Summary
Degree in computer science by Instituto Tecnologico de Los Mochis (2008), Master degree in Science and information technologies by the Universidad Autónoma Metropolitana (2013), currently a PhD Student at the Universidad Autónoma de Sinaloa. PhD studies focused on building software applications infused with Machine Learning, also called the deployment task. More than 5 years of experience in the industry on software quality assurance and software development.
Research Interests
Automate the deployment task with a compiler that translates a machine learning model to software programming languages.
- Software Engineering + Machine Learning
- Deploy machine larning Models to production
- Fast scoring of Machine learning models using multiple architectures
- Data management and processing
Recent Publications
- 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. April 2019.
- 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.
Keywords
- Machine learning deployment
- R
- PMML
- Compilers