Assoc Professor, Electrical and Computer Engineering
Machine learning, applied statistics, convex optimization, with applications to music information retrieval, computer audition, computational genomics, finance.
Gert Lanckriet’s research interests are on the interplay between machine learning, applied statistics and convex optimization, inspired by and with applications to computer audition and music information retrieval, in particular, music search and recommendation. His theoretical and algorithmic work focuses on kernel-based learning algorithms to optimally integrate multiple, heterogeneous data modalities, e.g., to analyze rich multimedia content consisting of text, audio, images, video, etc. A second area of his machine learning research studies the design of sparse learning algorithms, to design models that depend only on a small number of variables describing the data. This can reduce computational, experimental or economic requirements, or improve the interpretability or generalization performance of the model. His work in music information retrieval focuses on the theory and design of systems to organize and search large music (or, audio) databases. In particular, his lab studies algorithms for content-based music annotation and retrieval (to automatically annotate music with descriptive tags, e.g., genres, emotions, instruments, etc.), including the integration of human computation games with active machine learning, and music recommendation algorithms based on audio content as well as other rich multimedia content.
Gert Lanckriet joined the Electrical and Computer Engineering department at UCSD in September 2005, where he currently heads the Computer Audition Laboratory (CALab) and leads an interdepartmental group on Computational Statistics and Machine Learning (COSMAL). He received his M.Sc. and Ph.D. degree in Electrical Engineering and Computer Science from the University of California, Berkeley in 2001 respectively 2005. He received the Electrical Engineering degree from the Katholieke Universiteit Leuven, Belgium, in 2000. He was awarded the SIAM Optimization Prize in 2008 and is the recipient of a Hellman Fellowship, an IBM Faculty Award, an NSF CAREER Award and an Alfred P. Sloan Foundation Research Fellowship. In 2011, MIT Technology Review named him one of the 35 top young technology innovators in the world (TR35). He teaches a core undergraduate level course in electrical engineering, and graduate level courses in convex optimization and machine learning. Gert Lanckriet was born in Bruges, Belgium on March 1, 1977.
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