Behnaam Aazhang — accepted AI-conference papers

2020

Beamforming Design for High-Resolution Low-Intensity Focused Ultrasound Neuromodulation

Boqiang Fan, Wayne K. Goodman, Raymond Y. Cho, Sameer A. Sheth, Richard R. Bouchard, Behnaam Aazhang

ICASSP 2020accepted

Low-intensity focused ultrasound (LIFU) has been shown to modulate neural activity. Recent experiments suggest potential applications of LIFU stimulation for treating neuropsychiatric disorders like depression and Alzheimer's. The modulation effect is usually positively correlated with the ultrasoun…

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2020

Learnable Group Transform For Time-Series

Romain Cosentino, Behnaam Aazhang

ICML 2020poster

We propose a novel approach to filter bank learning for time-series by considering spectral decompositions of signals defined as a Group Transform. This framework allows us to generalize classical time-frequency transformations such as the Wavelet Transform, and to efficiently learn the representati…

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2019

The Geometry of Deep Networks: Power Diagram Subdivision

Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard Baraniuk

NeurIPS 2019poster

We study the geometry of deep (neural) networks (DNs) with piecewise affine and convex nonlinearities. The layers of such DNs have been shown to be max-affine spline operators (MASOs) that partition their input space and apply a region-dependent affine mapping to their input to produce their output…

Cited by 69 Source ↗PDF ↗code ↗Scholar ↗

2015

Inferring causal connectivity in epileptogenic zone using directed information

Rakesh Malladi, Giridhar P. Kalamangalam, Nitin Tandon, Behnaam Aazhang

ICASSP 2015accepted

Directed information, an information theoretic quantity, is developed in this paper to infer the causal connectivity from electrocorticography (ECoG) recordings of an epileptic patient. The causal connectivity can be used to infer the optimal electrodes for electrical stimulation based treatments of…

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