Daniel Buckland

Buckland

Assistant Professor of Emergency Medicine

Appointments and Affiliations

  • Assistant Professor of Emergency Medicine
  • Assistant Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science

Contact Information

  • Office Location: 2301 Erwin Road, DUMC Box 3096, Durham, NC 27710
  • Office Phone: (919) 660-6553
  • Email Address: dan.buckland@duke.edu

Education

  • M.D. Harvard Medical School, 2014
  • Ph.D. Massachusetts Institute of Technology, 2011
  • M.S. Massachusetts Institute of Technology, 2006

Courses Taught

  • ME 392: Undergraduate Projects in Mechanical Engineering
  • ME 394: Engineering Undergraduate Fellows Projects
  • ME 492: Special Projects in Mechanical Engineering
  • ME 591: Research Independent Study in Mechanical Engineering or Material Science
  • ME 592: Research Independent Study in Mechanical Engineering or Material Science

In the News

Representative Publications

  • Lam, SSW; Zaribafzadeh, H; Ang, BY; Webster, W; Buckland, D; Mantyh, C; Tan, HK, Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study., Healthcare (Basel, Switzerland), vol 10 no. 7 (2022) [10.3390/healthcare10071191] [abs].
  • Antonsen, EL; Myers, JG; Boley, L; Arellano, J; Kerstman, E; Kadwa, B; Buckland, DM; Van Baalen, M, Estimating medical risk in human spaceflight., Npj Microgravity, vol 8 no. 1 (2022) [10.1038/s41526-022-00193-9] [abs].
  • Oca, SR; Havas, J; Bridgeman, LJ; Buckland, DM, Durable Breast Phantom with Geometric and Mechanical Properties approximating Human Tissue for Ultrasound Image and Robotic System Testing, 2022 International Symposium on Medical Robotics, Ismr 2022 (2022) [10.1109/ISMR48347.2022.9807523] [abs].
  • Buckland, D; Parisi, M; Mctigue, K; Wu, S-C; Panontin, T; Vos, G; Petersen, D; Vera, A, NASA’s Identified Risks of Adverse Outcomes Due to Inadequate Human Systems Integration Architecture in Human Spaceflight, Human Centered Aerospace Systems and Sustainability Applications (2022) [10.54941/ahfe1001427] [abs].
  • Fenn, A; Davis, C; Buckland, DM; Kapadia, N; Nichols, M; Gao, M; Knechtle, W; Balu, S; Sendak, M; Theiling, BJ, Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units., Annals of Emergency Medicine, vol 78 no. 2 (2021), pp. 290-302 [10.1016/j.annemergmed.2021.02.029] [abs].