Difference-of-convex learning: optimization with non-convex sparsity functions 

Exploring complexity reduction in deep learning 

Leveraging sparsity in theoretical and applied machine learning and causal inference 

Spoken language processing in low resource scenarios with applications in automated behavioral coding 

Shrinkage methods for big and complex data analysis 

Exploitation of sparse and low-rank structures for tracking and channel estimation 

Analysis, design, and optimization of large-scale networks of dynamical systems 

Signal processing methods for brain connectivity 

Smaller, faster and accurate models for statistical machine translation 

Fast flexible dynamic three-dimensional magnetic resonance imaging 

Improved computational and statistical guarantees for high dimensional linear regression 

Feature learning for imaging and prior model selection 

Modern nonconvex optimization: parametric extensions and stochastic programming 

High dimensional estimation and inference with side information 

Mixed-integer nonlinear programming with binary variables 

Generalized linear discriminant analysis for high-dimensional genomic data with external information 

Hardware and software techniques for irregular parallelism 

Utilizing the sparsity of Kernel matrix with dynamic facturization for a dynamically dimension-expanding algorithm when solving convex quadratic programming problems 

Improved brain dynamic contrast enhanced MRI using model-based reconstruction 

On the theory and applications of structured bilinear inverse problems to sparse blind deconvolution, active target localization, and delay-Doppler estimation 

Computational methods for fluorescence molecular tomography 

Novel optimization tools for structured signals recovery: channels estimation and compressible signal recovery 

Neighborhood and graph constructions using non-negative kernel regression (NNK) 

Three essays on linear and non-linear econometric dependencies 

Large-scale inference in multiple Gaussian graphical models