Department of Electrical and Computer Engineering
University of California, San Diego
5604 EBU1
Mailcode 0407
La Jolla, CA 92093-0407
USA
Fax: + 1 858 534-6976
Phone: + 1 858 539-6003
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 group 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. He has also worked on applications in computational genomics and finance.
Gert Lanckriet received a Master's degree in Electrical Engineering from the Katholieke Universiteit Leuven, Leuven, Belgium, in 2000 and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from the University of California, Berkeley in 2001 respectively 2005. In 2005, he joined the Department of Electrical and Computer Engineering at the University of California, San Diego, where he heads the Computer Audition Lab (CALab). 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). His lab received a Yahoo! Key Scientific Challenges Award, a Qualcomm Innovation Fellowship and a Google Research Award. His research focuses on the interplay of convex optimization, machine learning and applied statistics, with applications in computer audition and music information retrieval.