
Thermal Source Localization Through InfiniteDimensional Compressed Sensing
We propose a scheme utilizing ideas from infinite dimensional compressed...
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Approximate Recovery of Initial Pointlike and Instantaneous Sources from Coarsely Sampled Thermal Fields via InfiniteDimensional Compressed Sensing
We propose a method for resolving the positions of the initial source of...
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Noise Learning in Empirical Bayesian Source Reconstruction Algorithms for Electromagnetic Brain Imaging
Electromagnetic brain imaging is the reconstruction of brain activity fr...
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Electromagnetic Brain Imaging using Sparse Bayesian Learning – Noise Learning and Model Selection
Brain source reconstruction from electro or magnetoencephalographic (EE...
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Improving EEG Source Localization through Spatiotemporal Sparse Bayesian Learning
Sparse Bayesian Learning (SBL) approaches to the EEG inverse problem suc...
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Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework
Methods for electro or magnetoencephalography (EEG/MEG) based brain sou...
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Robust Estimation of Noise for Electromagnetic Brain Imaging with the Champagne Algorithm
Robust estimation of the number, location, and activity of multiple corr...
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Ali Hashemi
verfied profile
I am a Data Scientist with over six years of experience and domain expertise in machine learning, statistics, optimization, and signal processing.
 Python, R, Matlab, C/C++, SQL (MySQL & PostgreSQL), Git & Github, LATEX
 TensorFlow; Keras; PyTorch; Python Libraries: Numpy, Pandas, Scipy, Scikitlearn, Seaborn, Bokeh;
DNN Architectures and Methods: CNN, RNN, LSTM, VAE, LRP, Transform Learning.
 Domain Expertise: Machine Learning, Deep Learning (Variational Autoencoders, Deep Learning on Graphs, Bayesian Deep Learning), Statistical Bayesian Inference with Uncertainty Analysis, Timeseries Analysis and Forecasting, Largescale Convex and Nonconvex Optimization, Compressed Sensing and Sparsity, Signal Processing, Inverse Problems.