Alvaro Calle Cordon, Ph.D.

Alvaro Calle Cordon, Ph.D., has diverse work experience in various roles and industries. Alvaro is currently serving as the Data Scientist Lead and Artificial Intelligence Consultant at QUANT AI Lab since June 2022. Prior to this, they worked as an Adjunct Professor at IE School of Human Sciences and Technology (HST) from September 2020.

Alvaro also worked as a Data Scientist at StratioBD from October 2019 to May 2022. Before that, they worked as a Senior Data Scientist at CAF (Construcciones y Auxiliar de Ferrocarriles) from October 2017 to October 2019. Alvaro joined CAF during their Digital Services initiative, which resulted in the creation of their platform LeadMind.

Alvaro's work experience also includes their role as a Mathematician at CEMOSA from March 2016 to October 2017. Alvaro was involved in several Horizon 2020 projects focused on subjects such as Data Storage & Management, Machine Learning, Predictive Maintenance, and Reliability Analysis in the field of Civil Engineering, particularly railway and road infrastructures.

In addition, Alvaro has served as an Adjunct Professor of Physics at Universidad de Málaga from October 2015 to March 2016, teaching various physics courses in Engineering degrees. Alvaro also worked as a Research Scientist at Universidad de Murcia from November 2013 to December 2014, where they conducted research on theoretical physics, specifically studying fundamental properties of light nuclei.

Alvaro's career began as a Postdoctoral researcher in theoretical nuclear and particle physics at Thomas Jefferson National Accelerator Facility from January 2011 to November 2013. Alvaro focused on computing baryon properties and performing statistical analyses of LQCD data using various programming languages.

Lastly, Alvaro completed their Ph.D. in physics and mathematics at Universidad de Granada from September 2006 to September 2010. Alvaro received a fellowship through the Spanish Research Training Program FPI Grant and co-authored articles published in peer-reviewed international scientific journals.

Overall, Alvaro Calle Cordon, Ph.D., has extensive experience in data science, artificial intelligence, teaching, and research in various fields, including physics, mathematics, and civil engineering.

Alvaro Calle Cordon, Ph.D. has a diverse education history. Alvaro started their academic journey in 1998 at Universidad de Granada, where they pursued a Bachelor's degree in Physics and completed it in 2005. In 2006, they furthered their studies at the same university and obtained a Master of Science (MS) degree in Physics and Mathematics, which took him until 2008.

Building on their previous accomplishments, they continued their education at Universidad de Granada and completed a Doctor of Philosophy (Ph.D.) degree in Physics in 2010. Seeking additional expertise, Alvaro pursued a Master's degree in Statistical Learning & Data Mining from Universidad Nacional de Educación a Distancia (U.N.E.D.) from 2014 to 2015.

In 2017, Alvaro Calle Cordon decided to explore a different field and enrolled at WorldQuant University. Alvaro successfully earned a Master's Degree in Financial Engineering in 2019, further diversifying their educational background.

Additionally, Alvaro has acquired several certifications throughout their career. Alvaro received certifications such as "Specialization - DeepLearning.AI TensorFlow Developer" from Coursera Course Certificates in January 2021, "NVIDIA DLI Certificate - Fundamentals of Accelerated Data Science with RAPIDS" from NVIDIA in May 2020, and "Master of Science in Financial Engineering" from WorldQuant University in April 2020. A few of their other certifications include "Specialization Certificate - Deep Learning" from Coursera Course Certificates in August 2018, "Data Manipulation at Scale: Systems and Algorithms" from Coursera Course Certificates in December 2015, and "Certificate in Advanced English (CAE)" from Cambridge English Language Assessment in June 2015.

Alvaro's educational journey showcases their dedication to continuous learning and their interest in various fields such as Physics, Mathematics, Statistical Learning, Data Mining, Financial Engineering, and Deep Learning.