Machine Learning for Signal Processing
Scope
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.
Highlights From the MLSP TC
The huge success of this wave of artificial intelligence (AI) has primarily been driven by machine learning, which provides the essential tools for analyzing signals and data that are ubiquitously available today. The MLSP TC aims at fostering novel machine learning methodologies for both longstanding and emergent signal processing applications of a broad range.
Check out this spotlight article to learn more about the MLSP TC's recent activities.
MLSP TC Member Election (2027–2029 Term)
The Machine Learning for Signal Processing Technical Committee is seeking nominations for new Members. Candidates may be self-nominated or nominated by current MLSP TC members. All candidates must be current IEEE and IEEE Signal Processing Society members in good standing and have demonstrated expertise relevant to machine learning for signal processing.
Members serve a three-year term and are expected to participate actively in TC activities, including conference and workshop reviewing, TC meetings and voting, and at least one subcommittee or recurring initiative. Current members completing their first term may be nominated for a second consecutive term. After two consecutive terms, a three-year gap in service is required before becoming eligible again.
The MLSP TC welcomes candidates who can contribute to its technical breadth, activities, and collaborative work. Nominations from underrepresented groups and regions, young professionals, and candidates from industry, government, and other underrepresented sectors are particularly encouraged. Members will be elected by the current voting members of the MLSP TC based on the candidates’ qualifications and the Committee’s current needs.
Submission
Interested candidates can download one of the following forms:
Complete the form and save in PDF format, and submit it along with an up-to-date CV via email to the MLSP Member Nomination and Election Subcommittee and TC Chairs by 30 September 2026.
