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Institute Affiliation:
Contact Information:
Phone:
858-534-2052
Email:
saul@cs.ucsd.edu
Personal Home Page
Research Page
| | Lawrence Saul - Associate Professor
Machine learning, pattern recognition, voice processing, auditory computation and methods for high dimensional
data analysis.
Lawrence Saul is internationally known for his work on high dimensional data analysis and visualization.
Combining ideas from the fields of computer science and statistics, he has developed powerful, new algorithms for
revealing low dimensional structure in high dimensional data. Though capable of identifying complex nonlinear
relationships, his approaches retain the tractability of traditional linear methods. His work on nonlinear
dimensionality reduction has applications in many areas of science and engineering, including computational
neuroscience, pattern recognition, and information processing in sensor networks. Saul is also an expert in the
application of ideas from machine learning to problems in audio processing. With his students, he is focused on
improved acoustic models for automatic speech recognition, efficient (real-time) algorithms for auditory scene
analysis, and robust integration of cues in different frequency bands. Much of his work in speech and audio
processing is inspired by psychoacoustic models of human listeners. He is especially interested in "the cocktail
party" problem -- how to follow a single voice in a room with multiple overlapping speakers. Algorithms for solving
this problem have widespread applications in audio surveillance and information retrieval.
Capsule Bio:
Lawrence Saul is an associate professor of Computer Science and Engineering at UCSD's Jacobs School of
Engineering. His main research interests lie in machine learning and audio processing. His methods for high
dimensional data analysis have been applied to many problems in science and engineering. Saul received his Ph.D.
in Physics from M.I.T. in 1994. In 1999, after working
three years in the speech center at AT&T Labs, he was
recognized by the MIT-based journal Technology Review as one of 100 top innovators under the age of thirty-five.
A recent bibliographic analysis by Essential Science Indicators indicated that his work had entered the top 1% of
total citations earned in the field of Computer Science. He is a founding member of the Editorial Board for the
Journal of Machine Learning Research.
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