Daniel D. Leeds: Research

Daniel D. Leeds
Research

Research Interests:

Signal processing, machine learning, biological modeling, human cognition (among many other topics)

More specifically: I am interested in modeling neural encoding of peceptual data and in modeling neural adaptation mechanisms employed as perceptual data changes (e.g., in intensity or in other statistical properties).

Research Projects:

Currently, I study efficient encodings for acoustic data, incorporating insights from neuroscience. I am implementing a model to capture non-linear structures in the spike codes produced by Smith and Lewicki.

Previously, I have developed several tools to analyze physiological signals for medical diagnoses and for biological research.

Automated auscultation: I have created software to produce two-dimensional visualizations of the distributions of patients in diagnostically-relevant high-dimensional feature spaces, expanding upon cardiac sound analysis at the MIT Computer Science/Artificial Intelligence Laboratory.
Economy of effort in speech: I have explored the kinematics of human speech, at the MIT Research Laboratory of Electronics Speech Group.
Statistical structure of bird song: I have studied the statistical structure of zebra finch communication, at Rockefeller University.

See my curriculum vitae for further details.

Papers:

Assisted Auscultation: Creation and Visualization of High Dimensional Feature Spaces for the Detection of Mitral Regurgitation (M.Eng. Thesis)
Z Syed, D Leeds, D Curtis, J Guttag, "Audio-visual tools for computer-assisted diagnosis of cardiac disorders," Computer Based Medical Systems 2006, June 2006.
Z Syed, D Curtis, D Leeds, F Nesta, R A Levine, and J Guttag, "A framework for the analysis of acoustical cardiac signals," accepted by IEEE Transactions on Biomedical Engineering.
Independent Manifolds in the Zebra Finch Song: A Strategy for Robust Social Interaction (Intel Science Talent Search submission)