The HPC4EAS research group has a long-standing track record in doctoral training and supervision across several research areas, including High-Performance Computing (HPC), performance analysis, fault tolerance, heuristic and metaheuristic optimization, and agent-based modeling and simulation of complex systems, particularly in healthcare.

The following overview summarizes the PhD theses supervised by members of the research group, organized into three main research areas:

  • Area A – High-Performance Computing, Performance Analysis, and Resilience
  • Area B – Heuristic and Metaheuristic Optimization
  • Area C – Intelligent Simulation and Healthcare Systems

These doctoral theses reflect the group’s scientific expertise, sustained research activity, and contribution to the training of researchers.

A detailed list of PhD theses is provided below, including doctoral candidates, thesis titles, supervisors, defense dates, and relevant research outcomes, where available.

Area A. High-Performance Computing, Performance Analysis, and Resilience

Diego Encinas (2026). Agent-Based Modeling and Simulation Applied to Input/Output Architecture in Parallel Computers. Supervisors: Marcelo Naiouf, Armando De Giusti, Emilio Luque, and Dolores Rexachs. Defense date: 20 March 2026.

Marina Morán (2025). Modeling and Management of Energy Consumption in a High-Performance Computing System with Fault Tolerance. Supervisors: Dolores Rexachs, Javier Balladini, and Enzo Rucci. Defense date: 25 April 2025.

Felipe Tirado (2024). Characterization of Applications with Irregular Behavior for Performance Prediction, Based on the PAS2P Philosophy. Supervisors: Alvaro Wong and Emilio Luque. Defense date: 4 April 2024.

Betzabeth León (2023). Storage Management for Fault Tolerance in High-Performance Computing. Supervisors: Dolores Rexachs and Daniel Franco. Defense date: 9 March 2023.

Diego Montezanti (2020). Soft Error Detection and Automatic Recovery in High Performance Computing Systems (SEDAR). Supervisors: Armando De Giusti, Marcelo Naiouf, Dolores Rexachs, and Emilio Luque. Defense date: 18 March 2020. Research output: SEDAR methodology.

Jorge Luis Villamayor Leguizamón (2018). Fault Tolerance Configuration and Management for HPC Applications using RADIC. Supervisor: Dolores Rexachs. Defense date: 30 November 2018. Research output: RADIC fault-tolerance methodology.

Laura María Espínola Brítez (2018). Efficient Communication Management in Cloud Environments. Supervisor: Daniel Franco. Defense date: 30 November 2018.

Pilar Gómez Sánchez (2018). Analyzing the Parallel Applications’ I/O Behavior Impact on HPC Systems. Supervisor: Dolores Rexachs. Defense date: 22 June 2018. Research output: Parallel I/O characterization.

Joe Carrion Jumbo (2017). Mejorando la red de los servicios de motores de búsqueda a través de enrutameinto basado en aplicación. Supervisor: Daniel Franco. Defense date: 20 July 2017.

Albert Gutiérrez Millà (2016). Crowd Modeling and Simulation on High Performance Architectures. Supervisor: Remo Suppi. Defense date: 22 July 2016. Research output: HPC-based crowd simulation.

Area B. Heuristic and Metaheuristic Optimization

María Harita (2026). Design and Application of a Heuristic Methodology for Solving Complex Optimization Problems. Supervisors: Emilio Luque and Dolores Rexachs. Defense date: 30 April 2026.

Area C. Intelligent Simulation and Healthcare Systems

Ramo Galeano (2026). Management of Hospital Emergency Services Based on Performance Indicators Using Simulation. Supervisors: Emilio Luque and Francisco Epelde. Defense date: 24 April 2026.

Mohsen Hallaj Asghar (2026). Designing In Silico Clinical Trials: The COPD Case Study. Supervisors: Emilio Luque and Alvaro Wong. Defense date: 24 March 2026.

Ghazal Tashakor (2021). Scalable Agent-Based Model Simulation Using Distributed Computing on System Biology. Supervisor: Remo Suppi. Defense date: 26 January 2021.

Elham Shojaei (2020). Simulation for Investigating Impact of Dependent and Independent Factors on Emergency Department System Using High Performance Computing and Agent-based Modeling. Supervisors: Emilio Luque and Francisco Epelde. Defense date: 19 October 2020. Distinction: Outstanding Dissertation Award.

Cecilia Elizabeth Jaramillo Jaramillo (2017). Modelización y Simulación de la transmisión por contacto de una infección nosocomial en el servicio de urgencias hospitalarias. Supervisors: Dolores Rexachs and Francisco Epelde. Defense date: 21 July 2017. Research output: Healthcare infection simulation.

Eva Bruballa (2017). Scheduling Non Critical Patients’ Admission in a Hospital Emergency Department. Supervisor: Emilio Luque. Defense date: 21 July 2017. Research output: Patient admission scheduling.

Francisco Borges (2016). Care HPS: A High Performance Simulation Methodology for Complex Agent-Based Models. Supervisor: Remo Suppi. Defense date: 30 September 2016. Research output: Care HPS methodology.

Liu Zhengchun (2016). Modeling & Simulation for Healtcare Operations Management Using High Performance Computing & Agent Based Model. Supervisor: Emilio Luque. Defense date: 22 July 2016. Distinction: Outstanding Dissertation Award.

Further details on the PhD theses supervised by members of the HPC4EAS research group are provided below, including thesis abstracts and links to the corresponding institutional repositories, where the full doctoral dissertations can be accessed.

PHD Thesis: María Harita: April 30, 2026

Title: Design and Application of a Heuristic Methodology for Solving Complex Optimization Problems

TDX Source: https://www.tdx.cat/handle/10803/697837#page=1

marielos

Optimization has become a cornerstone of High-Performance Computing (HPC) and modern computational science, enabling the effcient resolution of increasingly complex and large-scale problems. As complex systems, such as simulation models, logistic networks, or operational management frameworks, grow in dimensionality and uncertainty, for instance by incorporating additional decision variables, traditional deterministic or exhaustive search methods often become computationally infeasible. In this context, heuristic and metaheuristic approaches have emerged as flexible, adaptive, and evolving strategies capable of providing high-quality solutions within reasonable computational times. These methods do not aim to guarantee the absolute optimum but to approximate it effciently by balancing exploration and exploitation in vast and irregular search spaces. This doctoral research presents the development of a heuristic methodology Montecarlo Clustering Search Algorithm (MCSA) designed to address multiobjective combinatorial optimization problems under realistic conditions. The proposed approach integrates two complementary mechanisms: – Montecarlo sampling, which performs a global and stochastic exploration of the solution space, and – clustering-based refinement, which identifies promising regions and guides the search toward better solutions through iterative exploitation. Starting from this simple yet powerful idea, sampling and grouping, the methodology evolves into a versatile optimization framework capable of handling high-dimensional and heterogeneous problems. Its performance was validated through benchmark mathematical functions, demonstrating its reliability and adaptability across different optimization landscapes. Finally, the methodology was integrated with a simulation model of an Emergency Department (ED), allowing the algorithm to evaluate the real impact of each configuration under variable operational conditions. This integration highlights the practical applicability, robustness, and transferability of the proposed framework to real-world decision-making environments. Overall, this work contributes a generalizable and interpretable optimization methodology that bridges the gap between theoretical exploration and real-world application, advancing the role of heuristics in HPC-oriented optimization and complex system analysis.

PHD Thesis: Ramona Galeano: April 27, 2026

Title: Management of Hospital Emergency Services Based on Performance Indicators Using Simulation

TDX Source: https://www.tdx.cat/handle/10803/697477#page=1

Hospital emergency departments must be prepared to handle crises and disasters, including pandemics and epidemics. If these types of events occur, they require a rapid solution to provide a sustained response to the problem at hand. Simulation can be used effectively to create or improve a system or process. It is beneficial for building hypothetical scenarios and is used in many areas, including emergency departments, where it aids decision-making. In this context, simulation is presented as a valuable tool for designing or improving systems and processes. Its usefulness lies in its ability to create hypothetical scenarios that enable anticipation and planning of actions. This technique is widely used in various fields, including medical emergencies. Simulation in the emergency department serves as a decision-support system, enabling healthcare managers to make informed decisions and optimize resource utilization, ultimately leading to a more efficient patient care system. Standards in hospital emergency services achieve greater efficiency, more transparent communication, and uniformity of services across the entire healthcare system. Having a set of standards for calculating doctors, nurses, bays, and triage in hospital emergency services would not only facilitate better management of these situations but also ensure consistency and accuracy. Still, it would also help maintain a consistent, high-quality response across all hospitals nationwide. Using a set of standards in hospital emergency services could help us maintain a standard across all hospitals in each country. This research focuses on a simulator that will serve as a basis and enhance its predictive potential by adapting it to a range of scenarios, including pandemics and epidemics. We use a reference model based on the Spanish Ministry of Health and Social Policy’s document of standards and recommendations on hospital emergency services to analyze how specific parameters evolve under different conditions. Among the scenarios considered are population aging and growth, as well as reductions in available resources (such as physicians and nursing staff). This model enables the evaluation and adjustment of key aspects, including the composition of the medical team and nursing staff, the devices used (care boxes), and patient wait-time management. Additionally, we conducted a comparative analysis of emergency care standards across several countries. The objective is to evaluate the efficiency of the Spanish model relative to other international systems and to understand its performance across various contexts.

PHD Thesis: Mohsen Hallaj Asghar: March 24, 2026

Title: Designing In Silico Clinical Trials: The COPD Case Study

TDX Source: https://www.tdx.cat/handle/10803/697277#page=1

Chronic Obstructive Pulmonary Disease (COPD) is a complex and heterogeneous condition characterised by fluctuating symptoms, environmental sensitivity, and frequent episodes of instability that contribute substantially to emergency demand, particularly in ageing populations such as Spain. Understanding how COPD evolves over short time scales, and how individual patients respond to behavioural and environmental changes, is difficult to examine using traditional clinical research methods. Many relevant scenarios cannot be tested directly due to ethical or logistical constraints, and real-world datasets often fail to capture the diversity of trajectories observed among patients. This thesis addresses these limitations by developing a hybrid In-Silico modelling framework that integrates two complementary components. The first is a rule-based clinical model derived from a SIEL: Supervise Identification Exacerbation Level model, which evaluates daily physiological, symptomatic, behavioural, and environmental signals. The second is an agent-based model (ABM) that represents population heterogeneity in demographics, baseline severity, comorbidities, behavioural tendencies, and exposure patterns. When combined, these two layers form a hybrid model whose decisions and trajectories are traceable and transparent: each agent’s evolution can be followed step by step, and every state change can be linked to the specific SIEL rules and individual attributes that produced it. This integration provides a level of interpretability that neither rule-based systems nor agent-based approaches offer independently. A series of In-silico experimental simulations was conducted to examine the behaviour of the model under varied environmental and behavioural conditions. The framework reproduced a set of COPD patterns, including stability, worsening, and exacerbation-like dynamics, even when individuals were exposed to similar external contexts. Environmental stressors such as air-quality fluctuations and temperature drops influenced deterioration risk, while inter-individual differences generated heterogeneous outcomes consistent with real-world variation. These results demonstrate the capacity of the hybrid model to simulate realistic and diverse short-term disease trajectories and to explore scenarios that are infeasible to evaluate through traditional studies. Overall, the thesis proposes an interpretable, traceable, and scalable framework for simulating short-term COPD evolution. By combining rule-based clinical logic 4with agent-level variability, the model supports hypothesis generation, enhances the understanding of disease dynamics in heterogeneous populations, and offers a methodological foundation for future In-Silico research and scenario-based analyses in respiratory care.

PhD Thesis: Diego Omar Encinas: March 20, 2026

Title: Modelización y simulación basada en agentes aplicada a la arquitectura de entrada/salida de los computadores paralelos.

TDX Source: https://sedici.unlp.edu.ar/handle/10915/194710

The general objective of this thesis is to develop a model of the I/O subsystem in High Performance Computing (HPC) environments capable of predicting how changes in hardware and software components affect both the functionality and performance of parallel I/O systems. The work focuses on the application of Agent-Based Modeling and Simulation (ABMS) to represent the complex interactions within the I/O software stack, with particular emphasis on parallel file systems.

The main contributions are as follows:

The definition of an agent-based model for representing the hardware and software components involved in parallel I/O architectures in HPC systems.
The characterization of the interactions among the different layers of the I/O software stack, including application, middleware, and parallel file system components.
The design and implementation of SABIOS (Simulator for Agent-Based I/O System), a simulation tool capable of modeling and visualizing the behavior of HPC I/O systems under different configurations.
A detailed representation of the agents and communication mechanisms involved in the operation of the PVFS2 parallel file system.
The evaluation of the impact of different I/O configurations and architectural parameters on system functionality and performance.
The demonstration of how ABMS can be used to quantify and visualize the temporal and spatial behavior of parallel I/O systems, enabling the analysis of bottlenecks and optimization opportunities in HPC environments.

The results obtained with SABIOS demonstrate the effectiveness of agent-based simulation for studying complex I/O architectures, providing a flexible framework for analyzing performance, scalability, and the interaction between the different layers of the parallel I/O stack.

PhD Thesis: Marina Morán: April 25, 2025 (Associate Professor Universidad Nacional del Comahue, Argentina)  

Title: Modelización y gestión del consumo energético en un sistema de computación de altas prestaciones con tolerancia a fallos.

award-1_0.png
Outstanding dissertation award

TDX Source: https://sedici.unlp.edu.ar/handle/10915/180599

mmoran

The general objective of this thesis is to identify and manage the potential for energy savings when using the fault tolerance method based on checkpoint/restart in high-performance computing systems. The main contributions are as follows:

A method for building energy consumption prediction models for system-level coordinated checkpoint/restart in SPMD applications with homogeneous rollbacks.

An energy consumption prediction model for checkpoint/restart applied to a heat propagation application following the SPMD parallel programming paradigm.

A study of system (hardware and software) and application-level factors that affect the energy consumption generated by checkpoint and restart operations.

The definition of a series of energy-saving strategies in the event of a failure, where only the failed processes are recovered, considering that they communicate directly and indirectly with the failed process.

The definition of a model to evaluate the impact of applying these strategies on energy consumption under different scenarios.

The design and development of a simulator that utilizes the model above enables the evaluation of the proposed strategies and determines the most energy-efficient one.

PhD Thesis: Felipe Leonardo Tirado Marabolí: April 4, 2024 (Associate Professor Universidad Católica del Maule, Talca, Chile)  

Title: Caracterización de aplicaciones con comportamiento irregular para predecir su rendimiento, basado en la filosofía PAS2P.

TDX Source: : https://www.tdx.cat/handle/10803/691721#page=1

ft Modeling parallel scientific applications allows us to understand the intricacies of parallel application behavior. Many scientific applications exhibit irregular behavior, which makes it difficult to obtain the model that characterizes the application. This difficulty is mainly due to their non-deterministic pattern of communication and computation. One way to obtain information from the applications is through analysis tools. The PAS2P tool, based on application repeatability, focuses on performance analysis and prediction using the application signature. It uses the same resources that run the parallel application for its analysis, thus creating a machine-independent model and identifying common patterns in the application.

When applying the PAS2P tool to irregular applications, generating a model is impossible due to the non-homogeneous communication and computation pattern that prevents finding a repeatability pattern, ma- king it difficult to obtain a model that characterizes the behavior of these applications.

Due to the above, we present a modeling methodology for irregular parallel applications that analyzes data by processes. This approach successfully characterizes the application based on the behavior of each process. To effectively characterize irregular applications, we group the repeatability patterns of all the processes executing the application into a single model. The model that characterizes the behavior of the irregular parallel application can be used to analyze and predict the execution time of the application on a target machine.

The experimental validation of the proposed model called “Extension of PAS2P for irregular applications” demonstrates a reduction in the communications performed by PAS2P, which implies a decrease in the time required for the application analysis. Furthermore, it is possible to characterize irregular applications, establishing a machine-independent model that can predict the execution time with an average error of less than 9 %.

 

PhD Thesis: Betzabeth del Carmen León Otero: March 9, 2023. (Associate Professor UAB)

Title: Gestión del Almacenamiento para Tolerancia a Fallos en Computación de Altas Prestaciones.

TDX Source: http://hdl.handle.net/10803/689691

fotobetzabeth2 In HPC environments, it is essential to keep applications that require a long execution time running continuously. Redundancy is one of the methods used in HPC as a protection strategy against any failure, but generating an overhead due to redundant information implies additional time and resources to ensure the correct functioning of the system.
Fault tolerance has become fundamental in ensuring system availability in high-performance computing environments. Among the strategies used is the rollback recovery, which consists of returning to a previous correct state previously saved. Checkpoints allow information on the state of a process to be saved periodically in a stable storage system.
Still, a lot of latency is involved as all processes are concurrently accessing the file system. Also, checkpoint storage can affect parallel application performance and scalability that uses message passing. Therefore, it is important to know the elements that can impact checkpoint storage and how they can influence the scalability of a fault-tolerant application. For example, characterizing the files generated when performing the checkpoint of a parallel application is useful to determine the resources consumed and their impact on the I/O system. It is also important to characterize the application that performs the checkpoint because the I/O of the checkpoint depends mainly on it.
The present research proposes a methodology that helps in configuring stable storage of the I/O files generated by fault tolerance, considering the access patterns to the generated files and the user requirements. This methodology has three phases in which the I/O patterns of the checkpoint are characterized. Then, the stable storage requirements are analyzed, and the behavior of the fault tolerance strategy is modeled.
A model of prediction of checkpoint scalability has been proposed as part of the last phase of the methodology. This methodology can be useful when selecting which type of checkpoint configuration is most appropriate based on the characteristics of
the applications and the available resources. Thus, the user will know how much storage space the checkpoint consumes and how much the application consumes to establish policies that help improve the distribution of resources.

 

PhD Thesis: Ghazal Tashakor: January 26, 2021, 18:00 (Postdoc at Jülich Research Center – DEEP projects). 

Title: Scalable agent-based model simulation using distributed computing on system biology.

TDX Source: http://hdl.handle.net/10803/671332

ne2.pngAbstract: Agent-based modeling is a very useful computational tool to simulate complex behavior using rules at micro and macro scales. This type of modeling’s complexity is in defining the rules that the agents will have to define the structural elements or the static and dynamic behavior patterns. This thesis considers the definition of complex models of biological networks that represent cancer cells obtain behaviors on different scenarios by means of simulation and to know the evolution of the metastatic process for non-expert users of computer systems.

Besides, a proof of concept has been developed to incorporate dynamic network analysis techniques and machine learning in agent-based models based on developing a federated simulation system to improve the decision-making process. For this thesis’s development, the representation of complex biological networks based on graphs has been analyzed, from the simulation point of view, to investigate how to integrate the topology and functions of this type of networks interacting with an agent-based
model. For this purpose, the ABM model has been used as a basis for the construction, grouping, and classification of the network elements representing the structure of a complex and scalable biological network.

The simulation of complex models with multiple scales and multiple agents provides a useful tool for a scientist, non-computer expert to execute a complex parametric model and use it to analyze scenarios or predict variations according to the different patient’s profiles.
The development has focused on an agent-based tumor model that has evolved from a simple and well-known ABM model. The variables and dynamics referenced by the Hallmarks of Cancer have been incorporated into a complex model based on graphs. Based on graphs, this model is used to represent different levels of interaction and dynamics within cells in the evolution of a tumor with different degrees of representations (at the molecular/cellular level).

A simulation environment and workflow have been created to build a complex, scalable network based on a tumor growth scenario. In this environment, dynamic techniques are applied to know the tumor network’s growth using different patterns. The experimentation has been carried out using the simulation environment developed considering the execution of models for different patient profiles, as a sample of its functionality, to calculate parameters of interest for the non-computer expert, such as the evolution of the tumor volume.

 

PhD Thesis: Elham Shojaei: October 19 2020, 10:30. (Researcher at Leibniz Rechenzentrum, der Bayerischen Akademie der Wissenschaften)

Title: Simulation for Investigating Impact of Dependent and Independent Factors on Emergency Department System Using High Performance Computing and Agent-based Modeling.

award-1_0.png
Outstanding dissertation award

TDX Source: http://hdl.handle.net/10803/670856

ne2.pngAbstract: Increased life expectancy, and population aging in Spain, along with their corresponding health conditions such as non-communicable diseases (NCDs), have been suggested to contribute to higher demands on the Emergency Department (ED). Spain is one of such countries which an ED is occupied by a very high burden of patients with NCDs. They very often need to access healthcare systems and many of them need to be readmitted even though they are not in an emergency or dangerous situations. Furthermore many NCDs are a consequence of lifestyle choices that can be controllable. Usually, the living conditions of each chronic patient affect health variables and change the quantity of these health variables, so they can change the stability situation of the patients with NCDs to instability and its resultant will be visiting ED.

In this study, a new method for the prediction of future performance and demand in the emergency department (ED) in Spain is presented. Prediction and quantification of the behavior of ED are, however, challenging as ED is one of the most complex
parts of hospitals. Future years of Spain’s ED behavior was predicted by the use of detailed computational approaches integrated with clinical data. First, statistical models were developed to predict how the population and age distribution of patients
with non-communicable diseases change in Spain in future years. Then, an agent-based modeling approach was used for simulation of the emergency department to predict impacts of the changes in population and age distribution of patients with NCDs on the performance of ED, reflected in hospital LoS, between years 2019 and 2039.

Then in another part of this study, we propose a model that helps to analyze the behavior of chronic disease patients with a focus on heart failure patients based on their lifestyle. We consider how living conditions affect the signs and symptoms of chronic disease and, accordingly, how these signs and symptoms affect chronic disease stability. We use an agent-based model, a state machine, and a fuzzy logic system to develop the model. Specifically, we model the required ’living condition’ parameters that can influence the required medical variables. These variables determine the stability class of chronic disease.

This thesis also investigates the impacts of Tele-ED on behavior, time, and efficiency of ED and hospital utilization. Then we propose a model for Tele-ED which delivers the medical services online. Simulation and Agent-based modeling are powerful tools that allow us to model and predict the behavior of ED as a complex system for VIa given set of desired inputs. Each agent based on a set of rules responds to its environment and other agents. This thesis can answer several questions in regards to the demand and performance of ED in the future and provides health care providers with quantitative information on economic impact, affordability, required staff, and physical resources. Prediction of the behavior of patients with NCDs can also be beneficial for health policy to plan for increasing health education in the community, reduce risky behavior, and teaching to make healthy decisions in a lifetime. Prediction of behavior of Spain’s ED in future years can help care providers for decision-makers to improve health care management.

 

PhD Thesis: Diego Montezanti: March 18, 2020 09:00 (ARG). (Researcher at III-LIDI UNLP)

Title:  Soft Error Detection and Automatic Recovery in High Performance Computing Systems (SEDAR).

TDX Source: http://sedici.unlp.edu.ar/handle/10915/104331

ne2.pngAbstract:

Reliability and fault tolerance have become aspects of growing relevance in the field of HPC, due to the increased probability that faults of different kinds will occur in these systems. This is fundamentally due to the increasing complexity of the processors, in the search to improve performance, which leads to a rise in the scale of integration and in the number of components that work near their technological limits, being increasingly prone to failures. Another factor that affects is the growth in the size of parallel systems to obtain greater computational power, in terms of number of cores and processing nodes.

As applications demand longer uninterrupted computation times, the impact of faults grows, due to the cost of relaunching an execution that was aborted due to the occurrence of a fault or concluded with erroneous results. Consequently, it is necessary to run these applications on highly available and reliable systems, requiring strategies capable of providing detection, protection and recovery against faults.

In the next years it is planned to reach Exa-scale, in which there will be supercomputers with millions of processing cores, capable of performing on the order of 1018 operations per second. This is a great window of opportunity for HPC applications, but it also increases the risk that they will not complete their executions. Recent studies show that, as systems continue to include more processors, the Mean Time Between Errors decreases, resulting in higher failure rates and increased risk of corrupted results; large parallel applications are expected to deal with errors that occur every few minutes, requiring external help to progress efficiently. Silent Data Corruptions are the most dangerous errors that can occur, since they can generate incorrect results in programs that appear to execute correctly. Scientific applications and large-scale simulations are the most affected, making silent error handling the main challenge towards resilience in HPC. In message passing applications, a silent error, affecting a single task, can produce a pattern of corruption that spreads to all communicating processes; in the worst case scenario, the erroneous final results cannot be detected at the end of the execution and will be taken as correct.

Since scientific applications have execution times of the order of hours or even days, it is essential to find strategies that allow applications to reach correct solutions in a bounded time, despite the underlying failures. These strategies also prevent energy consumption from skyrocketing, since if they are not used, the executions should be launched again from the beginning. However, the most popular parallel programming models used in supercomputers lack support for fault tolerance. 

PhD Thesis supervised by members of the group: