Leslie M. Collins

Image of 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. She is currently chairing the ECE Department. 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.

Appointments and Affiliations
  • Professor of Electrical and Computer Engineering
  • Faculty Network Member of the Duke Institute for Brain Sciences
Contact Information:
Education:

  • Ph.D. University of Michigan at Ann Arbor, 1995
  • M.Sc.Eng. University of Michigan at Ann Arbor, 1986
  • B.S.E. University of Kentucky at Lexington, 1985

Curriculum Vitae
Research Interests:

This laboratory’s research is in the area of physics-based statistical signal processing algorithms, and we are actively engaged in two general application areas: (1) Investigating human auditory perception and developing remediation strategies for the hearing impaired; (2) developing sensor-based algorithms for the detection of hazardous buried objects, such as unexploded ordnance (UXO) and landmines. Our research methodology is distinguished in two fundamental ways. First, we place an emphasis on incorporating the physics or phenomenology that governs the specific application directly into the signal processing framework, and we consider both experimental and theoretical issues. Second, we maintain an interactive collaboration with the end-user community that provides necessary feedback to the development process and validates the real-world utility of our research efforts. Our work in these application areas has improved quality of life and safety of life as a result of the development of novel signal processing algorithms.

Specialties:

Sensing and Sensor Systems
Homeland Security
Land Mine Detection
Neural Prosthesis
Geophysics
Signal Processing

Awards, Honors, and Distinctions:

  • Capers & Marion McDonald Award for Excellence in Teaching and Research, Duke University, School of Engineering, 2005
  • Eta Kappa Nu
  • Full Member, USNC/URSI Commission A (Electromagnetic Metrology), 2000
  • Outstanding Engineering: Innovative Technical Contributions to the Benefit of Human Welfare, IEEE Eastern North Carolina Section, 1998
  • Project of the Year: Statistical Signal Processing with Physics-Based Methods, Strategic Environmental Research and Development Program, DOD, 2000
  • Project of the Year: Wide Area Assessment for UXO Remediation, Strategic Environmental Research and Development Program, DOD, 2005
  • Senior Member, IEEE, 2001
  • Sigma Xi
  • Tau Beta Pi

Courses Taught:
  • BME 493: Projects in Biomedical Engineering (GE)
  • ECE 280L9: Signals and Systems - Lab
  • ECE 280L: Introduction to Signals and Systems
  • ECE 391: Undergraduate Research in Electrical and Computer Engineering
  • ECE 392: Undergraduate Research in Electrical and Computer Engineering
  • ECE 488: Digital Image and Multidimensional Processing
  • ECE 493: Undergraduate Research in Electrical and Computer Engineering
  • ECE 494: Undergraduate Research in Electrical and Computer Engineering
  • ECE 582: Digital Signal Processing
  • ECE 899: Special Readings in Electrical Engineering
  • ENERGY 395-1: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 395: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 396-1: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 396: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 795-1: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 795: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 796-1: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 796: Connections in Energy: Interdisciplinary Team Projects

Representative Publications: (More Publications)
    • Ratto, CR; Morton, KD; Collins, LM; Torrione, PA, Analysis of Linear Prediction for Soil Characterization in GPR Data for Countermine Applications, Sensing and Imaging, vol 15 no. 1 (2014) [10.1007/s11220-014-0086-8] [abs].
    • Colwell, KA; Ryan, DB; Throckmorton, CS; Sellers, EW; Collins, LM, Channel selection methods for the P300 Speller., Journal of Neuroscience Methods, vol 232 (2014), pp. 6-15 [10.1016/j.jneumeth.2014.04.009] [abs].
    • Mainsah, BO; Colwell, KA; Collins, LM; Throckmorton, CS, Utilizing a Language Model to Improve Online Dynamic Data Collection in P300 Spellers, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol 22 no. 4 (2014), pp. 837-846 [10.1109/TNSRE.2014.2321290] [abs].
    • Desmond, JM; Collins, LM; Throckmorton, CS, The effects of reverberant self- and overlap-masking on speech recognition in cochlear implant listeners., Journal of the Acoustical Society of America, vol 135 no. 6 (2014), pp. EL304-EL310 [10.1121/1.4879673] [abs].
    • Ratto, CR; Jr, KDM; Collins, LM; Torrione, PA, Bayesian context-dependent learning for anomaly classification in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol 52 no. 4 (2014), pp. 1969-1981 [10.1109/TGRS.2013.2257175] [abs].