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Scalable Solvers Group

Yang Liu

Yang Liu 2 square
Yang Liu
Research Scientist
Computational Research Division
Phone: 510-486-5283
Lawrence Berkeley National Laboratory
1 Cyclotron Road
MS 50A-3111
Berkeley, CA 94720

Yang Liu is a research scientist in the Scalable Solvers Group of the Computational Research Division at Lawrence Berkeley National Laboratory (LBL). He is currently working on scalable sparse direct solver development supported by the Exascale Computing Project. Before joining LBL, he was a postdoctoral fellow in the Radiation Laboratory at University of Michigan. For more information, please visit: https://liuyangzhuan.github.io


Research Interests

  • Numerical Linear Algebra -- sparse and dense direct solvers, butterfly algebras, and randomized algorithms. 
  • Computational Electromagnetics -- fast iterative and direct integral equation solvers and their applications. 
  • High Performance Computing -- communication avoiding algorithms, heterogeneous computing.

Education

  • Ph.D., Electrical Engineering, University of Michigan, May 2015. 
  • M.S., Mathematics, University of Michigan, Nov. 2014. 
  • M.S., Electrical EngineeringUniversity of Michigan, May 2013. 
  • B.S., Electrical Engineering, Shanghai Jiao Tong University, June, 2010. 

Journal Articles

S. B. Sayed, Y. Liu, L. J. Gomez, A. C. Yucel, "A butterfly-accelerated volume integral equation solver for broad permittivity and large-scale electromagnetic analysis", arxiv-preprint, November 5, 2021,

H. Luo, J.W. Demmel, Y. Cho, X. S. Li, Y. Liu, "Non-smooth Bayesian optimization in tuning problems", arxiv-preprint, September 21, 2021,

Yang Liu, Pieter Ghysels, Lisa Claus, Xiaoye Sherry Li, "Sparse Approximate Multifrontal Factorization with Butterfly Compression for High Frequency Wave Equations", SIAM J. Sci. Comput., June 22, 2021,

Yang Liu, Xin Xing, Han Guo, Eric Michielssen, Pieter Ghysels, Xiaoye Sherry Li, "Butterfly factorization via randomized matrix-vector multiplications", SIAM J. Sci. Comput., March 9, 2021,

Y. Liu, W. Sid-Lakhdar, E. Rebrova, P. Ghysels, X. Sherry Li, "A parallel hierarchical blocked adaptive cross approximation algorithm", The International Journal of High Performance Computing Applications, January 1, 2019,

H. Guo, Y. Liu, J. Hu, E. Michielssen, "A butterfly-based direct solver using hierarchical LU factorization for Poggio-Miller-Chang-Harrington-Wu-Tsai equations", Microwave and Optical Technology Letters, 2018, 60:1381-1387,

A. C. Yucel, W. Sheng, C. Zhou, Y. Liu, H. Bagci, E. Michielssen, "An FMM-FFT Accelerated SIE Simulator for Analyzing EM Wave Propagation in Mine Environments Loaded With Conductors", IEEE Journal on Multiscale and Multiphysics Computational Techniques, 2018, 3:3-15,

Y. Liu, A. C. Yucel, H. Bagci, A. C. Gilbert, and E. Michielssen, "Wavelet-enhanced plane-wave time-domain algorithm for analysis of transient scattering from electrically large conducting objects", IEEE Trans. Antennas Propag., 2017,

Y. Liu, H. Guo, and E. Michielssen, "A HSS matrix-inspired butterfly-based direct solver for analyzing scattering from two-dimensional objects", IEEE AntennasWireless Propag. Lett., 2017,

Y. Liu, A. Al-Jarro, H. Bagci, and E. Michielssen, "Parallel PWTD-accelerated explicit solution of the time domain electric field volume integral equation", IEEE Trans. Antennas Propag., 2016,

Y. Liu, A. C. Yucel, H. Bagci, and E. Michielssen, "A Scalable Parallel PWTD-Accelerated SIE Solver for Analyzing Transient Scattering From Electrically Large Objects", IEEE Trans. Antennas Propag., 2016,

Y. Liu, A. C. Yucel, V. Lomakin, and E. Michielssen, "Graphics processing unit implementation of multilevel plane-wave time-domain algorithm", IEEE Antennas Wireless Propag. Lett., 2014,

A. C. Yucel, Y. Liu, H. Bagci, and E. Michielssen, "Statistical characterization of electromagnetic wave propagation in mine environments", IEEE Antennas Wireless Propag. Lett., 2013,

Conference Papers

X. Zhu, Y. Liu, P. Ghysels, D. Bindal, X. S. Li, "GPTuneBand: multi-task and multi-fidelity Bayesian optimization for autotuning large-scale high performance computing applications", SIAM PP, February 23, 2022,

Y. Cho, J. W. Demmel, X. S. Li, Y. Liu, H. Luo, "Enhancing autotuning capability with a history database", IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), December 20, 2021,

Nan Ding, Yang Liu, Samuel Williams, Xiaoye S. Li, "A Message-Driven, Multi-GPU Parallel Sparse Triangular Solver", SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21), July 19, 2021,

Y. Liu, W. M. Sid-Lakhdar, O. Marques, X. Zhu, C. Meng, J. W. Demmel, X. S. Li, "GPTune: multitask learning for autotuning exascale applications", PPoPP, February 17, 2021, doi: 10.1145/3437801.3441621

Gustavo Chavez, Elizaveta Rebrova, Yang Liu, Pieter Ghysels, Xiaoye Sherry Li, "Scalable and memory-efficient kernel ridge regression", 34th IEEE International Parallel and Distributed Processing Symposium, July 14, 2020,

Nan Ding, Samuel Williams, Yang Liu, Xiaoye S. Li, "Leveraging One-Sided Communication for Sparse Triangular Solvers", 2020 SIAM Conference on Parallel Processing for Scientific Computing, February 14, 2020,

Yang Liu, Mathias Jacquelin, Pieter Ghysels, Xiaoye S Li, "Highly scalable distributed-memory sparse triangular solution algorithms", 2018 Proceedings of the Seventh SIAM Workshop on Combinatorial Scientific Computing, 2018, 87--96,

E. Rebrova, G. Chavez, Y. Liu, P. Ghysels, X. S. Li, "A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression", IEEE IPDPSW, 2018,

Book Chapters

Yang Liu, Eric Michielssen, "Parallel fast time-domain integral-equation methods for transient electromagnetic analysis", Parallel Algorithms in Computational Science and Engineering, ( July 7, 2020)