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
Professor Ombao’s research interest is in the statistical modeling of time series data and visualization of high dimensional signals and images. He has developed a coherent set of methods for modeling and inference on dependence in complex brain signals; testing for differences in networks across patient groups; biomarker identification and disease classification based on networks and in modeling association between high dimensional data from different domains (e.g., genetics, brain function and behavior).
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