Luis J. Mena obtained a doctorate in computer science in 2008 at the National Institute of Astrophysics, Optics and Electronics (Mexico). He also has a master's degree in applied computing and a bachelor's degree in computing at the University of Zulia (Venezuela). He currently works as a full-time Professor
at the Polytechnic University of Sinaloa, and is the leader of the Consolidated Academic Group of Information Technology and Applied Communications. He is a level II national researcher of Mexico in the engineering area and an honorary researcher of the System of Researchers and Technologist of Sinaloa. Among his main scientific findings are the development of an algorithm to measure the variability of blood pressure, which has allowed opening new fields of research regarding the clinical value of this phenomenon, and a symbolic binary classification algorithm to extract patterns from unbalanced clinical data sets. In addition, he has published more than 90 peer-reviewed articles in prestigious journals and conference proceedings. His main research interests include pattern recognition for medical diagnosis and prognosis, as well as the development of mobile computing systems for personal health monitoring.
Research Keywords & Expertise
Data Mining
machine learning
Pattern Recognition
medical diagnosis
Unbalanced data
Fingerprints
24%
machine learning
5%
Unbalanced data
Short Biography
Luis J. Mena obtained a doctorate in computer science in 2008 at the National Institute of Astrophysics, Optics and Electronics (Mexico). He also has a master's degree in applied computing and a bachelor's degree in computing at the University of Zulia (Venezuela). He currently works as a full-time Professor
at the Polytechnic University of Sinaloa, and is the leader of the Consolidated Academic Group of Information Technology and Applied Communications. He is a level II national researcher of Mexico in the engineering area and an honorary researcher of the System of Researchers and Technologist of Sinaloa. Among his main scientific findings are the development of an algorithm to measure the variability of blood pressure, which has allowed opening new fields of research regarding the clinical value of this phenomenon, and a symbolic binary classification algorithm to extract patterns from unbalanced clinical data sets. In addition, he has published more than 90 peer-reviewed articles in prestigious journals and conference proceedings. His main research interests include pattern recognition for medical diagnosis and prognosis, as well as the development of mobile computing systems for personal health monitoring.