discrete variables with an unknown number of interacting objects. His most influential contribution is the development of the **Distributional Clauses Particle Filter**, a fast inference algorithm for state estimation in these challenging settings, introduced in his highly cited 2013 paper (34 citations). This work, together with his foundational 2016 paper on **Probabilistic Logic Programming for Hybrid Relational Domains** (38 citations), established a powerful framework for representing and learning probabilistic models that can handle both uncertainty and relational structure. Nitti further advanced the field by pioneering methods for **learning the structure of dynamic hybrid relational models** (11 citations), moving beyond the common practice of discretizing continuous variables. His research also extends to robotics, where he has explored **relational affordances for multiple-object manipulation** (21 citations) and used combinatory categorial grammars to improve semantic parsing of robot commands by leveraging spatial context. Through these contributions, Nitti has provided essential tools and algorithms for building more robust and adaptable AI systems.