Computational Analysis of Piano Practice: Towards Modeling Practice Mistakes and Repetitions for Understanding Learning Behaviours

By: Alia Ahmed Morsi Moustafa
Supervisor: Dr. Xavier Serra

Date: September 29, 2026 - 14:00 h
Room: 55.309

Abstract

The main goal of this research is to understand the process of musical instrument learning in the Western Classical tradition, through developing computational methods for the analysis of piano practice recordings. We focus on mistakes and the organizational structure of practice sessions, as both can reveal information about one’s musical expertise. Specifically, our research (i) models mistakes in a manner that reflects educational understanding beyond deviations from a reference, (ii) trains classifiers that can automatically detect common piano learning mistakes, and (iii) develops methods to understand the organization of musical content in practice sessions based on similarity matrix analysis.

We propose treating piano mistakes as sequences that include both initial error and subsequent recovery phases, with each composed of low-level operations (note insertions, deletions, and time shifts) and release a toolkit that generates synthetic labelled data according to the proposed framework. Considering mistakes as behavioral phenomena with a contextual and qualitative aspect, we investigate whether the locations of salient errors in a performance can be detected through neighbouring context without music score comparisons, through supervised learning experiments combining synthetic and real data. Our results show feasibility while revealing generalization challenges across mistake types and performance contexts.

As for the computational analysis of practice organization, we develop a similaritybased segmentation method that identifies repeated attempts at passages, with emphasis on grouping together attempts of the same passage despite the existence of mistakes. We discuss how self-similarity analysis enables close examination of mistake patterns across repetitions to provide insights into planning and learning strategies, and to enable future comparative studies that track the evolution of learning.

This dissertation operates in a domain where computational measurement and educational meaning are still being aligned, and takes that gap seriously as a methodological starting point. The contributions above are components of a bigger picture towards formalizing realistic and useful computational analysis targets for piano learning. The choices of approach in mistake modelling, simulation, detection, and practice structure analysis collectively illustrate how methodological transparency can deepen the analysis itself.

Translational Computational Cardiac Safety: Comprehensive Modeling for Predicting Drug-induced Proarrhythmic Risks Across Diverse Populations

By: Paula Domínguez Gómez
Supervisors: Dr. Oscar Cámara, Dr. Jazmín Aguado & Dr. Borje Darpo

Date: October 26, 2026 - 15:00 h
Room: 55.309

Abstract

Cardiac safety assessment is one of the most consequential challenges in drug development. Drug-induced arrhythmias have historically driven late-stage clinical failures and even postmarket withdrawals, and the field has responded with increasingly stringent preclinical and clinical screening requirements. The underlying mechanism (block of cardiac ion channels, leading to QT interval prolongation and potentially fatal ventricular arrhythmias) is well characterized, yet the translation of preclinical safety signals into reliable predictions of clinical risk remains an unsolved problem. Traditional methodologies, such as in vitro ion channel assays and animal models, provide important early warning signals but are limited in their ability to capture the complexity of human cardiac electrophysiology, the heterogeneity of patient populations, and the range of clinical exposure conditions encountered in practice.

Computational modeling has emerged as a powerful complement to existing frameworks, offering mechanistic understanding of underlying processes, enhanced predictive accuracy, and the ability to explore scenarios that are inaccessible to experimental or clinical investigation. Initiatives such as the Comprehensive in vitro Proarrhythmia Assay have formalized the role of in silico modeling in cardiac safety assessment, promoting a shift toward model-informed drug development in regulatory practice. Within this context, the present thesis develops and validates a series of translational computational frameworks for drug-induced proarrhythmic risk assessment, bridging the gap between preclinical testing and clinical outcomes across diverse and clinically relevant patient populations. 

The work develops virtual cardiac populations for in silico clinical trials, building a framework for predicting exposure-response relationships and validating it against clinical trial data. This foundation is progressively extended to incorporate placebo effects, pathological conditions, and mathematical models of drug interactions, enabling safety stratification across underrepresented subpopulations and risk estimation for combination therapies. A parallel line of work addresses scalability limitations through artificial intelligence cardiac emulators, which replicate the behavior of the underlying electrophysiological models at a fraction of the computational cost, enabling real-time sex-specific proarrhythmic risk assessment and uncertainty quantification, demonstrated through a loperamide overdose case study. Underpinning both lines of work is a structured verification, validation, and uncertainty quantification pipeline for scientific machine learning. This pipeline extends traditional credibility assessment principles to systems whose parameters are inferred from data, rather than derived from physics-based models.

Taken together, these contributions support the view that the incorporation of computational tools into drug cardiac safety assessment represents an evolution rather than a revolution. The principles underlying these approaches are not new; what this thesis provides is a more representative, more scalable, and more integrated cardiac safety assessment built on those same principles, better equipped to meet the demands of modern drug development and the expectations of an increasingly model-informed regulatory environment.

Characterising Brain States and Brain-state Transitions through Whole-brain Modelling and in Silico Perturbations

By: Irene Acero i Pousa
Supervisor: Dr. Gustavo Deco

Date: October 28, 2026 - 10:00 h
Room: 55.309

Abstract

Brain states can be studied through spontaneous activity, but their responses to perturbation provide complementary information about their organisation and capacity for change. This thesis combines whole-brain modelling with in silico perturbations to characterise brain states, examine state-dependent responsiveness, and model transitions.

First, weak perturbations characterised static and time-resolved functional hierarchy in fMRI data of schizophrenia and resilience. Second, personalised MEG models showed that perturbational effects depend on stimulation site, timing, frequency, and ongoing dynamics. Third, we introduced Inception, a framework for estimating post-perturbation states. Applied to fMRI data from disorders of consciousness, it identified personalised perturbations that shifted modelled dynamics towards a healthy reference.

Finally, in fMRI data from depression, combining Inception with functional hierarchy revealed distinct candidate network-level mechanisms associated with escitalopram and psilocybin and identified alternative perturbation strategies for weaker responders. Together, these findings establish perturbation-based modelling as a hypothesis-generating framework for studying and potentially guiding brain-state change. 

Applied Machine Learning in Healthcare: Discrimination Risks, Prediction Stability, and Lifecycle Evaluation

By: Ioannis Bilionis
Supervisors: Dr. Carlos Castillo & Dr. Luis Fernández-Luque

Date: October 30, 2026 - 15:00 h
Room: 55.309

Abstract

The rapid adoption of Artificial Intelligence (AI) in healthcare has amplified concerns around data quality, algorithmic fairness, and the reliability of Machine Learning (ML) systems across their lifecycle. Despite significant advances in fairness-aware ML, existing evaluation approaches remain largely centered on predictive performance and static fairness metrics, providing limited insight into how training data composition, prediction instability, and model updates affect equitable outcomes across patient populations. This thesis addresses these gaps through a data-centric and lifecycle-aware set of frameworks and empirical analyses for developing equitable and trustworthy clinical AI.

First, it identifies critical gaps in publicly available health datasets, highlighting the underrepresentation of behavioral, mental health, and sociodemographic variables essential for fairness-aware modeling. Building on this foundation, the thesis demonstrates that dataset balance alone does not guarantee fairness, leveraging Systematic Arbitrariness (SA), a measure of prediction instability, as a novel lens in healthcare to reveal hidden disparities across patient subgroups. It then proposes a Discrimination Risk Assessment Framework that provides an actionable assessment of training data composition, enabling the detection of subgroup-specific risks prior to model deployment through the joint analysis of performance, stability, and training value. Finally, the work extends fairness-aware model evaluation to the post-deployment phase, showing that model updates under real-world data shifts can degrade stability and exacerbate inequities even when accuracy is preserved, and introduces a multidimensional monitoring framework to mitigate these risks.

Overall, these contributions establish a comprehensive approach to clinical ML that integrates data quality, fairness, and robustness. Although the proposed methodologies are empirically validated using representative chronic disease and pediatric diabetes case studies, the underlying frameworks are intended to provide a general foundation for discrimination-risk assessment and lifecycle-aware monitoring across a broader range of healthcare AI applications. 

Advancing the Understanding of Congenital Heart Diseases through Computational Modeling of the Fetal Cardiovascular System

By: Maria Inmaculada Villanueva Baxarias
Supervisors: Dr. Bart Bijnens, Dr. Gabriel Bernardino Pérez & Dr. Patricia García Cañadilla

Date: November 3, 2026 - 12:30 h
Room: 55.309

Abstract

Congenital heart diseases are one of the major causes of infant mortality, affecting approximately 1% of live births. During fetal life, shunts redistribute blood flow and help preserve organ perfusion, partially compensating disease-related alterations and potentially masking them, complicating prenatal diagnosis and severity assessment. The evaluation of fetal health and disease diagnosis relies mainly on imaging techniques, which can only provide a partial characterization of fetal hemodynamics, as they cannot directly quantify pressure or oxygen saturation, and are further limited by spatial resolution, fetal motion, and angle dependence. In this thesis, we advanced the understanding of congenital heart diseases through computational models of the fetal cardiovascular system on different physiological scales. We developed a closed-loop 0D model of the fetal circulation, coupled with an oxygen transport model, and a 3D computational fluid dynamic model of the fetal right atrium. First, we showed that in aortic coarctation, ventricular disproportion and ductus arteriosus dilation compensate for aortic narrowing, preserving pressures, organ perfusion, and most velocities; aortic isthmus velocity pattern might aid diagnosis. Second, we demonstrated that the Eustachian valve promotes preferential streaming of oxygenated ductus venosus blood toward the foramen ovale, influencing cerebral oxygen delivery. Finally, we found that fetal transposition of the great arteries remains relatively hemodinamically stable despite marked variability in oxygen distribution, particularly cerebral oxygenation; foramen ovale and ductus arteriosus velocity patterns may indicate worse postnatal outcomes. 

Efficient Computation of Distances Between Markov Chains: Applications to Reinforcement and Imitation Learning

By: Sergio Calo Oliveira
Supervisors: Dr. Anders Jonsson & Dr. Javier Segovia-Aguas

Date: November 6, 2026 - 14:00 h
Room: 55.309

Abstract

Markov chains are the standard model for sequential stochastic systems, and they sit at the heart of the Markov decision process framework used to model sequential decision-making problems. As learning systems are deployed across increasingly large, complex, and often subtly different environments, representation and generalization have become central concerns. Being able to compare stochastic processes is key to reasoning about transfer, robustness, and generalization. Under that premise, it is crucial to understand how similarity between stochastic processes can be quantified in a way that is both theoretically grounded and computationally tractable. The goal of this thesis is to build tools to answer this question by developing efficient methods to compute a theoretically grounded distance between Markov chains. We start by showing that two notions of distance developed in entirely separate communities (optimal transport distances and bisimulation metrics) are equivalent objects under a given cost function. That equivalence lets us reformulate the distance as a linear program over discounted occupancy couplings, add entropy regularization, and derive a Sinkhorn-like algorithm. The resulting method comes with sample-complexity guarantees and improved computational complexity over prior approaches, though it assumes full knowledge of both chains’ dynamics. We then drop that assumption by characterizing the distance through the chains’ marginal occupancy measures, which can be estimated from samples. We propose what is, to the best of our knowledge, the first method for estimating the distance from sample streams alone: a stochastic primal-dual algorithm with accompanying convergence guarantees. Finally, we derive the gradient of the distance with respect to the parameters of the compared chains and propose a general algorithm for fitting a parametric model to a stochastic process. We apply this method to representation learning, state-space compression, and imitation learning from observations. 

Investigation of Treatments for Depression with Whole-brain Dynamics and Modelling

By: Paulina Clara Dagnino
Supervisor: Dr. Gustavo Deco

Date: December 1, 2026 - 15:00 h
Room: 55.309

Abstract

Depression is a leading contributor to the global burden of disease. Different evidence-based treatments have demonstrated clinical efficacy, yet a deeper understanding of their neural signatures is needed to optimise effects. This thesis investigates functional magnetic resonance imaging (fMRI) brain changes following escitalopram, psilocybin, and mindfulness-based cognitive therapy (MBCT) in depression, as well as meditation in health. Using whole-brain dynamics and modelling as a unifying framework, perturbational, thermodynamic-inspired and dimensionality reduction approaches are applied to characterise the brain’s hierarchical and spatiotemporal organisation. The findings reveal that each intervention is associated with specific brain changes, with convergence across methods supporting the robustness of the results and differences adding new layers of knowledge. Importantly, brain markers are linked to clinical and behavioural outcomes and interpreted within psychological frameworks. Overall, this work contributes to our understanding of how escitalopram, psilocybin, and MBCT modulate neurocognitive functioning in depression and opens avenues for future research.