Timothy Dunn

Assistant Professor of Biomedical Engineering

Appointments and Affiliations

  • Assistant Professor of Biomedical Engineering
  • Assistant Professor in Neurosurgery

Contact Information

Education

  • Ph.D. Harvard University , 2015

Research Interests

Machine learning, computer vision, neurobiology, animal behavior, computational neuroscience, prognostic modeling, traumatic brain injury

Awards, Honors, and Distinctions

  • Technological Innovations in Neuroscience Award. McKnight Foundation. 2021
  • Peer Recognition Honor. Duke, Pratt School of Engineering. 2020
  • Certificate of Distinction in Teaching. Harvard University . 2017
  • Certificate of Excellence in Teaching. Harvard University. 2017
  • AI Watson XPrize Finalist (with team DataKind). IBM. 2017
  • Certificate of Distinction in Teaching. Harvard University . 2013
  • Graduate Research Fellowship. National Science Foundation . 2010
  • I.L. Chaikoff Award for Undergraduate Research. UC Berkeley . 2008
  • MCB Department Citation (Best in Class). UC Berkeley. 2008

Courses Taught

  • BME 394: Projects in Biomedical Engineering (GE)
  • BME 493: Projects in Biomedical Engineering (GE)
  • BME 590: Special Topics in Biomedical Engineering
  • BME 789: Internship in Biomedical Engineering
  • BME 791: Graduate Independent Study
  • BME 792: Continuation of Graduate Independent Study
  • BME 899: Special Readings in Biomedical Engineering
  • ECE 899: Special Readings in Electrical Engineering
  • EGR 393: Research Projects in Engineering

In the News

Representative Publications

  • Li, Tianqing, Kyle S. Severson, Fan Wang, and Timothy W. Dunn. “Improved 3D Markerless Mouse Pose Estimation Using Temporal Semi-Supervision.” Int J Comput Vis 131, no. 6 (June 2023): 1389–1405. https://doi.org/10.1007/s11263-023-01756-3.
  • Thomson, Eric E., Mark Harfouche, Kanghyun Kim, Pavan C. Konda, Catherine W. Seitz, Colin Cooke, Shiqi Xu, et al. “Gigapixel imaging with a novel multi-camera array microscope.” Elife 11 (December 14, 2022). https://doi.org/10.7554/eLife.74988.
  • Adil, Syed M., Lefko T. Charalambous, Shashank Rajkumar, Andreas Seas, Pranav I. Warman, Kelly R. Murphy, Shervin Rahimpour, et al. “Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation.” Neurosurgery 91, no. 2 (August 1, 2022): 272–79. https://doi.org/10.1227/neu.0000000000001969.
  • Adil, Syed M., Cyrus Elahi, Dev N. Patel, Andreas Seas, Pranav I. Warman, Anthony T. Fuller, Michael M. Haglund, and Timothy W. Dunn. “Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.” World Neurosurg 164 (August 2022): e8–16. https://doi.org/10.1016/j.wneu.2022.02.097.
  • Kirsch, Elayna P., Alexander Suarez, Katherine E. McDaniel, Rajeev Dharmapurikar, Timothy Dunn, Shivanand P. Lad, and Michael M. Haglund. “Construct validity of the Surgical Autonomy Program for the training of neurosurgical residents.” Neurosurg Focus 53, no. 2 (August 2022): E8. https://doi.org/10.3171/2022.5.FOCUS22166.