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 matrix-free 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 COVID-19 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 M-Product
  O. Malik, S. Ubaru, L. Horesh, M. Kilmer, and H. Avron
  SIAM International Conference on Data Mining (SDM), 2021.
  [ arXiv]

2020

• Spectrum-Adapted 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 Data-dependent 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 data-dependent 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, A-K. Seghouane, and Y. Saad
  SIAM International Conference on Data Mining (SDM), 2019.
  [ arXiv, SIAM]
• Spectrum-Adapted 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, 19-33, 2018.
  [ Link, PDF]

2017

• UoI-NMF_cluster : A Robust Nonnegative Matrix Factorization Algorithm for Improved Parts-Based 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. Roosta-Khorasani, 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), 5544-5558, 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 Abd-Krim Seghouane
  Neural Computation, 29(5):1317-1351, 2017.
  [ arXiv, NC]
• Improving the Incoherence of a Learned Dictionary via Rank Shrinkage
  S. Ubaru, Abd-Krim Seghouane, and Yousef Saad
  Neural Computation, 29(1):263-285, 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 low-weight 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 multi-line addressed LCD
  S. Ubaru and Temkar N. Ruckmongathan
  IEEE, Journal of Display Technology, vol 8, no. 11, pp 669–677, November, 2012.
  [ JDT]