Timothy Dunn

Research Themes

Biomedical & Health Data Sciences, Biomedical Imaging & Biophotonics, Computational Modeling of Biological Systems, Neural Engineering

Research Interests

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

Education

• Ph.D. Harvard University, 2015

Positions

• Assistant Professor of Biomedical Engineering
• Assistant Professor in Neurobiology
• Assistant Professor in the Department of Electrical and Computer Engineering
• Assistant Professor in Neurosurgery
• Member of the Center for Cognitive Neuroscience

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
• AI Watson XPrize Finalist (with team DataKind). IBM. 2017
• Certificate of Excellence in Teaching. Harvard University. 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

• EGR 393: Research Projects in Engineering
• ECE 899: Special Readings in Electrical Engineering
• ECE 493: Projects in Electrical and Computer Engineering
• BME 899: Special Readings in Biomedical Engineering
• BME 792: Continuation of Graduate Independent Study
• BME 791: Graduate Independent Study
• BME 789: Internship in Biomedical Engineering
• BME 590: Special Topics in Biomedical Engineering
• BME 580: An Introduction to Biomedical Data Science (GE)
• BME 494: Projects in Biomedical Engineering (GE)
• BME 493: Projects in Biomedical Engineering (GE)
• BME 394: Projects in Biomedical Engineering (GE)
• AIPI 591: Special Readings in AI for Product Innovation

Publications

• Kim K, Lyu S, Mantri S, Dunn TW. TULIP: Multi-Camera 3D Precision Assessment of Parkinson's Disease. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2024. p. 22551–62.
• Manjunath P, Lerner B, Dunn T. Towards Interactive and Interpretable Image Retrieval-Based Diagnosis: Enhancing Brain Tumor Classification with LLM Explanations and Latent Structure Preservation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2024. p. 335–49.
• Li T, Severson KS, Wang F, Dunn TW. Improved 3D Markerless Mouse Pose Estimation Using Temporal Semi-Supervision. Int J Comput Vis. 2023 Jun;131(6):1389–405.
• Thomson EE, Harfouche M, Kim K, Konda PC, Seitz CW, Cooke C, et al. Gigapixel imaging with a novel multi-camera array microscope. eLife. 2022 Dec;11:e74988.
• Adil SM, Charalambous LT, Rajkumar S, Seas A, Warman PI, Murphy KR, et al. Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation. Neurosurgery. 2022 Aug 1;91(2):272–9.
• Adil SM, Elahi C, Patel DN, Seas A, Warman PI, Fuller AT, et al. Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting. World Neurosurg. 2022 Aug;164:e8–16.
• Kirsch EP, Suarez A, McDaniel KE, Dharmapurikar R, Dunn T, Lad SP, et al. Construct validity of the Surgical Autonomy Program for the training of neurosurgical residents. Neurosurg Focus. 2022 Aug;53(2):E8.
• Satyadev N, Warman PI, Seas A, Kolls BJ, Haglund MM, Fuller AT, et al. Machine Learning for Predicting Discharge Disposition After Traumatic Brain Injury. Neurosurgery. 2022 Jun 1;90(6):768–74.
• Warman PI, Seas A, Satyadev N, Adil SM, Kolls BJ, Haglund MM, et al. Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries. Neurosurgery. 2022 May 1;90(5):605–12.
• Marshall JD, Li T, Wu JH, Dunn TW. Leaving flatland: Advances in 3D behavioral measurement. Current opinion in neurobiology. 2022 Apr;73:102522.
• Yao X, Pathak V, Xi H, Chaware A, Cooke C, Kim K, et al. Increasing a microscope's effective field of view via overlapped imaging and machine learning. Optics express. 2022 Jan;30(2):1745–61.
• Spears CA, Adil SM, Kolls BJ, Muhumza ME, Haglund MM, Fuller AT, et al. Surgical intervention and patient factors associated with poor outcomes in patients with traumatic brain injury at a tertiary care hospital in Uganda. J Neurosurg. 2021 Nov 1;135(5):1569–78.
• Dunn TW, Marshall JD, Severson KS, Aldarondo DE, Hildebrand DGC, Chettih SN, et al. Geometric deep learning enables 3D kinematic profiling across species and environments. Nat Methods. 2021 May;18(5):564–73.
• Adil SM, Elahi C, Gramer R, Spears CA, Fuller AT, Haglund MM, et al. Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda. J Neurotrauma. 2021 Apr 1;38(7):928–39.
• Marshall JD, Aldarondo DE, Dunn TW, Wang WL, Berman GJ, Ölveczky BP. Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire. Neuron. 2021 Feb;109(3):420-437.e8.
• Koltai DC, Dunn TW, Smith PJ, Sinha DD, Bobholz S, Kaddumukasa M, et al. Sociocultural determinants and patterns of healthcare utilization for epilepsy care in Uganda. Epilepsy Behav. 2021 Jan;114(Pt B):107304.
• Zhang L, Dunn T, Marshall JD, Ölveczky BP, Linderman S. Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model. In 2021.
• Yao X, Xi H, Zhou KC, Chaware A, Cooke C, Li Y, et al. Increasing a microscope’s effective field of view via overlapped imaging and machine learning. Optics InfoBase Conference Papers. 2021 Jan 1;
• Elahi C, Spears CA, Williams S, Dunn TW, Najjuma JN, Staton CA, et al. An Attitude Survey and Assessment of the Feasibility, Acceptability, and Usability of a Traumatic Brain Injury Decision Support Tool in Uganda. World Neurosurg. 2020 Jul;139:495–504.
• Dunn TW, Fitzgerald JE. Correcting for physical distortions in visual stimuli improves reproducibility in zebrafish neuroscience. eLife. 2020 Mar;9:e53684.
• Harfouche M, Dunn TW, Naumann EA, Horstmeyer R. Imaging the behavior and neural activity of freely moving organisms with a gigapixel microscope. In: Optics InfoBase Conference Papers. 2019.
• Dunn TW, Koo PK. Inferring Functional Neural Connectivity With Deep Residual Convolutional Neural Networks. bioRxiv. 2017 May 25;
• Naumann EA, Fitzgerald JE, Dunn TW, Rihel J, Sompolinsky H, Engert F. From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response. Cell. 2016 Nov 3;167(4):947-960.e20.
• Dunn TW, Mu Y, Narayan S, Randlett O, Naumann EA, Yang C-T, et al. Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion. Elife. 2016 Mar 22;5:e12741.
• Dunn TW, Gebhardt C, Naumann EA, Riegler C, Ahrens MB, Engert F, et al. Neural Circuits Underlying Visually Evoked Escapes in Larval Zebrafish. Neuron. 2016 Feb 3;89(3):613–28.
• Huang K-H, Ahrens MB, Dunn TW, Engert F. Spinal projection neurons control turning behaviors in zebrafish. Current biology : CB. 2013 Aug;23(16):1566–73.
• Kokel D, Dunn TW, Ahrens MB, Alshut R, Cheung CYJ, Saint-Amant L, et al. Identification of nonvisual photomotor response cells in the vertebrate hindbrain. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2013 Feb;33(9):3834–43.
• Fortin DL, Dunn TW, Fedorchak A, Allen D, Montpetit R, Banghart MR, et al. Optogenetic photochemical control of designer K+ channels in mammalian neurons. Journal of neurophysiology. 2011 Jul;106(1):488–96.
• Dunn TW, Fortin DL, Kramer RH. Engineering light-regulated ion channels. In: Imaging in Neuroscience A Laboratory Manual. 2011.
• Fortin DL, Banghart MR, Dunn TW, Borges K, Wagenaar DA, Gaudry Q, et al. Photochemical control of endogenous ion channels and cellular excitability. Nature methods. 2008 Apr;5(4):331–8.
• Dunn T. Brain-Wide Neural Dynamics Underlying Looming-Evoked Escapes and Spontaneous Exploration.

In The News

• Bass Connections Team Aims for Global Health Change (Jan 20, 2023 | Bass Connections)
• Welcome the Pratt School New Faculty (Oct 26, 2021 | Pratt School of Engineering)
• Timothy Dunn: Tracking Movement to Decipher Brain Activity (Jun 22, 2021 | Duke Engineering News)
• Learning to DANNCE (Apr 23, 2021 | Duke AI Health)
• Tim Dunn - The DANNCE Study (Apr 19, 2021 | Duke Forge)
• Neural Circuits at the Brain Scale (Nov 8, 2016 | Harvard MCB News)
• Calculating Time to Collision (Feb 3, 2016 | Harvard MCB News)