Ricardo Henao

Associate Professor in Biostatistics & Bioinformatics

Appointments and Affiliations

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

Contact Information

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


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

Courses Taught

  • ECE 392: Projects in Electrical and Computer Engineering
  • ECE 493: Projects in Electrical and Computer Engineering
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 891: Internship
  • ECE 899: Special Readings in Electrical Engineering
  • EGR 393: Research Projects in Engineering
  • IDS 793: Independent Study

In the News

Representative Publications

  • Pavon, Juliessa M., Laura Previll, Myung Woo, Ricardo Henao, Mary Solomon, Ursula Rogers, Andrew Olson, et al. “Machine learning functional impairment classification with electronic health record data.” J Am Geriatr Soc, May 17, 2023. https://doi.org/10.1111/jgs.18383.
  • Chapfuwa, Paidamoyo, Chenyang Tao, Chunyuan Li, Irfan Khan, Karen J. Chandross, Michael J. Pencina, Lawrence Carin, and Ricardo Henao. “Calibration and Uncertainty in Neural Time-to-Event Modeling.” Ieee Trans Neural Netw Learn Syst 34, no. 4 (April 2023): 1666–80. https://doi.org/10.1109/TNNLS.2020.3029631.
  • Bai, Ke, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, and Lawrence Carin. “Open World Classification with Adaptive Negative Samples,” March 9, 2023.
  • Wang, Rui, Pengyu Cheng, and Ricardo Henao. “Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Sampling,” February 25, 2023.
  • Assaad, Serge, David Dov, Richard Davis, Shahar Kovalsky, Walter T. Lee, Russel Kahmke, Daniel Rocke, et al. “Thyroid Cytopathology Cancer Diagnosis from Smartphone Images Using Machine Learning.” Mod Pathol 36, no. 6 (February 13, 2023): 100129. https://doi.org/10.1016/j.modpat.2023.100129.