Biography

Dr. Luis Miralles

Lecturer in Computer Science | Machine Learning Researcher | Software Engineering Educator

Dr. Luis Miralles is a Lecturer in Computer Science at Technological University Dublin, Ireland. His academic and professional work combines university teaching, applied research, software engineering, mobile and distributed systems, cybersecurity, and machine learning.

With more than ten years of university teaching experience and an international career across Ireland, Spain, Mexico, and Peru, Dr. Miralles has developed a broad academic profile focused on practical computing education, applied artificial intelligence, and real-world software systems.


Academic Profile

  • Lecturer in Computer Science at Technological University Dublin.
  • Researcher in applied machine learning, software engineering, and data-driven systems.
  • Author of more than 50 peer-reviewed publications.
  • Research impact of more than 1,000 academic citations.
  • Experienced supervisor of PhD, Master’s, and undergraduate research projects.
  • International academic experience in Ireland, Spain, Mexico, Peru, Germany, and Slovenia.

Current Role at Technological University Dublin

At Technological University Dublin, Dr. Miralles teaches and coordinates modules in computer science and software development. His teaching focuses on helping students develop strong theoretical foundations while also gaining practical skills that can be applied in industry and research.

Teaching Areas

  • Data Mining and Reinforcement Learning
  • Mobile Software Development
  • Enterprise Application Development
  • Distributed Systems
  • Programming support and practical software development laboratories

Teaching and Curriculum Development

  • Redesigned and modernised modules in Enterprise Application Development.
  • Updated Data Mining teaching content to include modern machine learning approaches.
  • Developed practical learning activities for Mobile Software Development.
  • Supports students through programming laboratories and project supervision.
  • Promotes applied, hands-on learning through software projects and real-world examples.

Research Interests

Dr. Miralles’ research focuses on applied machine learning and intelligent software systems. His work explores how data-driven methods can be used to solve practical problems in health, accessibility, digital systems, cybersecurity, and industrial environments.

  • Machine learning applications for COVID-19 and public health
  • Online advertising and intelligent advertising systems
  • Web accessibility and assistive technologies
  • Human activity recognition
  • Digital forensics
  • Zero-shot learning
  • Predictive maintenance using time series
  • Explainable artificial intelligence
  • AI-generated content and plagiarism detection
  • Numerical optimisation and Runge-Kutta schemes

Research Leadership and Projects

Dr. Miralles is actively involved in research leadership and collaborative projects. He is an ML-Lab Leader at Technological University Dublin and has contributed to applied artificial intelligence projects involving academic institutions, research centres, and industry partners.

  • ML-Lab Leader at Technological University Dublin.
  • Involved with CeADAR, Ireland’s Centre for Applied AI.
  • Participant in Enterprise Ireland Innovation Voucher projects.
  • Contributor to innovation grants and collaborative research initiatives.
  • Supervisor in projects related to online child protection, including N-Light.
  • Applicant and contributor to research funding proposals in machine learning and public health.
  • Contributor to Marie Skłodowska-Curie research proposals and collaborations.

PhD Supervision

Supervision and mentoring are central parts of Dr. Miralles’ academic work. He has successfully co-supervised doctoral researchers to completion and continues to supervise PhD students in emerging areas of artificial intelligence and machine learning.

Completed PhD Supervision

  • Kaiqiang Huang – Human Action Recognition with Scarce Training Data, focusing on Zero-Shot Learning.
  • Fatma Elzahraa Eltaher – Outdoor Navigation Support Using Machine Learning for People with Visual Impairment.

Current PhD Research Topics

  • Detection of AI-generated plagiarism.
  • Optimisation of Runge-Kutta numerical schemes.
  • Explainability in machine learning models.

Previous Academic Experience

University College Dublin

  • Postdoctoral Research Fellow from 2017 to 2020.
  • Worked on machine learning projects involving predictive maintenance.
  • Contributed to research in reinforcement learning, graph analysis, and text classification.

Universidad Panamericana, Mexico

  • Teacher and researcher in Computer Science from 2014 to 2017.
  • Taught Object-Oriented Programming.
  • Taught Analysis and Design of Algorithms.
  • Contributed to research and academic development in computing.

Universidad Católica Santo Toribio de Mogrovejo, Peru

  • Lecturer in 2012.
  • Taught Analysis and Design of Systems.
  • Taught Quality Systems, Engineering, and Software Development.

Industry and Professional Experience

Before and alongside his academic career, Dr. Miralles developed practical industry experience in software development, digital systems, web platforms, advertising, and consulting.

Software Development

  • Worked as a software developer at the University of Murcia.
  • Managed Digitum, a digital repository for scientific documents.
  • Developed systems for storage, access, and management of academic resources.

Web Development and Advertising

  • Worked as a freelance website developer and advertising manager.
  • Developed more than ten internet portals.
  • Managed platforms receiving approximately 40,000 advertising impressions per day.
  • Gained practical experience in online advertising systems and digital business models.

Consulting

  • Worked as an intern business consultant at Everis in Madrid.
  • Participated in a project related to renewable energies.
  • Gained experience in business technology consulting and applied IT solutions.

Education

PhD in Machine Learning in Computer Science

  • Institution: University of Murcia, Spain
  • Period: 2014–2017
  • Thesis: Design of a collaborative advertisement exchange model between advertising networks to optimise profitability.
  • Focus: Machine learning, online advertising systems, optimisation, and collaborative models.

Master’s Degree in IT Security

  • Institution: University of La Rioja, Spain
  • Period: 2013–2014
  • Focus: Cybersecurity, information systems protection, security management, and protection against cyber-attacks.

MBA in the Technology Sector

  • Institution: AEDE Business School, Madrid, Spain
  • Period: 2009–2010
  • Focus: Business management, technology strategy, group dynamics, case studies, and project preparation.

Undergraduate Degree in Computer Engineering

  • Institution: University of Murcia, Spain
  • Period: 2002–2007
  • Specialisation: Artificial Intelligence
  • Final Project: Image Segmentation using Ontologies

Technical Skills

Programming Languages

  • C
  • C++
  • C#
  • Java
  • Python
  • R
  • HTML

Artificial Intelligence and Data Science

  • Machine Learning
  • Data Mining
  • Feature Selection
  • Genetic Algorithms
  • Reinforcement Learning
  • Text Classification
  • Graph Analysis
  • Predictive Maintenance
  • Human Activity Recognition

Software Engineering

  • Mobile application development
  • Enterprise application development
  • Distributed systems
  • Object-oriented programming
  • Web development
  • Database-driven applications

Certifications and Professional Training

  • GDPR Certificate from Technological University Dublin.
  • Fire Safety Certificate from Technological University Dublin.

Languages

  • Spanish: Native language.
  • English: Advanced/fluent professional level.

International Experience

Dr. Miralles has developed an international academic and professional career, having worked, studied, or collaborated in several countries. His experience across different educational systems has shaped his approach to teaching, supervision, and research collaboration.

  • Spain – education, research, software development, and consulting.
  • Ireland – lecturing, postdoctoral research, supervision, and applied AI projects.
  • Mexico – teaching and research in computer science.
  • Peru – university teaching in software engineering and systems analysis.
  • Germany – international academic engagement.
  • Slovenia – international academic engagement.

Academic and Community Engagement

In addition to teaching and research, Dr. Miralles has contributed to academic community life through student-focused initiatives, departmental activities, and practical improvements to the university environment.

  • Organised book reading competitions.
  • Supported student engagement initiatives.
  • Contributed to academic community events.
  • Participated in activities for staff recognition and retirement celebrations.
  • Helped with initiatives to improve shared academic and student spaces.

Professional Identity

Dr. Luis Miralles’ professional identity is built around the connection between computing education, applied research, and practical software systems. His career reflects a commitment to preparing students for real-world computing challenges while also contributing to research that applies artificial intelligence and software engineering to socially and industrially relevant problems.

His work brings together experience in machine learning, cybersecurity, accessibility, mobile computing, distributed systems, and digital platforms. Through teaching, supervision, research, and collaboration, he continues to contribute to the development of responsible, practical, and impactful computing solutions.

Overview about Reinforcement Learning

 

This video gives an overview on Reinforcement Learning:



Highlighted Publications

My publications can be found in:

https://scholar.google.com/citations?user=wocWwrQAAAAJ&hl=en&oi=ao

https://www.researchgate.net/profile/Luis-Miralles-Pechuan?ev=hdr_xprf

https://orcid.org/0000-0002-7565-6894



History and Future of Reinforcement Learning in Artificial Intelligence: A Real-World approach

Machine Learning is an important part of artificial intelligence in which computers learn in an automated fashion from the provided information. Machine Learning applications are increasingly present in our lives and due to the advantages that entail predicting events with a certain degree of reliability, they have been covering more and more sectors. Supervised learning is the most developed branch of Machine Learning. Some daily life examples could be predicting the weather forecast, displaying the most suitable adverts to users on the websites, selecting the best route based on traffic, estimating the future price of the stock market, or calculating the risks of a catastrophe for an insurance company.


One of the most developed areas in recent years has been Reinforcement Learning. In RL, there is an agent that performs some actions in an environment and gets a reward for those actions. The agent gets a positive reward when the action is correct and a negative reward when the action is incorrect. In the long term, the agent will learn which sequences of actions drove it to the highest rewards, to repeat them, and which drove it to penalizations to avoid them. That’s a very similar approach to that of humans learning to ride a bicycle or mastering a sport. RL applications are ideal in scenarios where there is a clear goal to optimize such as energy consumption or traffic reduction, and there are millions of different actions that need to be sequentially taken to converge to the optimal solution.


RL dates back to the 80s but their applications were quite limited due to the available computer resources and the exponential growth of the Q-Table for complex applications. However, Deep Learning is able to approximate enormous Q-Tables with artificial neural networks catapulting RL to a new stage. The difference between RL and ML can be best appreciated with an example: Imagine that we want to create an algorithm able to play an online game. If we use supervised learning, we will record the movements of a professional and teach an algorithm to imitate such behaviour. The agent will be just as good as the professional with whom he is trained but in no case better than him.


RL has shown that an agent is able to play even better than a human simply by being trained with rewards playing against itself, as it happened in the GO game, the AlphaGo beat the world champion master Lee Sedol by 4 games to one. This was a great achievement in artificial intelligence since the number of combinations in GO, is greater than the number of atoms in the universe. One of the most successful RL applications has been that of Google in which the electricity bill of the data centres was reduced by 40%. Traditionally, games with imperfect information such as Poker, where uncertainty has to be effectively addressed, have remained as a challenge to Artificial intelligence for a long time. However, Deepstack an RL algorithm was able to defeat poker professional players at the famous game Texas hold’em.


Finally, although RL seems to be quite revolutionary, there are a few drawbacks that need to be solved which basically are the amount of time to find the optimal solution and the complexity of finding out a proper configuration of all the parameters involved in the program.