Personal Homepage of Vassilis Kalantzis
Contact
IBM Research
Thomas J. Watson Research Center
1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA
vkal (at) ibm (dot) com
About
I am a Manager and Senior Research Scientist at IBM Research. Prior to my current role I was a Herman H. Goldstine Memorial Postdoctoral Fellow. I received my PhD (2018) and M.Sc (2016) in Computer Science and Engineering from the University of Minnesota, Twin Cities, U.S., and M.Sc (2014) and M.Eng (2011) in Computer Engineering and Informatics from the University of Patras, Greece.
PhD Thesis
I obtained my PhD (Advisor: Yousef Saad) in 09/2018 from the University of Minnesota, Twin Cities. My dissertation is titled "Domain decomposition algorithms for the solution of sparse symmetric generalized eigenvalue problems". You can view/download a copy here.
Publications
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Regenerative Ulam-von Neumann Algorithm: An Innovative Markov chain Monte Carlo Method for Matrix Inversion S. Ghosh, L. Horesh, V. Kalantzis, Y. Lu, and T. Nowicki SIAM Journal on Matrix Analysis and Applications (2025), To Appear |
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Multi-Sense Embeddings for Language Models and Knowledge Distillation Q. Wang, M. Zaki, G. Kollias, and V. Kalantzis In Proceedings of Findings of the 63rd Annual Meeting of the Association for Computational Linguistics (Findings of ACL 2025) |
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Differentiable Graph Centrality G. Kollias, and V. Kalantzis In Proceedings of the 29th IEEE Conference on High Performance Extreme Computing (HPEC 2025) |
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A Framework For The Iterative Solution of Sparse Linear Systems on Hybrid Architectures Using Homomorphic Encryption L. Horesh, V. Kalantzis, B. M. Trager, and S. Ubaru In Proceedings of the 29th IEEE Conference on High Performance Extreme Computing (HPEC 2025) |
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Power Iteration with Probabilistic Updates for Systems with Heterogeneous Performance S. Ghosh, L. Horesh, V. Kalantzis, G. Kollias, Y. Lu, T. Nowicki, and S. Ubaru In Proceedings of the 29th IEEE Conference on High Performance Extreme Computing (HPEC 2025) |
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Fast Linear Solvers via AI-Tuned Markov Chain Monte Carlo-based Matrix Inversion W. Lee, A. Lebedev, S. Ghosh, O. Yaman, V. Kalantzis, et al. Supercomputing Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems (ScalAH25) |
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Stable Iterative Refinement Algorithms for Solving Linear Systems C. W. Wu, M. S. Squillante, V. Kalantzis, and L. Horesh Journal of Computational and Applied Mathematics (2025), To Appear |
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On the Variance of Schatten p-Norm Estimation with Gaussian Sketching Matrices L. Horesh, V. Kalantzis, Y. Lu, and T. Nowicki Monte Carlo Methods and Applications (2025), To Appear DOI: 10.1515/mcma-2025-2006 |
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Single-Pass Top-N Subgraph Centrality of Graphs via Subspace Projections V. Kalantzis, G. Kollias, S. Ubaru, N. Abe, and L. Horesh Journal of Complex Networks (2025), Vol. 13, No. 1 DOI: 10.1093/comnet/cnae049 |
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Straggler-tolerant Stationary Methods for Linear Systems V. Kalantzis, Y. Xi, L. Horesh, and Y. Saad SIAM Journal on Scientific Computing (2025), To Appear DOI: 10.1137/24M1673346 |
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A Rational Filtering Algorithm for Sequences of Shifted Symmetric Linear Systems with Applications to Frequency Response Analysis A. P Austin, L. Horesh, and V. Kalantzis SIAM Journal on Scientific Computing (2024), Vol. 46, No. 6, pp. A3552-A3573 DOI: 10.1137/23M1578474 Software: Matlab |
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A Parallel Algorithm for Computing Partial Spectral Factorizations of Matrix Pencils via Chebyshev Approximation T. Xu, A. P. Austin, V. Kalantzis, and Y. Saad SIAM Journal on Scientific Computing (2024), Vol. 46, No. 2, pp. S324-S351 DOI: 10.1137/22M1501155 Software: Github |
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Asynchronous Randomized Trace Estimation V. Kalantzis, S. Ubaru, C. W. Wu, G. Kollias, and L. Horesh In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024) [Acceptance rate: 28%] Proceedings of Machine Learning Research Software: Github |
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Topological Data Analysis on Noisy Quantum Computers I. Y. Akhalwaya, S. Ubaru, K. L. Clarkson, M. S. Squillante, V. Jejjala, Y. He, K. Naidoo, V. Kalantzis, and L. Horesh In Proceedings of the 12th International Conference on Learning Representations (ICLR 2024) Selected for oral presentation (1.2% of total submissions) [Acceptance rate: 31%] OpenReview Archive |
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Directed Graph Transformers Q. Wang, G. Kollias, V. Kalantzis, N. Abe, and M. J. Zaki Transactions on Machine Learning Research (2024) OpenReview Archive Software: Github |
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Counting Triangles of Graphs via Matrix Partitioning P. Traganitis, V. Kalantzis, S. Ubaru, G. Kollias, and L. Horesh In Proceedings of the 34th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2024) 10.1109/MLSP58920.2024.10734796 |
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Solving Sparse Linear Systems via Flexible GMRES with In-Memory Analog Preconditioning V. Kalantzis et al. In Proceedings of the 27th IEEE Conference on High Performance Extreme Computing (HPEC 2023) DOI: 10.1109/HPEC58863.2023.10363502 |
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Matrix Resolvent Eigenembeddings for Dynamic Graphs V. Kalantzis and P. Traganitis In Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023) DOI: 10.1109/ICASSP49357.2023.10096476 |
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Quantum Graph Transformers G. Kollias, V. Kalantzis, T. Salonidis, and S. Ubaru In Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023) DOI: 10.1109/ICASSP49357.2023.10096345 |
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Accelerating Matrix Trace Estimators by Aitken’s Δ-squared Process V. Kalantzis, G. Kollias, S. Ubaru, and T. Salonidis In Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023) DOI: 10.1109/ICASSP49357.2023.10095081 |
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parGeMSLR: A Parallel Multilevel Schur Complement Low-Rank Preconditioning and
Solution Package for General Sparse Matrices T. Xu, V. Kalantzis, R. Li, Y. Xi, G. Dillon, and Y. Saad Parallel Computing (2022), Vol. 113 DOI: 10.1016/j.parco.2022.102956 Software: Github |
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Enhanced Algebraic Substructuring for Symmetric Generalized Eigenvalue Problems V. Kalantzis and L. Horesh Numerical Linear Algebra with Applications (2022), Vol. 30, No. 2 DOI: 10.1002/nla.2473 |
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Directed Graph Auto-Encoders G. Kollias, V. Kalantzis, T. Ide, A. Lozano, and N. Abe In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022) [Acceptance rate: 15%] DOI: 10.1609/aaai.v36i7.20682 Software: Github |
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Fast Randomized non-Hermitian Eigensolvers Based on Rational Filtering and Matrix Partitioning V. Kalantzis, Y. Xi, and L. Horesh SIAM Journal on Scientific Computing (2021), Vol. 43, No. 5, pp. S791-S815 DOI: 10.1137/19M1280004 |
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Solving Sparse Linear Systems with Approximate Inverse Preconditioners on Analog Devices V. Kalantzis et al. In Proceedings of the 25th IEEE Conference on High Performance Extreme Computing (HPEC 2021) DOI: 10.1109/HPEC49654.2021.9622816 |
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Projection Techniques to Update the Truncated SVD of Evolving Matrices with Applications V. Kalantzis, G. Kollias, S. Ubaru, A. N. Nikolakopoulos, L. Horesh, and K. L. Clarkson In Proceedings of the 38th International Conference on Machine Learning (ICML 2021) [Acceptance rate: 21%] Proceedings of Machine Learning Research |
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Sparse Graph-Based Sketching for Numerical Linear Algebra D. Hu, S. Ubaru, A. Gittens, L. Horesh, K. L. Clarkson, and V. Kalantzis In Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021) DOI: 10.1109/ICASSP39728.2021.9414030 |
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A Domain Decomposition Rayleigh-Ritz Algorithm for Symmetric Generalized Eigenvalue Problems V. Kalantzis SIAM Journal on Scientific Computing (2020), Vol. 42, No. 6, pp. C410-C435 DOI: 10.1137/19M1280004 |
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A Spectral Newton-Schur Algorithm for the Solution of Symmetric Generalized
Eigenvalue Problems V. Kalantzis Electronic Transactions on Numerical Analysis (2020), Vol. 52, pp. 132-153 DOI: 10.1553/etna_vol52s132 |
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TeraPCA: a Fast and Scalable Method to Study Genetic Variation in Tera-scale Genotypes A. Bose, V. Kalantzis, E. M. Kontopoulou, M. Elkadi, P. Paschou, and P. Drineas Bioinformatics (2019), Volume 35, Issue 19, pp. 3679-3683 DOI: 10.1093/bioinformatics/btz157 Software: C++ |
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Stable Computation of Generalized Matrix Functions via Polynomial Interpolation J. L. Aurentz, A. P. Austin, M. Benzi, and V. Kalantzis SIAM Journal on Matrix Analysis and Applications (2019), Vol. 40, No. 1, pp. 210-234 DOI: 10.1137/18M1191786 |
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EIGENREC: Generalizing PureSVD for Effective and Efficient Top-N Recommendations A. N. Nikolakopoulos, V. Kalantzis, E. Gallopoulos, and J. D. Garofalakis Knowledge and Information Systems (2019), Vol. 58, No. 1, pp. 59-81 DOI: 10.1007/s10115-018-1197-7 |
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Beyond Automated Multilevel Substructuring: Domain Decomposition with Rational Filtering V. Kalantzis, Y. Xi, and Y. Saad SIAM Journal on Scientific Computing (2018), Vol. 40, No. 4, pp. C477-C502 DOI: 10.1137/17M1154527 |
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A Hierarchical Low-Rank Schur Complement Preconditioner for Indefinite Linear Systems G. Dillon, V. Kalantzis, Y. Xi, and Y. Saad SIAM Journal on Scientific Computing (2018), Vol. 40, No. 4, pp. A2234-A2252 DOI: 10.1137/17M1143320 |
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Domain Decomposition Approaches for Accelerating Contour Integration Eigenvalue Solvers for Symmetric Eigenvalue Problems V. Kalantzis, J. Kestyn, E. Polizzi, and Y. Saad Numerical Linear Algebra with Applications (2018), Vol. 25, No. 5 DOI: 10.1002/nla.2154 |
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A Scalable Iterative Dense Linear System Solver for Multiple Right-Hand Sides in Data Analytics V. Kalantzis, C. Malossi, C. Bekas, A. Curioni, E. Gallopoulos, and Y. Saad Parallel Computing (2018), Vol. 74, pp. 136-153 DOI: 10.1016/j.parco.2017.12.005 Software: Fortran |
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Cucheb: A GPU Implementation of the Filtered Lanczos Procedure J. L. Aurentz, V. Kalantzis, and Y. Saad Computer Physics Communications (2017), Vol. 220, pp. 332-340 DOI: 10.1016/j.cpc.2017.06.016 Software: C++/CUDA |
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Factored Proximity Models for Top-N Recommendations A. N. Nikolakopoulos, V. Kalantzis, E. Gallopoulos, and J. D. Garofalakis In Proceedings of the 8th IEEE International Conference on Big Knowledge (ICBK 2017) [Acceptance rate: 27%] DOI: 10.1109/ICBK.2017.14 Software: Fortran |
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PFEAST: A High Performance Sparse Eigenvalue Solver Using Distributed-Memory Linear Solvers J. Kestyn, V. Kalantzis, E. Polizzi, and Y. Saad In Proceedings of the 2016 ACM/IEEE Supercomputing Conference (SC 2016) [Acceptance rate: 18%] DOI: 10.1109/SC.2016.15 |
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Spectral Schur Complement Techniques for Symmetric Eigenvalue Problems V. Kalantzis, R. Li, and Y. Saad Electronic Transactions on Numerical Analysis (2016), Vol. 45, pp. 305-329 |
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Estimating the Trace of the Matrix Inverse by Interpolating from the Diagonal of an Approximate Inverse L. Wu, J. Laeuchli, V. Kalantzis, A. Stathopoulos, and E. Gallopoulos Journal of Computational Physics (2016), Vol. 326, pp. 828-844 DOI: 10.1016/j.jcp.2016.09.001 |
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Accelerating Data Uncertainty Quantification by Solving Linear Systems with Multiple Right-Hand Sides V. Kalantzis, C. Bekas, A. Curioni, and E. Gallopoulos Numerical Algorithms (2013), Vol. 62, No. 4, pp. 637-653 DOI: 10.1007/s11075-012-9687-2 |
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Disclaimer
Any opinions, statements, or conclusions expressed in the above reports do not necessarily reflect the views of the acknowledged funding sponsors or International Business Machines Corporation, Inc.