Member of Machine Learning for Signal Processing Technical Committee (2004-2007)

The Machine Learning for Signal Processing Technical Committee (MLSP TC) is at the interface between theory and application, developing novel theoretically-inspired methodologies targeting both longstanding and emergent signal processing applications. Central to MLSP is on-line/adaptive nonlinear signal processing and data-driven learning methodologies. Since application domains provide unique problem constraints/assumptions and thus motivate and drive signal processing advances, it is only natural that MLSP research has a broad application base. MLSP thus encompasses new theoretical frameworks for statistical signal processing (e.g. machine learning-based and information-theoretic signal processing), new and emerging paradigms in statistical signal processing (e.g. independent component analysis (ICA), kernel-based methods, cognitive signal processing) and novel developments in these areas specialized to the processing of a variety of signals, including audio, speech, image, multispectral, industrial, biomedical, and genomic signals. The MLSP TC is focused on fostering research in these areas, the application of these techniques, and in educating the technical community about research developments in these areas. The MLSP TC organizes related technical sessions at ICASSP and has an annual international workshop, now in its nineteenth year, with the above-described technical scope. In addition, MLSP has technical presence on the IEEE Transactions on Signal Processing and (currently) the IEEE Transactions on Image Processing Editorial Boards and has initiated a new set of EDICS for IEEE Transactions on Signal Processing, including a recently approved area, Cognitive Information Processing, which is emerging and expected to attract strong interest in coming years. Fields of Interest Algorithm and Architectures: Artificial neural networks, kernel methods, committee models, Gaussian processes, independent component analysis, advanced (adaptive, nonlinear) signal processing, (hidden) Markov models, Bayesian modeling, parameter estimation, generalization, optimization, design algorithms. Applications: Biomedical engineering, bioinformatics, speech processing, blind source separation, image processing (computer vision, OCR), multimodal interactions, multi-channel processing, intelligent multimedia and web processing, robotics, sonar and radar, financial analysis, time series prediction, data fusion, data mining, adaptive filtering, communications, sensors, system identification, and other signal processing and pattern recognition applications. Implementations: Parallel and distributed implementation, hardware design, and other general implementation technologies