Data Science

The current Data Science environment is multimedia information-centric, where scientists seek to add data value by turning it into information. Unlike old epochs in which the information was centered only on physic phenomenon observations within eyes vision, today, we can arrive at distant stars and correlate the light of stars with water, ice, carbon, and other chemical substances. Furthermore, we can further expand our universe vision and the surrounding world due to data mining, Big Data analytics, and Deep Learning techniques.

The data science observations go from the subatomic scale as Bóson de Higgs to big-scale as a Black hole. Thus, the Big Data analytics techniques enable us to find a needle in the haystack. The datasets consist of large databases from different multimedia sources, where there is a clear need for standardization. Furthermore, the processing environment leads to hybrid Big Data platforms with distributed GPU/CPU design where the inference processing with AI algorithms becomes new other domains.

Our research focuses on distributed system development to fulfill the best computational resource requirement possible, considering Big Data frameworks, AI algorithms, and communication protocols.