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Joaquin Ortega

Instructional Professor, Statistics

Computer, Electrical and Mathematical Science and Engineering Division
Email icon joaquin.ortegasanchez@kaust.edu.sa

Dive into application tasks early on, ensuring a thorough understanding of theoretical foundations.

Program Affiliations

Biography

Prior to joining KAUST in 2019, Ortega Sánchez spent sixteen years working at the Mathematics Research Center (CIMAT) in Guanajuato, Mexico. He completed his postgraduate and graduate studies in London, where he studied mathematics at King’s College London before obtaining his Ph.D. in Probability Theory at Imperial College London. After his time in the U.K., Ortega Sánchez returned to his native Venezuela, where he worked for over 20 years at the Universidad Central de Venezuela and the Venezuelan Scientific Research Institute.

He has taught courses on stochastic models, time series, measure theory, advanced probability, extreme value theory, statistical consulting, functional data analysis, applied statistics, time series, and design of experiments. His career has seen him teach courses at several institutions worldwide, including the University of Paris-Sud, France, and the University of Valladolid, Spain.

Ortega’s primary role at KAUST is teaching statistics and providing additional mathematics support.

Research Interests

Throughout his career, Ortega’s research has focused on stochastic processes, specifically Gaussian processes and time series, with applications in oceanography and biostatistics. More recently, his work has focused on functional data analysis.
Keyword tag icon
Stochastic processes Gaussian processes functional data analysis

Education Profile

  • PhD, Imperial College London, 1979

  • MSc, King's College London, 1975

  • BSc, King's College London, 1974

Publications

  • Rivera-García, D., García-Escudero, L.A., Mayo-Iscar, A. & Ortega, J. Robust clustering for time series using spectral densities and functional data analysis. Advances in Computational Intelligence. Proceedings International Work-Conference on Artificial Neural Networks 2017, Lecture Notes in Computer Science Vol. 10306, pp 142-153, 2017.

  • Euán, C., Ombao, H. & Ortega, J. The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure. Journal of Classification 35(1): 71–99, 2018.

  • Euán, C., Ombao, H. & Ortega, J. Spectral Synchronicity in Brain Signals. Statistics in Medicine 37, 2855-2873, 2018, doi: https://doi.org/10.1002/sim.7695.

  • Rivera-García, D., García-Escudero, L.A., Mayo-Iscar, A. & Ortega, J. Time Series, Spectral Densities and Robust Functional Clustering. Neural Proc. Letters, 2018 https://doi.org/10.1007/s11063- 018-9926-1.

  • D. Rivera-García, L. A. García-Escudero, A. Mayo-Iscar, J. Ortega. Robust clustering for functional data based on trimming and constraints. Advances in Data Analysis and Classification (2019) 13: 201. https://doi.org/10.1007/s11634-018-0312-7