Dennis L. Barbour, M.D., Ph.D.

Associate Professor
Biomedical Engineering

Neurosciences Program
Biomedical Informatics and Data Science Program

  • 314-935-7548

  • 314-935-5083

  • 314-935-7448

  • 200E Whitaker



  • perception and cognition, behavioral assessment, medical informatics, computational audiology, machine learning, clinical decision support

  • Sensation, perception and cognitive processing of sounds and speech

Research Abstract:

Dennis Barbour’s research interests include auditory processing, cognitive neuroscience, machine learning and medical informatics. Most recently he has developed new machine learning methods for rapidly and thoroughly evaluating perception and cognition. These new tests are not only useful for exploring normal nervous system function, but also for diagnosing disorders. The principles contributing to these successful diagnostics may also lead to effective neurotherapeutics, alone or when coupled with drug or device therapies.

Mentorship and Commitment to Diversity Statement:
Research efforts in support of underserved groups, and inclusion of individuals from those groups in the research enterprise, are both high priorities of mine. A key organizing principle of the lab is that prioritization of individualized scientific and medical inference will benefit all, especially individuals poorly served by current group-centric paradigms. Design for equity takes center stage for projects in the lab, with input from key stakeholders actively sought. I aim to create a safe and supportive workplace, providing equitable guidance for the career development of all lab members. Given my own narrow intersection of identities, I am open to education and correction as needed to improve my stewardship of the above principles.

Selected Publications:

Chesley B, Barbour DL. “Visual field estimation by probabilistic classification” IEEE J Biomed Health Inform, 24(12):3499-3506, 2020.

Barbour DL, DiLorenzo JC, Sukesan KA, Song XS, Chen JY, Degen EA, Heisey KL, Garnett R. “Conjoint psychometric field estimation for bilateral audiometry.” Behav Res Meth. 51(3):1271-1285, 2019.

Sun W, Barbour DL. Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex. PLoS Biology, 15(11):e2002459, 2017.

Gardner JR, Malkomes G, Cunningham JP, Barbour DL, Garnett R. Bayesian active model selection with an application to automated audiometry. Adv Neural Inf Proc Sys, 2377-2385, 2015.

XD Song, Wallace BM, Gardner JR, Ledbetter NM, Weinberger KQ, Barbour DL. Fast, continuous audiogram estimation using machine learning. Ear and Hearing 36(6):e326-35, 2015.

Watkins PV, Barbour DL. Specialized neuronal adaptation for preserving input sensitivity. Nat Neurosci. 2008 Nov;11(11): 1259-61.

Last Updated: 6/2/2022 5:05:06 PM

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