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Leslie M. Collins
Professor of Electrical and Computer Engineering
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.
Appointments and Affiliations
- Professor of Electrical and Computer Engineering
- Faculty Network Member of the Duke Institute for Brain Sciences
- Faculty Network Member of The Energy Initiative
- Office Location: 3461 CIEMAS, Durham, NC 27708
- Office Phone: (919) 660-5260
- Email Address: firstname.lastname@example.org
- Ph.D. University of Michigan, Ann Arbor, 1995
- M.Sc.Eng. University of Michigan, Ann Arbor, 1986
- B.S.E. University of Kentucky, Lexington, 1985
Physics-based machine learning algorithms for big data, including developing remediation strategies for the hearing impaired and sensor-based algorithms for the detection of hazardous buried objects
- BME 493: Projects in Biomedical Engineering (GE)
- ECE 280L9: Signals and Systems - Lab
- ECE 280L: Introduction to Signals and Systems
- ECE 391: Projects in Electrical and Computer Engineering
- ECE 392: Projects in Electrical and Computer Engineering
- ECE 493: Projects in Electrical and Computer Engineering
- ECE 494: Projects in Electrical and Computer Engineering
- ECE 590: Advanced Topics in Electrical and Computer Engineering
- ECE 891: Internship
- ECE 899: Special Readings in Electrical Engineering
- ENERGY 395: Connections in Energy: Interdisciplinary Team Projects
- ENERGY 396: Connections in Energy: Interdisciplinary Team Projects
- ENERGY 590: Special Topics in Energy
- ENERGY 795: Connections in Energy: Interdisciplinary Team Projects
- ENERGY 796: Connections in Energy: Interdisciplinary Team Projects
- PSY 756: Research Practicum
In the News
- Listening to Broken Hearts, Saving Lives (Sep 21, 2018 | Pratt School of Engineering)
- Smart Meters: Duke Engineers Seek Energy Insights by Reading a Building's Electrical Signatures (Oct 4, 2016)
- Pratt Researchers Are Using Deep Learning to Distinguish Solar Panels from Swimming Pools (Aug 31, 2016)
- Malof, JM; Reichman, D; Karem, A; Frigui, H; Ho, KC; Wilson, JN; Lee, WH; Cummings, WJ; Collins, LM, A large-scale multi-institutional evaluation of advanced discrimination algorithms for buried threat detection in ground penetrating radar, Ieee Transactions on Geoscience and Remote Sensing, vol 57 no. 9 (2019), pp. 6929-6945 [10.1109/TGRS.2019.2909665] [abs].
- Kong, F; Chen, C; Huang, B; Collins, LM; Bradbury, K; Malof, JM, Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: Preliminary results, International Geoscience and Remote Sensing Symposium (Igarss) (2019), pp. 3903-3906 [10.1109/IGARSS.2019.8898617] [abs].
- Lin, K; Huang, B; Collins, LM; Bradbury, K; Malof, JM, A simple rotational equivariance loss for generic convolutional segmentation networks: Preliminary results, International Geoscience and Remote Sensing Symposium (Igarss) (2019), pp. 3876-3879 [10.1109/IGARSS.2019.8898722] [abs].
- Prabhudesai, KS; Collins, LM; Mainsah, BO, Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages, International Ieee/Embs Conference on Neural Engineering, Ner, vol 2019-March (2019), pp. 937-940 [10.1109/NER.2019.8716996] [abs].
- Stump, E; Reichman, D; Collins, LM; Malof, JM, An exploration of gradient-based features for buried threat detection using a handheld ground penetrating radar, Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics, vol 11012 (2019) [10.1117/12.2519949] [abs].