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Program Affiliations

Biography

Hernando Ombao is a professor in the Statistics Program and the principal investigator of the Biostatistics Group at KAUST. His research focuses on developing time series models and novel data science methods for analyzing high-dimensional complex biological processes. He leads a group of researchers specializing in spectral and time-series analysis, functional data analysis, state-space models, and signal processing for brain signals and images. His group collaborates closely with neuroscientists to model the associations between neurophysiology, cognition and animal behavior.

Before joining KAUST, Professor Ombao was a tenured faculty member at the University of Illinois Urbana-Champaign, U.S., Brown University, U.S. and the University of California, Irvine, U.S. He earned a B.Sc. in Mathematics in 1989 from the University of the Philippines, an M.Sc. in Statistics in 1995 from the University of California, Irvine, and a Ph.D. in biostatistics in 1999 from the University of Michigan.

Ombao is an elected fellow of the American Statistical Association. He has been awarded several grants as a principal investigator by the U.S. National Science Foundation. In 2017, he received the UC Irvine School of Information Sciences Mid-Career Award for Research. He has served as a panel member of the Biostatistics Study Section at the U.S. National Institutes of Health and as an associate editor of leading statistical journals. He is co-editor of the book Handbook of Statistical Methods for Neuroimaging (CRC Press, 2016) and co-editor of a special issue of the Journal of Time Series Analysis.

At KAUST, he holds secondary appointments in the Applied Mathematics and Computational Sciences (AMCS) and the Bioengineering Programs. He also serves as chair of the Institutional Biosafety and Bioethics Committee. Ombao actively collaborates with researchers across the campus and is a co-founder of the interdisciplinary KAUST Neuro-AI Laboratory (NAIL).

Research Interests

Professor Ombao’s research focuses on the statistical modeling of time series data and the visualization of high-dimensional signals and images.


He has developed a coherent set of methods for modeling and inference on the dependence of complex brain signals: testing for differences in networks across patient groups, identifying biomarkers, classifying diseases based on networks and modeling associations between high-dimensional data from different domains, such as genetics, brain function and behavior.

Keyword tag icon
Longitudinal Models Mixed Effects Models Functional Magnetic Resonance Imaging Electroencephalograms

Education Profile

  • Ph.D. in Biostatistics, University of Michigan 1999

  • M.Sc. in Statistics, UC Davis, 1995

  • B.Sc. in Mathematics, University of the Philippines, Diliman, 1989

Awards and Recognitions

  • Mid-Career Dean’s Award for Research, UC Irvine School of the Information and Computer Sciences, 2017

  • Elected Fellow, American Statistical Association, 2016

  • Grant on Studies on Signals and Images Via the Fourier Transform, NSF Division Mathematical Sciences, 2015 - 2019

  • Grant on Bayesian State-Space Models for Behavioral Time Series Data, NSF Division Social and Economic Sciences, 2015 - 2017

  • Grant on Localized Cross Spectral Analysis and Pattern Recognition Methods for Non-Stationary Signals, NSF Division of Mathematical Sciences, 2004 - 2007

Publications

  • Ombao H, Fiecas M, Ting CM and Low YF. (2017+). Statistical Models for Brain Signals with Properties that Evolve Across Trials. NeuroImage, Accepted for publication.

  • Wang Y, Ombao H and Chung M. (2017+). Persistence Landscape with Application to Epileptic Seizure Encephalogram Data. Annals of Applied Statistics, Accepted for publication.

  • 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.

  • 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.

  • Ombao H, Lindquist M, Thompson W and Aston J. (2016). Handbook of Statistical Methods for NeuroImaging. CRC Press. ISBN 9781482220971

Research Areas