Research in AI

I am a Scientific Software Engineer at UofC (Population Genetics) and a PhD student in Intelligent Systems at UNED (Intelligent Motor Skills Learning).

I am interested in the applications of Artificial Intelligence in Human Motion Analysis and Bioinformatics.

Summary

I am a Computer Sciences Engineer from Madrid, Spain, with a MSc in Artificial Intelligence. Currently, I am studying a PhD in Intelligent Systems. I am interested in the applications of Artificial Intelligence and AR/VR/MR techniques to Human Motion Analysis and Bioinformatics. I am specialized in Applied Artificial Intelligence, Machine Learning, Algorithmics and Evolutionary Computation.

I am proficient in C/C++ and Python. I also have experience and knowledge in other programming languages like C#, Java, JavaScript, PHP, SQL, Prolog and Assembler. I like using the more appropriate programming language depending on the requisites of each project.

I am continuously updating myself reading papers and learning new concepts, pattern designs and paradigms. I love learning about new technologies and its applications in real life problems.

Artificial Intelligence

I have been focusing my career in the field of Artificial Intelligence since I started my Bachellor's Degree. Studying the fundations and different approaches followed in the four areas of Artificial Intelligence: i) perception and actuation, ii) knowledge representation, iii) automated reasoning, and iv) machine learning.

During my Master's and my PhD I have explored the different ways in which Artificial Intelligence techniques can be applied to the creation of Intelligent Tutoring Systems to learn Motor Skills.

Machine Learning

The AI area that I have studied the most is Machine Learning. I have experience applying different kinds of algorithms from the three main types of learning: i) Supervised Learning, ii) Unsupervised Learning and iii) Reinforcement Learning.

Some of the algorithms I am familiarized with are deep neural networks (FFN, 1D-CNN, 2D-CNN, LSTM, GRU...), Decission Trees, Random Forest, Support Vector Machines (SVM), k-NN, k-Means, Watkin's algorithms, deepRL, Genetic Algorithms and Neuroevolution.

Evolutionary Computation

I believe that Evolutionary Computation techniques are a good approach to find near-optimal solutions in many types of problems. Evolutionary Computation techniques can be defined as "guided random search" techniques due to its non-deterministic nature and the definition of fit functions to estimate the fitness of individuals in the population.

I am familiariez with the development and use of different Evolutionary Computation approaches, including Genetic Algorithms, Evolutionary Grammars, Genetic Programming, Prey-Predator Genetic Algorithms, Interactive Genetic Algorithms and Island Models.

Machine Learning

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