Hernando Ombao

Principal Investigator
Professor of Statistics​​​​​​​​​​​​​​​


​Hernando Ombao is Professor of Statistics and Principal Investigator of the KAUST Biostatistics Group. Hs main area of research is on developing statistical models and methods for analyzing high dimensional complex biological processes. At KAUST, he directs a group of researchers working on methods for brain signals and images using spectral analysis, time series analysis, functional data, state-space models and signal processing. His group actively collaborates with neuroscientists in modeling associations between neurophysiology, cognition and animal behavior.

Dr. Ombao is Fellow of the American Statistical Association. Prior to joining KAUST, he held faculty positions at the University of Pittsburgh, University of Illinois, Brown University and the University of California, Irvine. He serves Associate Editor for the Journal of the American Statistical Association. He held visiting scholar positions at various institutions including the Universite Catholique de Louvain (Belgium), Warwick University (UK), Centro de Investigaciones en Matematicas (Mexico), Universidad de Valladolid (Spain), University of the Philippines, Universiti Teknologi Malaysia. He is Co-Editor of the Handbook of Statistical Methods for Neuroimaging (CRC press, 2016). 

Research Interests

​High Dimensional Time series, Spectral Analysis, Functional Data Analysis, Forecasting, Modeling Brain Signals

Selected Publications

  • ​Cruz M*, Bender M and Ombao H. (2017). Robust Interrupted Time Series Models for Analyzing Complex Healthcare Interventions. Statistics in Medicine, In Press. This paper won an award in the JSM 2017 Student Paper Competition.
  • Fiecas M and Ombao H. (2016). Modeling the Evolution of Dynamic Brain Processes During an Associative Learning Experiment. Journal of the American Statistical Association, 111, 1440-1453.Ombao H, Lindqui
  • Ombao H, Lindquist M, Thompson W and Aston J. Handbook of Statistical Methods for NeuroImaging. CRC Press. 2016. ISBN 9781482220971
  • Yu Z*, Prado R, Burke E, Cramer S and Ombao H. (2016). A Hierarchical Bayesian Model for Studying the Impact of Stroke on Brain Motor Function. Journal of the American Statistical Association, 111, 549-563.
  • Kang H, Ombao H, Linkletter C, Long N and Badre D. (2012). Spatio-Spectral Mixed Effects Model for Functional Magnetic Resonance Imaging Data. Journal of the American Statistical Association, 107, 568-577. H. KANG Recipient of the 2011 ENAR John Van Ryzin Award for Best Paper​
​[* denotes PhD Student]


​PhD in Biostatistics, University of Michigan
MS in Statistics, University of California Davis
BS in Mathematics, University of the Philippines


  • ​​Mid-Career Dean’s Award for Research (2017) UC Irvine School of the Information and Computer Sciences
  • Elected Fellow, American Statistical Association (2016)
  • Grant on Studies on Signals and Images Via the Fourier Transform, NSF Division Mathematical Sciences, 2015-2018
  • Grant on Bayesian State-Space Models for Behavioral Time Series Data, NSF Division Social and Economic Sciences, 2014-2017
  • Grant on Localized Cross Spectral Analysis and Pattern Recognition in Non-Stationary Signals, NSF Division of Mathematical Sciences, 2004-2008​