Leslie M Collins, Ph.D.

About

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

Education

• B.S.E. University of Kentucky, 1985
• M.Sc.Eng. University of Michigan, Ann Arbor, 1986
• Ph.D. University of Michigan, Ann Arbor, 1995
• Professor of Electrical and Computer Engineering
• Professor in the Department of Head and Neck Surgery & Communication Sciences
• Professor of Biomedical Engineering
• Faculty Network Member of the Duke Institute for Brain Sciences

Selected publications

• Markakis PJ, Malof JM, Collins L, Bradbury K. When Centroids Mislead: Quantifying the Consequences of Sub-Optimally Aggregating Gridded Raster Data to Polygons. ISPRS International Journal of Geo Information. 2026 Jun 1;15(6).
• Kassaw K, Luzi F, Collins LM, Malof JM. Are deep learning models robust to partial object occlusion in visual recognition tasks? Pattern Recognition. 2026 Mar 1;171.
• Gupta A, Liu A, Haines E, Alghamdi R, Kota AS, Collins LM, et al. A Data-Centric Analysis of the Impact of Training Data Quality vs. Quantity on P300 Brain-Computer Interface Performance (Student Abstract). In: Proceedings of the Aaai Conference on Artificial Intelligence. 2026. p. 41217u20139.
• Underwood EA, McKechnie T, Collins LM. Data-Efficient Deep Learning for SAR Automatic Target Recognition: A Comprehensive Survey. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2026 Jan 1;
• Lin R, Mo C, Shariff R, Zhang D, Alumar A, Kassaw K, et al. Assessing the Impact of Population Data Domain Differences on Transfer Learning in P300-based Brain-Computer Interfaces. In: Proceedings of the Aaai Conference on Artificial Intelligence. 2025. p. 29415u20137.
• Stump EA, Luzi F, Collins LM, Malof JM. Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer. In: Proceedings 2025 IEEE Winter Conference on Applications of Computer Vision Wacv 2025. 2025. p. 5460u20139.
• McKechnie IT, Collins LM, Malof JM. SIMPL Multi-Aspect MADD: Rapidly Generating Low-Cost Multi-Aspect Military Data for All-Domains at Scale. In: Proceedings of SPIE the International Society for Optical Engineering. 2025.
• Karpurapu A, Williams HA, DeBenedittis P, Baker CE, Ren S, Thomas MC, et al. Deep Learning Resolves Myovascular Dynamics in the Failing Human Heart. JACC Basic Transl Sci. 2024 May;9(5):674u201386.
• Shahidi LK, Collins LM, Mainsah BO. Objective intelligibility measurement of reverberant vocoded speech for normal-hearing listeners: Towards facilitating the development of speech enhancement algorithms for cochlear implants. The Journal of the Acoustical Society of America. 2024 Mar;155(3):2151u201368.
• Ren S, Luzi F, Lahrichi S, Kassaw K, Collins LM, Bradbury K, et al. Segment anything, from space? In: Proceedings 2024 IEEE Winter Conference on Applications of Computer Vision Wacv 2024. 2024. p. 8340u201350.