Research

Below you can see my current list of manuscripts and presentations. I am interested in using transfer learning and machine learning to address health care problems.

Publications

  1. J Hickey, J P Williams, E C Hector (2022+). Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST). arXiv In Review

  2. C Hong, M Liu, D M Wojdyla, J Hickey, M Pencina, R Henao (2023). Trans-Balance: Reducing Demographic Disparity for Prediction Models in the Presence of Class Imbalance. To appear in The Journal of Biomedical Informatics

  3. J Hickey, R Henao, M Pencina, D M Wojdyla, M Engelhard (2023+). Improving Event Time Prediction by Learning to Partition the Timeline. arXiv In Review

Presentations

  1. Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST). Joint Statistical Meeting Oral Presentation. August 2023

  2. Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST). North Carolina State University Graduate Research Symposium Poster Presentation. April 2023

  3. Trans-Balance: Reducing Demographic Disparity for Prediction Models in the Presence of Class Imbalance. Duke University Oral Presentation. April 2023

  4. Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST). ENAR Poster Presentation. March 2023

  5. Improving Event Time Prediction by Learning to Partition the Timeline. Duke University Oral Presentation. March 2023

  6. Improving Event Time Prediction by Learning to Partition the Timeline. North Carolina State University Oral Seminar. September 2022

  7. Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST). Joint Statistical Meeting Poster Presentation. August 2022