School of Computer Science PGR Seminar Carla Davesa Sureda and Gen Li

Carla Davesa Sureda will present Compiling Expressive Planning with Data Types.

Abstract: Classical planning relies on PDDL, a language whose expressivity is limited: modellers must encode structured data, numeric reasoning, and counting through propositional workarounds that are often verbose and error prone. In this talk, I present an extension of the Unified Planning framework with high-level modelling constructs: bounded integers, range variables, arrays, sets, and count expressions, together with a set of composable compilers that transform these constructs back into planner-compatible PDDL. This separation allows modellers to work with richer abstractions while preserving access to existing planners. I will describe the compilation pipeline, discuss trade-offs between different compilation strategies, and present results on a range of benchmark domains, such as the 15-Puzzle, Sokoban, and Rush Hour.

Bio: Carla Davesa Sureda is a third-year PhD student in the School of Computer Science, supervised by Prof Ian Miguel and Dr Joan Espasa. Her research focuses on automated planning, in particular on extending the expressivity of planning formalisms through compilation techniques, with connections to constraint programming.

Gen Li will present Visualization of clinical pathways based on sepsis comorbidities.

Abstract: Sepsis is a life-threatening condition, yet patients exhibit substantial differences in underlying diseases, disease progression, and clinical outcomes—a phenomenon commonly referred to as clinical heterogeneity. This heterogeneity makes it challenging to identify patient subgroups with similar characteristics and to deliver targeted treatments.

Previous studies have primarily relied on acute physiological measurements and laboratory parameters to stratify patients, often with a focus on outcome prediction. In contrast, comorbidities, as a potential source of heterogeneity, have been less frequently used as primary clustering features. Moreover, few studies have systematically characterized comorbidity co-occurrence patterns within identified subgroups, limiting our understanding of how interactions among comorbidities contribute to sepsis heterogeneity.

Bio: Gen Li is a PhD student in Computer Science, supervised by Dr Areti Manataki (School of Computer Science) and Dr Martin McKechnie (School of Medicine). His research lies at the intersection of health data science and data visualization, currently focusing on disease phenotyping of sepsis patients and the design of interactive clinical pathway visualization systems