Overview
Leslie M. Collins earned the BSEE degree from the University of Kentucky, and the MSEE, and PhD degrees from the University of Michigan, Ann Arbor. From 1986 through 1990 she was a Senior Engineer at Westinghouse Research and Development Center in Pittsburgh, PA. She joined Duke in 1995 as an Assistant Professor and was promoted to Associate Professor in 2002 and to Professor in 2007. Her research interests include physics-based statistical signal processing, subsurface sensing, auditory prostheses and pattern recognition. She is a member of the Tau Beta Pi, Sigma Xi, and Eta Kappa Nu honor societies. Dr. Collins has been a member of the team formed to transition MURI-developed algorithms and hardware to the Army HSTAMIDS and GSTAMIDS landmine detection systems. She has been the principal investigator on research projects from ARO, NVESD, SERDP, ESTCP, NSF, and NIH. Dr. Collins was the PI on the DoD UXO Cleanup Project of the Year in 2000. As of 2015, Dr. Collins has graduated 15 PhD students.
Current Duke Appointments & Affiliations
Professor of Electrical and Computer Engineering
·
2007 - Present
Pierre R. Lamond Department of Electrical and Computer Engineering,
Pratt School of Engineering
Professor in the Department of Head and Neck Surgery & Communication Sciences
·
2021 - Present
Head and Neck Surgery & Communication Sciences,
Clinical Science Departments
Professor of Biomedical Engineering
·
2024 - Present
Biomedical Engineering,
Pratt School of Engineering
Faculty Network Member of the Duke Institute for Brain Sciences
·
2011 - Present
Duke Institute for Brain Sciences,
University Institutes and Centers
Recent Scholarly Works
When Centroids Mislead: Quantifying the Consequences of Sub-Optimally Aggregating Gridded Raster Data to Polygons
Journal article ISPRS International Journal of Geo Information · June 1, 2026 Point data, such as population, disease incidence, and greenhouse gas emissions, are commonly aggregated to a uniform grid of raster data for storage and representation. In many remote sensing applications, polygons are instead used to describe regions of ... Full text CiteAre deep learning models robust to partial object occlusion in visual recognition tasks?
Journal article Pattern Recognition · March 1, 2026 Image classification models, including convolutional neural networks (CNNs), perform well on a variety of classification tasks but struggle under conditions of partial occlusion of relevant objects. Methods to improve performance under occlusion, including ... Full text CiteA Data-Centric Analysis of the Impact of Training Data Quality vs. Quantity on P300 Brain-Computer Interface Performance (Student Abstract)
Conference Proceedings of the Aaai Conference on Artificial Intelligence · January 1, 2026 The current standard for training brain-computer interface (BCI) machine learning models is user-specific. There is a high interest in developing generic models that are trained on data from other users to minimize BCI calibration time; however, this is li ... Full text CiteRecent Grants
BREEZE: New Ventricular Direct Cooling Stylet to Mitigate Secondary Brain Injury
ResearchCo Investigator · Awarded by National Institutes of Health · 2022 - 2025InstaJam: every airman a sensor - jamming classification, localization, and visualization
ResearchCo-Principal Investigator · Awarded by InstaJam, LLC · 2023 - 2025Leveraging Natural Language Processing for Reverberant Speech Enhancement in Cochlear Implants
ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2023 - 2025View All Grants
Education
University of Michigan, Ann Arbor ·
1995
Ph.D.
University of Michigan, Ann Arbor ·
1986
M.Sc.Eng.
University of Kentucky ·
1985
B.S.E.