​Ricardo Henao, a quantitative scientist, is an Associate Professor of Bioengineering in the Biological and Environmental Science and Engineering (BESE) Division, and Computer Science in the Computer, Electrical, Mathematical Sciences and Engineering Division (CEMSE), member of the Smart Health Initiative (SHI) and the Computational Biosciences Research center (CBRC), at KAUST (King Abdullah University of Science and Technology). He is also currently an Associate Professor in the department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering (ECE), member of the Information Initiative at Duke (iiD) and the Duke Clinical Research Institute (DCRI), all at Duke University.

Building 4 3334

808 2225

4700 King Abdullah University of Science and Technology, Building 4 Room 3334
Thuwal 23955 - 6900, KSA

Research Interests

​The theme of Professor Henao's research is the development of novel statistical methods and machine learning algorithms primarily based on probabilistic modeling. His expertise covers several fields including applied statistics, signal processing, pattern recognition and machine learning. His methods research focuses on hierarchical or multilayer probabilistic models to describe complex data, such as that characterized by high-dimensions, multiple modalities, more variables than observations, noisy measurements, missing values, time-series, multiple modalities, etc., in terms of low-dimensional representations for the purposes of hypothesis generation and improved predictive modeling. 

Most of his applied work is dedicated to the analysis of biological data such as gene expression, medical imaging, clinical narrative, and electronic health records. His recent work has been focused on the development of sophisticated machine learning models, including deep learning approaches, for the analysis and interpretation of clinical and biological data with applications to predictive modeling for diverse clinical outcomes.


  • Duke University (2011-2015), Postdoctoral Associate
  • University of Copenhagen (2011), Postdoctoral Researcher
  • Technical University of Denmark (2008-2011), Ph.D.
  • Universidad Nacional de Colombia (2002-2004), M.Sc.
  • Universidad Nacional de Colombia (1997-2002), B.Eng.

Selected Publications

  • Wang, R., Yu, T., Zhao, H., Kim, S., Mitra, S., Zhang, R. & Henao, R. Few-Shot Class-Incremental Learning|for Named Entity Recognition in Proceedings of the 60th Annual Meeting of the Association for Computational|Linguistics (Volume 1: Long Papers) (2022), 571-582.
  • Kong, F. & Henao, R. Efficient Classification of Very Large Images with Tiny Objects in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022).
  • Chapfuwa, P., Tao, C., Li, C., Page, C., Goldstein, B., Carin, L. & Henao, R. Adversarial time-to-event modeling in Proceedings of the 35th International Conference on Machine Learning (2018).
  • Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A. & Carin, L. Variational Autoencoder for Deep Learning|of Images, Labels and Captions in Advances in Neural Information Processing Systems 29 (2016).
  • Tsalik, E. L., Henao, R., Nichols, M., Burke, T., Ko, E. R., McClain, M. T., Hudson, L. L., Mazur, A., Freeman,|D. H., Veldman, T., et al. Host gene expression classifiers diagnose acute respiratory illness etiology. Science|Translational Medicine 8 (2016).