I am an engineer in the Environmental & Information Systems Department. I work on various projects developing algorithms in machine learning, signal processing, digital communications, and acoustics. A sample of my projects is below.

Similarity-based statistical learning - Basic research into a new statistical learning architecture that can recognize patterns from small numbers of samples and effectively fuse many disparate data sources by using flexible heterogeneous similarity measures based on psychology and information theory. Traditional approaches to similarity describe objects as numerical vectors in a multidimensional geometric space, where each element represents a feature of the object.Similarity between objects is measured by computing their distance (e.g. Euclidean). Judging similarity between objects characterized by many disparate data sources poses challenges of data representation, quantitative comparison, and contextual decisions about the predictive value of different data sources. These challenges are not met by current metric space statistical learning architectures, which have particular difficulty learning from high-dimensional data and coping with non-numerical features.

Underwater acoustic signal processing and classification - Environmental complexities radically influence the behavior of sound waves in water, and thus affect the parameters measured by sonars. Signal processing algorithms help mitigate the environmental effects on the measured acoustic signals, and contribute to the successful development of signal detection and classification techniques. I use the Sonar Simulation Toolkit (SST), CASS, and Matlab for sonar research.

Blind demodulation and automatic modulation classification - Blind demodulation techniques, that is techniques that do not rely on a priori knowledge of signal parameters, are combined with modulation classification methods to automatically demodulate unknown signals. Blind techniques include blind equalization with the constant modulus algorithm (CMA), blind symbol rate estimation, carrier and bandwidth estimation, residual carrier and phase compensation. Modulation classification is achieved by automatic classifiers operating on features extracted from partially demodulated signals.

GPS anti-jamming algorithms - Intentional and unintentional interference can jam global positioning (GPS) receivers. New algorithms, combining notions of beamforming (MVDR), space-time adaptive processing (STAP), and frequency excision, significantly reduce the interference.