He is currently a PhD student in Mathematics, where he is researching reduced order models and their applications to the thermophysiological and architectural field. She enjoys using the R language, although she also occasionally uses Python to design neural networks. Her specialty is data cleaning and processing using machine learning, areas in which she wants to continue developing.
Dedicated and diligent, with a keen interest in various aspects of artificial intelligence and its potential applications in the industry. I gained valuable experience as a software programmer during my internship at IR Solutions, and later transitioned to working in the research group. Currently, I specialize in weapon detection through CCTVs and BodyCams, having successfully implemented a weapon detection system that represents a significant advancement in this field.
Jose L. Salazar González received the degree in Computer Engineering and the Ph.D. degree in Computer Engineering from the University of Seville (Spain) in 2018 and 2023, respectively. He has been a researcher at the same university since October 2018. In his Ph.D. research, Jose innovated in indoor localization and firearm detection on CCTV, enhancing detection accuracy through supervised and semi-supervised learning. Currently, Jose is focusing his research efforts on Large Language Models (LLMs), exploring the applications and implications of these models in artificial intelligence.
Constantino is a curious individual, more interested in questions than in answers. He sees unanswered questions as an opportunity to explore and develop ideas. He enjoys approaching problems in a cross-disciplinary manner, especially relishing those that require more holistic approaches. This mindset led him to pursue degrees in engineering (UHU) and philosophy (UNED) before ultimately studying mathematics at the University of Seville (US). He views mathematics as ideal tools for addressing a wide range of problems, not only helping to solve them but also providing better solutions and refining ambiguities. His interests often lie at the intersections of disciplines. For instance, in 3D design, he finds a convergence of art, technology, and mathematics. This motivates him to work on projects such as Horus, a security-focused initiative where he primarily focuses on expanding datasets with artificial data by designing systems to generate 3D models procedurally. He also collaborates on socially impactful projects like Tal-IA, which has the potential to improve the quality of life for certain sectors of society.
Enrique J. López Ortiz received a degree in computer engineering from the University of Seville (Spain) in September 2019 and a master’s degree in logic, computation and artificial intelligence in October 2020. Since June 2020, he has been a researcher at the University of Seville (Spain) in the field of multi-agent systems and neural networks. He has been working on different projects at the University of Seville since 2019, and is currently a Ph.D. student at the same university. His Thesis is focus on Echo State Networks.
Marina Perea Trigo received a degree in Health Engineering and a master’s degree in Software Engineering from the University of Seville (Spain) in 2018 and 2019 respectively, and has been a researcher at the same university since December 2019. She worked from January 2019 to August 2019 for a company developing desktop applications. She is currently a PhD student collaborating on research projects on artificial intelligence for the University of Seville (Spain).
Miguel Ángel holds a degree in Computer Engineering from the University of Murcia (2008, average grade 8.24, a collaboration grant, and one year of Erasmus at NTNU), and a PhD in Computer Science from the University of Seville (2013, Cum Laude by Unanimity, and International Doctorate mention for a 3-month stay in 2011 at NTNU’s HPCLab). He is a member of the Research Group in Natural Computation of the University of Seville (PAI-TIC 193) since 2009, where he was a predoctoral fellow, and a postdoctoral fellow afterwards. In 2014 he was PI of the NVIDIA CUDA Research Center at the University of Seville, which only 3 Spanish universities held at the time. From December 2014 to August 2017, he moved to Fraunhofer IIS (Germany) first as ERCIM fellow and later as research associate. He is accredited as Profesor Contratado Doctor since 2014, and he is a Profesor Ayudante Doctor at the University of Seville since August 2017. He is currently a founding member of the SCORE-Lab Unit of Excellence (November 2020), an associate member of I3US (January 2021), a collaborating member of the DeepKnowledge group (April 2020), and a collaborating member of the ACLab and the Membrane Computing Research Group at the University of the Philippines Diliman (2011). He has been a member in organizing committees of international conferences such as HPCS2020, BICAS and BWMCs. He is the lead administrator for GPU computing severs in his group since 2008.
His main line of research is the application of GPU computing: parallel simulation of bio-inspired models (developing the first parallel simulators of P-systems with CUDA), application of Deep Learning for automatic video understanding (co-leading two ERCIM fellow researchers at Fraunhofer IIS), and acceleration of image compression codecs (JPEG2000 for digital cinema, participating in the easyDCP tool and its technical support with international customers, and standardization of the JPEGXS format for its parallelization, leading to the development of 9 patents). Currently, he is also collaborating in Deep Learning applications (sign language translation, video understanding, and biosignal processing), GPU-accelerated multi-agent simulation, robotics and virtual reality, among others. He has participated as a member of the research team in 7 national research projects, 3 regional projects, 2 international projects (China) and 3 contracts with public companies. He has published 32 articles in JCR indexed journals (6 Q1, 7 Q2, 6 Q3, 13 Q4), 8 in non-indexed journals, 55 communications in conferences, 9 patents, 4 book chapters, 6 edited conference proceedings, 1 special edited volume in an indexed journal, and 4 invited talks.
Since 2010, he has been teaching at the University of Seville, both at undergraduate and master’s level. He has supervised 10 master’s degree final projects, 4 bachelor’s degree final projects, 5 final projects and 2 practicum. In addition, he has participated as a support teacher in the Summer Science Campus at Andalucía Tech from 2011 to 2014 and 2018. In October 2018, he was appointed as a university ambassador for the NVIDIA Deep Learning Institute, organizing 6 workshops since then and participating as an assistant in 5 other workshops. His website https://www.cs.us.es/~mdelamor includes detailed information about his curriculum.
Since the beginning of my research life I have been interested in using mathematical tools as a means to model complex systems, which for the moment are resistant to being modelled by traditional means. In particular, I am interested in solving problems that require high computational resources to solve. Thus, my doctoral thesis was focused on the use of unconventional bio-inspired systems (mainly based on the use of DNA molecules or cells as basic units of calculation). Subsequently, and motivated by the type of limitations we found in modelling with these means, I focused on the mathematical study of complex networks as universal representation systems for highly complex systems and complex data, as well as the mathematical analysis and control of multi-agent systems formed by especially simple units (such as cellular automata).
In recent years, and always with the aim of better understanding complex systems through mathematical modelling, I have begun to make use of Machine Learning techniques for the modelling, analysis, prediction and control of complex dynamic systems and data obtained from them, with particular emphasis on those that have a very high semantic load that makes it difficult to process them by means of the most common data analysis techniques. It is therefore necessary to extend the methodological tools of complex networks in order to introduce a layer of measures and procedures capable of manipulating semantic information, taking advantage of the long-distance relationships established between their units. As a result of the methodologies developed, we have presented results with mathematical applications to fields traditionally alien to their techniques, with special attention to the study of systems of cultural objects, as well as the prediction of medical dynamics and mechanical engineering systems by using Deep Learning techniques.