Reinaldo Sanchez-Arias

Assistant Professor

Office Location

Innovation, Science, and Technology Building, 4700 Research Way, Room 2041, Lakeland, FL 33805



Ph.D. in Computational Science, The University of Texas at El Paso, 2013

B.S. in Mathematics, Universidad del Valle, Cali, Colombia, 2008


Social Media Accounts/Websites *

Personal website



Dr. Reinaldo (Rei) Sanchez-Arias joined Florida Poly in fall 2018 as an assistant professor in the Department of Data Science and Business Analytics. During his years studying for his doctorate at The University of Texas at El Paso, he was involved in research projects for the Army High Performance Computing Research Center (AHPCRC) in collaboration with a group at Stanford University. He interned at Repsol Oil Company USA during the summer of 2011, where he worked with the research and innovation geophysics department. While earning his doctorate, Sanchez-Arias worked on sparse representation methods for classification problems and dimensionality reduction. He completed a postdoctoral researcher appointment for the AHPCRC working in reduced order models for underbody-blast simulations and compression techniques. From 2014 to 2016, Sanchez-Arias was part of the applied mathematics department at Wentworth Institute of Technology (WIT) in Boston, Massachusetts, where he taught courses for applied mathematics and engineering students, nominated and served as the faculty advisor for the Society of Industrial and Applied Mathematics (SIAM) student chapter, coordinated linear algebra and capstone courses, and helped develop different elective courses in applied mathematics.

In fall 2016, Sanchez-Arias joined St. Thomas University, in Miami, where he served as the program director for the master’s degree in Big Data Analytics in the School of Science, advised and supervised graduate students, and revised curriculum for the computer science and mathematics undergraduate programs.

His general areas of interest include pattern recognition, computational linear algebra and optimization, data analysis, and operations research. Part of his latest research efforts has been focused on the design of fast algorithms for sparse representation problems, and their applications in data mining problems in science and engineering. His work has been presented at international and national conference meetings including SIAM meetings, the International Conference for High Performance Computing, and the International Conference of the Engineering in Medicine and Biology Society.



  • Numerical optimization
  • Data mining
  • Dimensionality reduction
  • Supervised learning methods
  • Linear and nonlinear programming


Professional Activities

  • Coordinator of the curriculum committee, Florida Poly Data Science and Business Analytics
  • Member, Research and development committee, Florida Poly Data Science and Business Analytics
  • Graduate advisor for master’s in analytics student


Awards and Honors

  • National Science Foundation CISE Proposal Writing Workshop, April 2018 (travel award)
  • Outstanding Ph.D. Dissertation Award, Computational Science Program, The University of Texas at El Paso, April 2014
  • Best Student Interval Paper Award, IFSA/NAFIPS Congress 2013, June 2013


Selected Publications

  • Bonavides-Aguilar C., Sanchez-Arias R., Lanzas C. “Accurate Prediction of Major Histocompatibility Complex Class II Epitopes by Sparse Representation via, I1-minimization.” In: BioData Mining, vol. 7, 2014.
  • Husowitz B., Sanchez-Arias R. “A Machine Learning Approach to Designing Guidelines for Acute Aquatic Toxicity.” In: J Biom Biostat, vol. 8, no. 6. 2017.
  • Sanchez R., Servin C., Argaez M. “Sparse Fuzzy Techniques Improve Machine Learning”. In: Joint World Congress of the International Fuzzy Systems Association and Annual Conference of the North American Fuzzy Information Processing Society IFSA/NAFIPS, pp. 531-535. 2013.
  • Sanchez R., Argaez M., Guillen P. “Sparse Representation via ¸1-minimization for Underdetermined Systems in Classification of Tumors with Gene Expression Data.” In: IEEE 33rd Annual International Conference Proceedings of the Engineering in Medicine and Biology Society, pp. 3362 – 3366. August 2011



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