Legend: AP (accepted publication), BC (book chapter), TR (technical report), CP (conference paper), TH (thesis)

 

S. Voronin. Multi-channel similarity based compression. J. CIS., 2020.
https://svoronin.neocities.org/reports/mcs_comp1a.pdf

 

Clustering and presorting for parallel Burrows-Wheeler compression. TR.

https://svoronin.neocities.org/reports/parallel_bwt_compress_tr.pdf

S. Voronin. Multi-stage image restoration with high noise/blur, J. CIS., 2019.
http://svoronin.neocities.org/reports/a.pdf

S. Voronin and C. Zaroli. Survey of computational methods for inverse problems, Intech, 2018. BC.

https://www.intechopen.com/books/recent-trends-in-computational-science-and-engineering/survey-of-computational-methods-for-inverse-problems

S. Voronin, L. Xiao, G. Mei, R. Xu. Multi-resolution classification techniques for PTSD detection from audio interviews, IEEE ISSPIT, 2018.
https://ieeexplore.ieee.org/document/8642657

S. Voronin and A. Grushin. A multi-resolution auto-ml approach for audio classification, 2018. TR.

https://svoronin.neocities.org/report s/mrac_paper1a.pdf

S. Voronin, C. Zaroli, N. Cuntoor. Conjugate gradient based acceleration for inverse problems. International Journal on Geomathematics (GEM), 2017. AP.

https://link.springer.com/epdf/10.1007/s13137-017-0099-2

 

S. Voronin and P.G. Martinsson. Efficient Algorithms for CUR and Interpolative Matrix Decomposition, Springer Journal of

Applied and Computational Mathematics, 2016. AP.

http://www.springer.com/-/3/AVg79N-jhQ3B_jFr7VaD

 

S. Voronin and I. Daubechies. An Iteratively Reweighted Least Squares Algorithm for Sparse

Regularization, AMS Contemporary Mathematics, 2016. BC.

http://arxiv.org/abs/1511.08970

 

N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz. Randomized Matrix Decompositions using R, 2016. TR.

http://arxiv.org/abs/1608.02148

 

S. Voronin and P.G. Martinsson. RSVDPACK: An implementation of randomized algorithms for computing the singular value, interpolative, and CUR decompositions of matrices on multi-core and GPU architectures, 2016. TR.

http://arxiv.org/abs/1502.05366

 

M. Lodhi, S. Voronin, and W. Bajwa. YAMPA: Yet another matching pursuit algorithm for compressive sensing. SPIE

Proceedings, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 2016. CP.

http://dx.doi.org/10.1117/12.2224334

 

P.G. Martinsson and S. Voronin. A randomized blocked algorithm for efficiently computing rank-revealing factorizations of matrices, SIAM Journal on Scientific Computing, 2015. AP.

http://epubs.siam.org/doi/abs/10.1137/15M1026080

 

S. Voronin, D. Mikesell, and G. Nolet. Compression Approaches for the Regularized Solutions of Linear Systems from Large-Scale Inverse Problems. International Journal on Geomathematics (GEM), 2015. AP.

http://link.springer.com/article/10.1007/s13137-015-0073-9

 

S. Voronin, G. Ozkaya, and D. Yoshida.  Convolution based smooth approximations to the absolute value function with application to non-smooth regularization, 2015. TR.

http://arxiv.org/abs/1408.6795

 

L. Huang and S. Voronin. Derivation of non-standard finite difference schemes for differential equations, 2015. TR.

https://svoronin.neocities.org/reports/nsfd_report.pdf

S. Voronin, D. Mikesell, I. Slezak, and G. Nolet. Solving large tomographic linear systems: size reduction and error estimation. Geophysical Journal International, 2014. AP.

http://gji.oxfordjournals.org/content/199/1/276.abstract

 

J. Charlety, S. Voronin, G. Nolet, I. Loris, F.J. Simons, K. Sigloch, and I.C. Daubechies. Seismic tomography with a sparsity constraint: comparison with smoothing and damping regularization. Journal of Geophysical

Research - Solid Earth, 2013. AP.

http://onlinelibrary.wiley.com/doi/10.1002/jgrb.50326/abstract

 

S. Voronin and R. Chartrand. A new generalized thresholding algorithm for inverse problems with sparsity constraints. ICASSP, 2013. CP.

http://math.lanl.gov/Research/Publications/Docs/voronin-2013-new.pdf

 

S. Voronin and H. Woerdeman. A new iterative firm-thresholding algorithm for

inverse problems with sparsity constraints. Applied and Computational Harmonic Analysis, 2012. AP.

http://www.sciencedirect.com/science/article/pii/S1063520312001327

 

S. Voronin. Regularization of Linear Systems with Sparsity Constraints with Applications to Large Scale Inverse Problems, Ph.D. Thesis, PACM, Princeton University, 2012. TH.

http://svoronin.neocities.org/reports/sergey_voronin_thesis_with_corrections.pdf

F. Simons, I. Loris, G. Nolet, I.C. Daubechies, S. Voronin, S. Judd, P. Vetter, J. Charlety, and C. Vonesch. Solving or resolving global tomographic models with spherical wavelets, and the scale and sparsity of seismic heterogeneity. Geophysical Journal International, 2011. AP.

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-246X.2011.05190.x/full

 

E.P. Gerber, S. Voronin, and L.M. Polvani. Testing the annular mode autocorrelation timescale in simple atmospheric general circulation models, Monthly Weather Review, 2008. AP.

http://journals.ametsoc.org/doi/abs/10.1175/2007MWR2211.1

 

S. Voronin, J. Matthewman, A. Charlton, L.M. Polvani, and G. Esler. A New Web Based Resource for Studying Major Mid-Winter Stratospheric Sudden Warmings, Stratospheric Processes and Their Role, Climate Newsletter, Vol. 27, 2006. CP.

http://www.sparc-climate.org/fileadmin/customer/6_Publications/Newsletter_PDF/27_SPARCnewsletter_Jul2006.pdf