Hi there! In this section I mention some things about me and my career that may be interesting. I offer less detail about skills and libraries than what you can find on my resume but I explain in a little more detail my experience as a PhD student and university research assistant. If you’re interested in my profile or want to know more, you can find my contacts on the main page under my profile picture, or you can see my resume.
I studied Computer Science at Universidad de Córdoba (UCO) obtaining a BSc in 2018, and then obtained a Data Science MSc at Universidad de Granada (UGR) in 2019. I started working as a research assistant at UCO in 2018 while working on my BSc thesis. This same thesis was used as the basis for my MSc thesis, where it got improvements to better take into consideration the underlying ordinal nature of the data. This work is further explained on the MSc thesis section of this web.
During my BSc I took all the machine learning classes available, and it’s here where I got a very sturdy foundation on the mathematical and statistical basis of ML. On my MSc I took electives such as: Big Data, Recommendation Systems, Time Series, Computer Vision, Non-Supervised Learning and Anomaly Detection.
As mentioned earlier, I started working as a research assistant in early 2018 while finishing my BSc thesis in which I used ordinal classification with a ResNet model to predict the age of people from face pictures. I then continued this work on my MSc thesis adding new ordinal ordinal loss functions among other improvements. You can see a summary of this work and an exposition of the results obtained and the conclusions following the link.
I then pursued a PhD to continue my research on Deep Learning methods. From late 2019 to late 2021 I was a PhD student at Universidad de Alcalá de Henares (UAH). During this time my main research line was to apply ML and DL models to meteorological problems for tasks such as:
For this I used classification, regression and unsupervised methods. In the methodological area the main point was to use Deep Learning methods in areas where it would be a novelty. Another edge of the research was to employ metaheuristics for the models’ architecture, and ordinal classification where applicable. Usually the amount of data available in these databases that I had access to wasn’t enough to train Deep Learning models; in general there were no images, and when they were available, there weren’t enough to train DNNs. Nonetheless with that same data where DL could not be applied to I applied traditional ML models. From this work two publications have been made. After some time I started to realize that I didn’t really like university research and that most likely I wasn’t going to stay in academia after finishing my PhD, so I thought that the best thing I could do was moving to the industry sector.
I will briefly list some of my most relevant skills in the following table:
| Data Science Languages: | Python, R, SQL |
| General Purpouse Languages: | C, C++, bash |
| Machine Learning Libraries: | scikit-learn, keras, tensorflow, PyTorch |
| Data Science Libraries: | NumPy, Pandas, ggplot, seaborn, matplotlib, tidyverse |
| Big Data Libraries/Tools: | MongoDB, MLlib, Spark |
I’ve worked primarily with Python and R in data science and machine learning projects. The libraries that I use the most are: sklearn, NumPy, Pandas and ggplot, although I’ve worked on projects with all of the ones listed, and I’ve had a good knowledge of them in the last two years. I’ve also worked with Big Data libraries such as Spark and MLlib, but my knowledge in this area is more limited than in “classical” ML and DL. If you want to know more about my experience, I can send you my resume. You can find my contacts on the main page under my profile picture.
I also know C and C++, and even if I quite like the languages (seriously, I do) I do not use them often now and I’m a little rusty. I am also proficient in bash. I love to use the CL for everything that I can in Linux and macOS, which are my main OSs. Thanks to my college background I’m proficient in programming, and have a solid background in maths, mainly linear algebra and statistics, which are the building blocks of ML.
As for hobbies in my free time I love first and foremost everything and anything music-related: listening to music or playing guitar, bass or keyboards; I also love reading and sports, like biking and taking long walks and hikes in nature. I like tracking my music listening with last.fm which I started using way back in 2007. I stopped using last.fm when I got an iPod, and started using it again when I ditched my trusty ol’ iPod classic for streaming services, which are (IMHO) way more convenient in any aspect imaginable.