I graduated in Computer Sciences
with a focus on Mathematics and Process Control (Statistics, Modeling,
Simulation, and Optimization). My dream was to automate a process fully, receiving
real-time information, evaluating a model, and sending back control signals
optimized to achieve a predefined goal. I loved Mathematics, and Computer
Programming was the tool to fulfill my dream, but I ended up loving both.
As always, dreams are incomplete, and I discovered I needed to be closer to the hardware and cables. Therefore, I studied and applied embedded development and structured cabling. Years later, I learned how to integrate all the technology for intelligent buildings.
After gaining all that experience and working with many programming languages, databases, and technologies, I am focused on developing enterprise applications and on the latest technologies like Deep Learning to close the loop while controlling the process.
Even when I developed my Statistical packages and Neural Networks and used SPSS, SAS, and other traditional statistical packages, now there are better neural network architectures, many open-source tools and languages, innovative data visualizations, and social media requires processing unstructured data requiring Natural Language Processing.
So, I started learning about modern Data Mining, Business Intelligence tools like Tableau and PowerBI, Python and its applications to Deep Learning, Keras, TensorFlow, SystemML, Azure AI, IBM Watson, visualizations like those of D3js, and databases like Azure SQL with Polybase for distributed storage and processing of the big data. Currently, my wife and I are creating a Master's program for teaching Probability and Statistics, Machine Learning, Deep Learning, and related subjects with Python and a computational focus.