Gert Lanckriet's research interests are on the interplay between machine learning, applied statistics and convex optimization techniques, as well as in the field of computer audition. His research focuses on the integration of multiple, heterogeneous data types for a variety of pattern discovery tasks where the amount of data is extremely large and the solutions desired to be sparse. An important challenge in the field of machine learning is to deal with the increasing amount of data that is available for learning and to leverage the (also increasing) diversity of information sources, describing these data. Beyond classical vectorial data formats, data in the format of graphs, trees, strings and beyond have become widely available for data mining, e.g., the linked structure of communication networks, amino acid sequences describing proteins, etc. Moreover, for interpretability, stability and economical reasons, decision rules that rely on a small subset of the information sources and/or a small subset of the features describing the data are highly desired: sparse learning algorithms are a must. Gert Lanckriet's research is inspired by practical applications in domains like computational genomics, financial engineering and computer audition. The latter has recently become a major focus of his research program and resulted in the creation of the Computer Audition Lab where interdisciplinary research is pursued to build systems that "listen to" and "describe" music and sound.
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