Foto

Ines F. Vega-Lopez

Director: Data Science Research Group
Email: ifvega at uas dot edu dot mx

Summary

He received the BE degree in computer Systems from Monterrey Institute of Technology (Tec de Monterrey), México in 1994, and the MS and PhD degrees in computer science from The University of Arizona in 1999, and 2004, respectively. He is a professor of computer science at the Universidad Autonoma de Sinaloa, Mexico. He is a member of the IEEE and ACM.

Research Interests
  • Knowledge Discovery from Data, Data Mining, Machine Learning and Data Science.
  • Massive Data Processing (Big Data) and High Performance Computing.
  • Data Engineering for the IoT.
  • Bioinformatics and Computational Biology.
Recent Publications

  • Maria E. Baez-Flores, Jose A. Magaña-Lizarraga, Jesus R. Parra-Unda, Yesmi P. Ahumada-Santos, Magdalena J. Uribe-Beltran, Bruno Gomez-Gil, Ines F. Vega-Lopez, and Rogelio Prieto-Alvarado. Whole-genome sequencing of Staphylococcus aureus L401, a mecA-negative community-associated methicillin-resistant strain isolated from a healthy carrier. Journal of Global Antimicrobial Resistance. 2019. April 2019.
  • Oscar Castro-Lopez, Daniel E. Lopez-Barron, and Ines F. Vega-Lopez. Next-Generation Heartbeat Classification with a Column-Store DBMS and UDFs. Journal of Intelligent Information Systems. Springer. DOI 10.1007/s10844-019-00557-w. 2019. To appear.
  • Oscar Castro-Lopez and Ines F. Vega-Lopez. Multi-target Compiler for the Deployment of Machine Learning Models. In Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO 2019). IEEE Press, Piscataway, NJ, USA, 280-281. ISBN: 978-1-7281-1436-1
  • Oscar Castro-Lopez and Ines F. Vega-Lopez. Fast Deployment and Scoring of Support Vector Machine Models in CPU and GPU. In Proceedings of the 1st International Workshop on Machine Learning and Software Engineering Symbiosis. Pp 45-52. Montpellier, France. September 2018. ISBN 978-1-4503-5972-6
  • Oscar Castro-Lopez and Ines F. Vega-Lopez. ML2ESC: A Source Code Generator to Embed Machine Learning Models in Production Environments. In Proceedings of the 14th International Conference on Data Science. Pp 70-73. Las Vegas, USA. August 2018. ISBN: 1-60132-481-2
  • Oscar Castro-Lopez and Ines F. Vega-Lopez. Glm.deploy: ‘C’ and ‘Java’ Source Code Generator for Fitted Glm Objects. URL https://cran.r-project.org/package=glm.deploy, r package version 1.0.4. March, 2018.

Current Projects

  • Using Deep Learning for the Identification of Plant Species in the Mexican Flora from Images. Funded by the Mexican Council for Science and Technology (CONACyT). Co-PI. 2018 – 2020.
  • Deep Neural Networks for the Classification of “Street-view” Images. Funded by the Mexican Council for Science and Technology (CONACyT). 2018 – 2020

Students

Doctorate

  • Oscar Castro-López
  • Jose R. Aguirre-Sanchez
Master
  • Zuriel Morales Casas
  • Eduardo Díaz-Gaxiola
Former students
  • Pavel A. Alvarez-Carrillo, PhD. Currently a professor at Universidad Autonoma de Occidente
  • Oswaldo Cuen-Tellez, PhD. Currently a professor at Instituto Tecnologico de Culiacan
  • Daniel E. López-Barron, PhD Student at University of Missouri-Kansas City
  • Luis E. Ibarra-Cazares. Currently a Data Scientist for Coppel
  • Vilma Sanchez-Lopez. Currently a Data Scientist for Coppel

Contact info

ifvega at uas dot edu dot mx
Universidad Autonoma de Sinaloa, Parque de Innovación Tecnologica
Av. Josefa Ortíz SN, Ciudad Universitaria, Culiacán, Sin. 80013
+52 (667) 758 1424