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Les Atlas
Also published under:L. Atlas, L. E. Atlas, Les E. Atlas
Affiliation
Department of Electrical and Computer Engineering, University of Washington, Seattle
Topic
Diagonal Matrix,Graph Signal,Signal Processing,Accurate Estimation,Activity Time Series,Adaptive Filter,Adaptive Technique,Additional Edges,Array Elements,Azimuth Angle,Brain Communication,Brain Processes,Conventional Process,Covariance Matrix,Diffusion Model,Diffusion Process,Directed Graph,Direction Angle,Direction Of Arrival,Direction Of Arrival Estimation,Discrete Fourier Transform,Discrete-time,Dynamic Linear Model,Dynamic Model,Ear Canal,Estimation Performance,Expectation Maximization,Factor Analysis,Factor Analysis Techniques,Flexible Domain,Flow In The Brain,Flow Model,Flow Signal,Frequency Bins,Gradient Components,Gradient Flow,Gradient Mode,Graph Structure,Graph Topology,Head-related Impulse Responses,Impulse Response,Latent Variables,Linear Dynamical System,Linear Dynamics,Linear Model,Linear System,Linear Time-varying Systems,Linear Weight,Local Stimulation,Maximum Likelihood Estimation,
Biography
Les Atlas (F’04) received the M.S. and Ph.D. degrees in
electrical engineering from Stanford University, Stanford, CA, USA, in 1979 and 1984, respectively. He joined the
University of Washington, Seattle, WA, USA, in 1984, where he is currently a Professor in electrical engineering and
an Adjunct Professor in computer science and Engineering. His research interest include the digital signal processing,
with specializations in audio and acoustic analysis, harmonic analysis and modulation representations, statistical
signal detection, recognition, and coding. His work has been used for these medical, consumer, and industrial
technologies: Cochlear implants, audio and speech processing, sonar, radar, and machine learning. He also has
contributed by educating students, including some who ended up in top faculty positions. His academic work has had
impact: He coauthored the first publication on convolutional neural networks for time signals. Also, one of his
hundreds of publications, “Improving generalization with active learning,” initiated the machine learning
field of active learning and selective sampling. He was the recipient of several awards including the 2004 Fulbright
Senior Research Scholar and the 2012 Virginia Merrill Bloedel Scholar Award. He is a Fellow of the IEEE for
contributions to time-varying spectral analysis and acoustical signal processing. He was the General Chair for the
1998 IEEE International Conference on Acoustics, Speech, and Signal Processing, the Founding Chair for the IEEE Signal
Processing Society Technical Committee on Theory and Methods, and a Member-at-Large of the Signal Processing
Society's Board of Governors. He has presented invited tutorials on demodulation signal processing at IEEE and
other large international conferences, such as Eurospeech. He codirected the 2015 Jelinek Summer Workshop on Speech
and Language Technology. His research is sponsored by DARPA, the Office of Naval Research, and an Amazon Catalyst
Award.