Department of Electrical and Computer Engineering
University of California, San Diego

5604 EBU1
Mailcode 0407
La Jolla, CA 92093-0407
Fax: + 1 858 534-6976
Phone: + 1 858 539-6003


Gert Lanckriet's research interests are on the interplay between machine learning, (convex) optimization, big data analytics and crowdsourcing, inspired by and with applications to computer audition, music information retrieval (in particular, music search and recommendation), multimedia, and personalized, mobile health. 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 crowdsourcing and active learing approaches. His group also works on music recommendation algorithms based on audio content as well as other rich multimedia content. His work in mobile health focuses on leveraging ubiquitous mobile sensors with large-scale data analytics, to monitor and analyze personal health (including phycisal activity, chronic disease episodes). 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), and is a co-PI of the Distributed Health Lab (DHLab).

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. In 2014, he received the Best Ten-Year Paper Award at the International Conference on Machine Learning. His research focuses on machine learning, optimization, big data analytics, and crowdsourcing, with applications in music search and recommendation, multimedia, and personalized, mobile health.