2022
 Towards Quantum Advantage on Noisy Quantum Computers
I. Akhalwaya, S. Ubaru, K. L. Clarkson, M. Squillante, V. Jejjala, Y.H. He, K. Naidoo, V. Kalantzis, and L. Horesh, 2022.
[ arXiv]
 On Quantum Algorithms for Random Walks in the Nonnegative Quarter Plane
V. Kalantzis, M. S. Squillante, S. Ubaru, and L. Horesh.
Workshop on Mathematical performance Modeling and Analysis (MAMA), 2022.
 Randomized matrixfree quadrature for spectrum and spectral sum approximation
T. Chen, T. Trogdon, and S. Ubaru, 2022.
[ arXiv]
 PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
P.F. Shustin, S. Ubaru, V. Kalantzis, L. Horesh, and H. Avron, 2022.
[ arXiv]
 Representation of the Fermionic Boundary Operator
I.Y. Akhalwaya, Y.H. He, L. Horesh, V. Jejjala, W. Kirby, K. Naidoo, and S. Ubaru.
Physical Review A, volume 106, 022407, 2022.
[ PRA, arXiv]
2021
 Quantum Topological Data Analysis with Linear Depth and Exponential Speedup
S. Ubaru, I. Akhalwaya, M. Squillante, K. Clarkson, and L. Horesh, 2021.
[ arXiv]
 Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription
S. Ubaru, L. Horesh, and G. Cohen
Journal of Biomedical Informatics (JBI), Special Issue on Novel Informatics Approaches to COVID19 Research, Volume 122, 103901, 2021.
[ Link, arXiv]
 Efficient Scaling of Dynamic Graph Neural Networks.
V. Chakaravarthy, S. Pandian, S. Raje, Y. Sabharwal, T. Suzumura, and S. Ubaru
Supercomputing (SC21), 2021.
[ arXiv]
 Analysis of stochastic Lanczos quadrature for spectrum approximation
Tyler Chen, Thomas Trogdon, and S. Ubaru
International Conference on Machine Learning (ICML), 2021.
[ arXiv]
 Projection techniques to update the truncated SVD of evolving matrices
V. Kalantzis, G. Kollias, S. Ubaru, A. Nikolakopoulos, L. Horesh, and K.L. Clarkson
International Conference on Machine Learning (ICML), 2021.
[ arXiv]
 Sparse graph based sketching for fast numerical linear algebra
D. Hu, S. Ubaru, A. Gittens, K.L. Clarkson, L. Horesh, and V. Kalantzis
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021.
[ arXiv]
 Dynamic Graph Convolutional Networks Using the Tensor MProduct
O. Malik, S. Ubaru, L. Horesh, M. Kilmer, and H. Avron
SIAM International Conference on Data Mining (SDM), 2021.
[ arXiv]
2020
 SpectrumAdapted Polynomial Approximation for Matrix Functions with Applications in Graph Signal Processing
L. Fan, D. Shuman, S. Ubaru, and Y. Saad,
Algorithms, Special Issue : Efficient Graph Algorithms in Machine Learning, 13(11), 295; 2020.
[ Link]
 Multilabel Classification by Hierarchical Partitioning and Datadependent Grouping
S. Ubaru, S. Dash, O. Gunluk, and A. Mazumdar
Neural Information Processing Systems (NeurIPS), 2020.
[ arXiv]
 Unsupervised Hierarchical Graph Representation Learning with Variational Bayes
S. Ubaru and J. Chen, 2020.
[ Link]
2019
 Multilabel prediction in log time and datadependent grouping
S. Ubaru, S. Dash, O. Gunluk, and A. Mazumdar
NeurIPS  Workshop on Information Theory and Machine Learning, 2019.
[ link]
 Tensor graph neural networks for prediction on time varying graphs
O. Malik, S. Ubaru, L. Horesh, M. Kilmer, and S. Becker
NeurIPS  Workshop on Graph Representation Learning, 2019.
[ link]
 Find the dimension that counts: Fast dimension estimation and Krylov PCA
S. Ubaru, AK. Seghouane, and Y. Saad
SIAM International Conference on Data Mining (SDM), 2019.
[ arXiv, SIAM]
 SpectrumAdapted Polynomial Approximation for Matrix Functions
L. Fan, D. Shuman, S. Ubaru, and Y. Saad,
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.
[ arXiv]
 Sampling and multilevel algorithms for fast matrix approximations
S. Ubaru and Y. Saad,
Numerical Linear Algebra with Applications, vol 26 (3), e2234, 2019.
[ arXiv, , NLAA]
 Provably convergent acceleration in factored gradient descent with applications in matrix sensing.
T Ajayi, D Mildebrath, A Kyrillidis, S. Ubaru, G Kollias, and K Bouchard, 2019.
[ arXiv]
2018
 Algorithmic advances in learning from large dimensional matrices and scientific data
Ph.D. Thesis, University of Minnesota, Minneapolis, May. 2018.
[ PDF]
 Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness
C. Musco, P. Netrapalli, A. Sifford, S. Ubaru and D.P. Woodruff
Innovations in Theoretical Computer Science (ITCS), 2018.
[ arXiv]
 Applications of trace estimation techniques
S. Ubaru and Yousef Saad
High Performance Computing in Science and Engineering (HPCSE), LNCS, vol. 11087, 1933, 2018.
[ Link,
PDF]
2017
 UoINMF_cluster : A Robust Nonnegative Matrix Factorization Algorithm for Improved PartsBased Decomposition and Reconstruction of Noisy Data
S. Ubaru , Kesheng Wu and Kristofer E. Bouchard
IEEE International Conference on Machine Learning and Applications (ICMLA), 2017.
[ PDF,
IEEE]
*Winner of the Best Paper Award*
 Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
K. E. Bouchard, A. F. Bujan, F. RoostaKhorasani, S. Ubaru, Prabhat, A. M. Snijders, J.H. Mao, E. F. Chang, M. W. Mahoney, and S. Bhattacharyya
Neural Information Processing Systems (NeurIPS), 2017.
[ arXiv,
NIPS]
 Multilabel Classification with Group Testing and Codes
S. Ubaru and Arya Mazumdar
International Conference on Machine Learning (ICML), 2017.
[ PDF, ICML]
 Fast estimation of tr(f(A)) via Stochastic Lanczos Quadrature
S. Ubaru, Jie Chen, and Yousef Saad
SIAM Journal on Matrix Analysis and Applications (SIMAX), 38(4), 1075–1099, 2017.
[ SIMAX, PDF,
Codes]
 Low rank approximation and decomposition of large matrices using error correcting codes
S. Ubaru, Arya Mazumdar, and Yousef Saad
IEEE Transactions on Information Theory, 63(9), 55445558, 2017.
[ arXiv, TofIT]
 Formation enthalpies for transition metal alloys using machine learning
S. Ubaru, Agnieszka Miedlar, Yousef Saad and James R. Chelikowsky
Physcal Review B, (Vol.95, No.21) 2017.
[ PRB]
 Fast estimation of approximate matrix ranks using spectral densities
S. Ubaru, Yousef Saad, and AbdKrim Seghouane
Neural Computation, 29(5):13171351, 2017.
[ arXiv,
NC]
 Improving the Incoherence of a Learned Dictionary via Rank Shrinkage
S. Ubaru, AbdKrim Seghouane, and Yousef Saad
Neural Computation, 29(1):263285, 2017.
[ PDF,
NC]
2016
 Fast methods for estimating the Numerical rank of large matrices
S. Ubaru and Yousef Saad
International Conference on Machine Learning (ICML), 2016.
[
PDF,
ICML,
Codes]
 Group testing schemes from lowweight codewords of BCH codes
S. Ubaru, Arya Mazumdar, and Alexander Barg
IEEE International Symposium on Information Theory (ISIT), 2016.
[ PDF, ISIT]
2015 and before

Low rank approximation using error correcting coding matrices
S. Ubaru, Arya Mazumdar, and Yousef Saad
International Conference on Machine Learning (ICML), 2015.
[
PDF, talk, slides]
Published version contains some minor errors that have since been corrected in the above revised version.
 Randomized techniques for matrix decomposition and estimating the approximate rank of a matrix
M. S. Thesis, University of Minnesota, Minneapolis, Nov. 2014.
[ Link, PDF]
 Displaying gray scales by cross pairing select and data voltages in multiline addressed LCD
S. Ubaru and Temkar N. Ruckmongathan
IEEE, Journal of Display Technology, vol 8, no. 11, pp 669–677, November, 2012.
[ JDT]
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