Education Profile

  • ​Postdoctoral Associate, Duke University, 2015
  • Postdoctoral Researcher, University of Copenhagen, 2011
  • PhD, Technical University of Denmark, 2011
  • MSc, Universidad Nacional de Colombia, 2004
  • BEng, Universidad Nacional de Colombia, 2002

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.

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).