Leslie M. Collins, Professor of Electrical and Computer Engineering


Leslie M. Collins

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.

Contact Info:
Office Location:  128 Hudson Hall
Office Phone:  (919) 660-5260, (919) 660-5212
Email Address:   send me a message
Web Page: http://www.ee.duke.edu/research/collins


PhD, University of Michigan, 1995
MS, University of Michigan, 1986
BS, University of Kentucky, 1985
Curriculum Vitae
Research Interests: Statistical Signal Processing, Remote Sensing, Auditory Prostheses

Current projects: • Development of novel advanced data processing techniques for fielded and prototype UXO and landmine sensors., • Multi-modal sensor fusion for subsurface sensing., • Sensor management for efficient sensor deployment to decrease time to decision., • Optimization of phenomenological electromagnetic induction model inversions for UXO detection., • Statistical modeling of ground-penetrating radar signatures for landmine detection., • Non-destructive fill material identification using gamma-ray spectroscopy., • Object classification through ultrasonic measurements and model fitting., • Classification of ultrasonic measurements of chemical signatures., • Interactive computational auditory scene analysis for acoustic event detection and classification., • EEG signal analysis to facilitate communication via brain-computer interface (BCI)., • Improvement of speech recognition and music perception for cochlear implant users through psychophysics and algorithm development.

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.

Areas of Interest:

statistical signal processing
remote sensing
auditory prostheses


Sensing and Sensor Systems
Homeland Security
Land Mine Detection
Neural Prosthesis
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
Teaching (Spring 2018):

  • ECE 590.05, ADVANCED TOPICS IN ECE Synopsis
    Gross Hall 100C, MW 10:05 AM-11:20 AM
Recent Publications   (More Publications)

  1. J. S. Stohl and C. S. Throckmorton and L. M. Collins, Investigating the effects of stimulus duration and context on pitch perception by cochlear implant users, Journal Of The Acoustical Society Of America, vol. 126 no. 1 (July, 2009), pp. 318 -- 326  [abs].
  2. S. L. Tantum and Q. Zhu and P. A. Torrione and L. M. Collins, Modeling position error probability density functions for statistical inversions using a Goff-Jordan rough surface model, Stochastic Environmental Research And Risk Assessment, vol. 23 no. 2 (February, 2009), pp. 155 -- 167  [abs].
  3. K. D. Morton and P. A. Torrione and C. S. Throckmorton and L. M. Collins, Mandarin Chinese tone identification in cochlear implants: Predictions from acoustic models, Hearing Research, vol. 244 no. 1-2 (October, 2008), pp. 66 -- 76  [abs].
  4. J. S. Stohl and C. S. Throckmorton and L. M. Collins, Assessing the pitch structure associated with multiple rates and places for cochlear implant users, Journal Of The Acoustical Society Of America, vol. 123 no. 2 (February, 2008), pp. 1043 -- 1053  [abs].
  5. J. J. Remus and L. M. Collins, Comparison of adaptive psychometric procedures motivated by the Theory of Optimal Experiments: Simulated and experimental results, Journal Of The Acoustical Society Of America, vol. 123 no. 1 (January, 2008), pp. 315 -- 326  [abs].

Leslie Collins is a Professor and Chair in Electrical and Computer Engineering and has joint appointments in Biomedical Engineering and Otolaryngology-Head and Neck Surgery (DUMC). She received her Ph.D. in Electrical Engineering:Systems from the University of Michigan. Her research interests lie in physics-based statistical signal processing with applications in remote sensing and auditory prostheses.