Ricardo Henao

Henao

Assistant Professor in Biostatistics and Bioinformatics

Appointments and Affiliations

  • Assistant Professor in Biostatistics and Bioinformatics
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Member of Duke Center for Applied Genomics and Precision Medicine
  • Member of the Duke Clinical Research Institute

Contact Information

  • Office Location: 140 Science Drive, Durham, NC 27710
  • Office Phone: (919) 668-0647
  • Email Address: ricardo.henao@duke.edu

Education

  • Ph.D. Technical University of Denmark (Denmark), 2011

Courses Taught

  • COMPSCI 394: Research Independent Study
  • ECE 391: Projects in Electrical and Computer Engineering
  • ECE 891: Internship
  • ECE 899: Special Readings in Electrical Engineering
  • EGR 190: Special Topics in Engineering
  • EGR 393: Research Projects in Engineering
  • EGR 590: Special Topics in Engineering
  • POE 190: Practice Oriented Education

In the News

Representative Publications

  • Elliott Range, DD; Dov, D; Kovalsky, SZ; Henao, R; Carin, L; Cohen, J, Application of a machine learning algorithm to predict malignancy in thyroid cytopathology., Cancer Cytopathol, vol 128 no. 4 (2020), pp. 287-295 [10.1002/cncy.22238] [abs].
  • Xiu, Z; Tao, C; University, D; Henao, R, Variational learning of individual survival distributions, Acm Chil 2020 Proceedings of the 2020 Acm Conference on Health, Inference, and Learning (2020), pp. 10-18 [10.1145/3368555.3384454] [abs].
  • Chapfuwa, P; Li, C; Mehta, N; Carin, L; Henao, R, Survival cluster analysis, Acm Chil 2020 Proceedings of the 2020 Acm Conference on Health, Inference, and Learning (2020), pp. 60-68 [10.1145/3368555.3384465] [abs].
  • Shen, D; Zhang, X; Henao, R; Carin, L, Improved semantic-aware network embedding with fine-grained word alignment, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Emnlp 2018 (2020), pp. 1829-1838 [abs].
  • Draelos, RL; Dov, D; Mazurowski, MA; Lo, JY; Henao, R; Rubin, GD; Carin, L, Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes., Corr, vol abs/2002.04752 (2020) [abs].