Luis De La Torre
Computer Science Instructor
- University of Puerto Rico, Mayagüez, PR. Ph.D. Computing and Information Sciences and Engineering. (2009)
- Dissertation Title: Scheduling divisible tasks under production or utilization constraints
- University of Puerto Rico, Mayagüez, PR. Master of Science (M.Sc.) in Scientific Computing. (2004)
- Universidad de Cartagena, Colombia. Bachelor of Science (B.S.) in Pure Mathematics. (2000)
- PI, Office of the President of the Ana G. Méndez University System. Proposal for Special Funds for Research Associate Vice-President, Cost Efficient and
Secure Resource Allocation of Data Intensive High Energy Physics Workflow. (2016) - Co-PI, Department of Defense, Defense University Research Instrumentation Program (DURIP), Award W911NF-14-1-0414 (2015-2016). CONFOCAL RAMANAFM-SNOM IMAGING SPECTROSCOPIC SYSTEM: An Initiative to Expand Materials Research in Puerto Rico. (2015)
- Mentor, AGMUS Institute of Mathematics, National Science of Foundation, Award 0822404. Mathematical Models for Parallel Secure Computing Algorithms. (2012-2014)
- Mentor, CCCE Caribbean Computing Center for Excellence, National Science of Foundation, Award 0940522. PHP Security Practices for Web Applications, Mathematics Model for 3-D Printing Human Body Parts. (2011-2014)
- Richard Tapia Celebration of Diversity in Computing Conference, San Francisco, CA. Travel Award. (2011)
- CAHSI Annual Meeting 2009, MOUNTAIN VIEW, CA. Travel Award. (2009)
- Computing and Information Sciences and Engineering Program, University of Puerto Rico, Mayaguez, PR. Graduate Research Fellow. (2008)
- Universidad de Cartagena, Colombia. Honor Student Fellow. (1998)
BOOK SERIES
1. Bhuiyan, T., Halappanavar, M., Friese, R. D., Medal H., De la Torre, L., Sathanur, A. V., Tallent, N. R., Stochastic Programming Approach for Resource Selection Under Demand Uncertainty. In: Klusáček D., Cirne W., Desai N. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2018. Lecture Notes in Computer Science, vol 11332. Springer, Cham, 2019.
2. Friese R.D., Halappanavar M., Sathanur A.V., Schram M., Kerbyson D.J., de la Torre L. (2018) Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties. In: Klusáček D., Cirne W., Desai N. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2017. Lecture Notes in Computer Science, vol 10773. Springer, Cham, 2018.
OFFICIAL REPORTS
1. De la Torre, L., and Halappanavar, M., Cost Efficient Resource Allocation for Multi-Cloud Computing. Computational & Statistical Analytics Group, 2017.
2. De la Torre, L., and Halappanavar, M., Cost Efficient Resource Allocation of Data Intensive high Energy Physics Workflows. Computational & Statistical Analytics Group, 2016.
3. De la Torre, L., and Halappanavar, M., Semi-Matchings Algorithms for Efficient Scheduling on Extreme-Scale Platforms. Computational & Statistical Analytics Group, 2015.