FULL PUBLICATION LIST

Preprint

1. H. Chen and Z. Zhang, "Stochastic model predictive control of autonomous systems with non-Gaussian correlated uncertainty," submitted to American Control Conference (ACC 2020).

2. Z. Liu and Z. Zhang, "Quantum-inspired Hamiltonian Monte Carlo for Bayesian sampling," submitted to Journal of Machine Learning Research. arXiv:1912.01937

3. R. Solgi, Z. He, W. J. Liang and Z. Zhang, "Evolutionary tensor shape search for optimum data compression with tensor train decomposition," submitted to Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) 2022.

5. Z. Yang, J. Shan and Z. Zhang, "Hardware-efficient mixed-precision CP tensor decomposition," submitted to SIAM J. Mathematics of Data Sciences, 2022.

2025

[C56]. Y. Zhao, X. Xiao, A. Descos, Y.Yuan, X. Yu, G. Kurczveil, M. Fiorentino, Z. Zhang and R. Beausoleil, "Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training," Optical Communication Conference (OFC), March 2025.

2024

[J45] X. Yu, S Hooten, Z. Liu, Y. Zhao, M. Fiorentino, T. Van Vaerenbergh and Z. Zhang, "Separable operator networks," Transactions on Machine Learning Research (TMLR), Dec. 2024

[C55]. X.Yu, S. Hooten, Z. Liu, Y. Zhao, M. Fiorentino, T. Van Vaerenbergh and Z. Zhang, "SepONet: Efficient Large-Scale Physics-Informed Operator Learning," NeuRIPS Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers (D3S3), Dec. 2024.

[C54]  Z. Yang, Z. Liu, S. Choudhary, X. Xie, C. Gao, S. Kunzmann and Z. Zhang, “CoMERA: Computing- and Memory-Efficient Pre-Training via Rank-Adaptive Tensor Optimization,”  Annual Conference on Neural Information Processing Systems (NeuRIPS), Vancouver, Canada, December 2024 (acceptance rate: 25.8%)

[C53] Y. Yang, K. Zhen, E. Banijamali, A. Mouchtaris and Z. Zhang, "AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning," Conference on Empirical Methods in Natural Language Processing (EMNLP), Miami Florida, Nov. 2024.

[J44]  Z. He, B. Peng, Y. Alexeev and Z. Zhang, "Distributionally Robust Variational Quantum Algorithms with Shifted Noise," IEEE Trans. Quantum Computing, vol. 5, pp. 1-12,  June 2024.

[C52] J. Lee, S. González-García, Z. Zhang and H. Jeong, "Coded Computing Meets Quantum Circuit Simulation: Coded Parallel Tensor Network Contraction Algorithm," Intl. Symp. Information Theory, Athen, Greece, July 2024.

[J43] Z. Chen, Q. Li, Z. Wang, Y. Yang, Y. Zhao and Z. Zhang, "PID Control-Based Self-Healing to Improve the Robustness of Large Language Models," Transactions on Machine Learning Research (TMLR), 2024.

[C51] Y. Yang, J. Zhou, N. Wong and Z. Zhang, "LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models," Conf. Northern American Association of Computational Linguistics (NAACL), Mexico City, Mexico, June 2024. (Acceptance rate: 22%, selected as top 5% for oral presentation)

[C50] A. Chen, Y. Zhang, J. Jia, J. Diffenderfer, J. Liu, K. Parasyris, Y. Zhang, Z. Zhang, B. Kailkhura, S. Liu, "DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training," International Conference on Learning Representations (ICLR), May 2024. (Acceptance rate: 31%)

2023

[J42] X. Yu, J. C. Serralles, I. Giannakopoulos, Z. Liu, L. Daniel, R. Lattanzi and Z. Zhang, "PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks," IEEE Journal on Multiscale and Multiphysics Computational Techniques, Vol. 9, pp. 49-60, Dec. 2023.

[C49] Y Zhao, X Xian, X Yu, Z Liu, Z Chen, G Kurczveil, RG Beausoleil, Z Zhang, "Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training, " NeuRIPS Workshop on Machine Learning with New Computing Paradigm (MLNCP), Dec. 2023.

[C48] Y. Pan, Z. He, N. Guo and Z. Zhang, "Distributionally robust circuit design optimization under variation shifts," Intl. Conf. Computer-Aided Design (ICCAD), 8 pages, San Francisco, CA, Oct. 2023. (Acceptance rate: 22.9%)

[C47] Z. Chen, Q. Li and Z. Zhang, "Fairness in non-stationary environment from an optimal control perspective," ICML Workshop Frontiers4LCD, 19 pages, Hawaii, July 2023. 

[C46] Z. Yang, S. Choudhary, S. Kunzmann, and Z. Zhang, “Quantization-aware and tensor-compressed training of transformers for natural language understanding,” INTERSPEECH, 6 pages, Dublin, Ireland, Aug. 2023.

[C45] Y. Zhao, X. Xiao, G. Kurczveil, R.G. Beausoleil and Z. Zhang, “Tensorized optical multimodal fusion network,” CLEO, 2 pages, San Jose, CA, May 2023.

[C44] Z. Liu, Y. Li, J. Hu, X. Yu, Xin Ai, Z. Zeng, and Z. Zhang, “DeepOHeat: Operator learning-based ultra-fast thermal simulation in 3D-IC design,” ACM/IEEE Design Automation Conference (DAC), PP. 1-6, San Francisco, CA, June 2023 (Acceptance rate: 22.7%)

2022

[J41] C. Lee, Z. Zhang and S. Janušonis, "Brain serotonergic fibers suggest anomalous diffusion-based dropout in artificial neural networks," Frontiers in Neuroscience, pp. 1-9, Oct. 2022.

[J40] Z. Chen, Q. Li and Z. Zhang, "Self-healing robust neural networks via closed-loop control," Journal of Machine Learning Research, vol. 23, no. 319, pp. 1-54, 2022.

[C43] D. Kochan, Z. Zhang and X. Yang, "A quantum-inspired Hamiltonian Monte Carlo method for missing data imputation," 3rd Annual Conference on Mathematical and Scientific Machine Learning, pp. 1-18, 2022.

[C42] X. Yu, J. E. C. Serrallés, I. I. Giannakopoulos, Z. Liu, L. Daniel, R. Lattanzi and Z.Zhang, "MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks," QMR Lucca workshop on MR Phase, Magnetic Susceptibility and Electrical Properties Mapping, Lucca, Italy, Oct. 2022.

[C41] Z. Liu*, X. Yu* and Z. Zhang, "TT-PINN: A tensor-compressed neural PDE solver for edge computing," accepted by ICML Workshop on Hardware-Aware Efficient Training, 2022. (*Equal contribution)

[C40] Z. He, B. Zhao and Z. Zhang, "Active sampling for accelerated MRI with low-rank tensors," accepted by Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Glasgow, Scotland, July 2022.

2021

[J39] Z. He and Z. Zhang, "PoBO: A Polynomial Bounding Method for Chance-Constrained Yield-Aware Optimization of Photonic ICs" accepted by IEEE Trans. CAD of Integrated Circuits and Systems, arXiv: 2107.12593.

[C39] D. Lykov*, A. Chen*, H. Chen*, K. Keipert, Z. Zhang, T. Gibbs and Y. Alexeev, "Performance evaluation and acceleration of the QTensor quantum circuit simulator on GPUs," International Conference for High Performance Computing, Networking, Storage, and Analysis 2021 (SC2021), 8 pages, Nov. 2021. (* equal contributions)

[J38] C. Hawkins, X. Liu and Z.Zhang, "Towards compact neural networks via end-to-end training: a Bayesian tensor approach with automatic rank determination," SIAM Journal on Mathematics of Data Science, vol. 4, no. 1, pp. 46-71, Jan. 2022.  Github codes

[J37] Z. He and Z. Zhang, "High-dimensional uncertainty quantification via tensor regression with rank determination and adaptive sampling," IEEE Trans. Components, Packaging and Manufacturing Technology, vol. 11, no. 9, pp. 1317 - 1328, Sept. 2021. (invited paper).

[C38]Z. He and Z. Zhang, "Progress of tensor-based high-dimensional uncertainty quantification of process variations," Applied Computational Electromagnetics Society Conference (ACES), July 2021. (invited paper)

[C37] Y. Chen, C. Hawkins, K. Zhang, Z. Zhang and C. Hao, "3U-EdgeAI: Ultra-low memory training, ultra-low bitwidth quantization, and ultra-low latency acceleration," Proc. ACM Great Lakes Symposium on VLSI (GLVLSI), pp. 1-6, June 2021 (Invited Paper)

[J36] C. Hawkins and Z. Zhang, "Bayesian tensorized neural networks with automatic rank selection," Neurocomputing, vol. 453, pp. 172-180, Sept. 2021. (early arXiv version in May 2019: link)

[C27] K. Zhang, C. Hawkins, X. Zhang, C. Hao and Z. Zhang, "On-FPGA training with ultra memory reduction: A low-precision tensor method," ICLR Workshop on Hardware-Aware Efficient Training (HAET), pp. 1-9, May 2021.

[J35] Z. Qu, L. Deng, B. Wang, H. Chen, J. Lin, L. Liang, G. Li, Z. Zhang and Y. Xie, "Hardware-enabled efficient data processing with tensor-train decomposition," IEEE Trans. Computer-Aided Design for Integrated Circuits and Systems.

[C26] M. Wicker, L. Laurenti, A. Patane, Z. Chen, Z. Zhang and M. Kwiatowska, "Bayesian inference with certifiable adversarial robustness," International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 2431-2439. April 2021.

[C25]. Z. Chen*, Q. Li* and Z. Zhang, "Towards robust neural networks via close-loop control," International Conference on Learning Representation (ICLR), May 2021, 22 pages. (*Equal contributing authors) Open-source codes at GitHub.

[J34]. L. Liang, L. Deng, J. Xu, M. Yan, X. Hu, Z. Zhang, G. Li and Y. Xie, "Fast search of the optimal contraction sequence in tensor networks," IEEE Journal of Selected Topics in Signal Processing, vo. 15, no. 3, pp. 574-586, April 2021.

2020

[J33]. B. Wang, L. Deng, Z. Qu, S. Li, Z. Zhang, X. Yuan, "Efficient processing of sparse tensor decomposition via unified abstraction and PE-interactive architecture," accepted by IEEE Trans. Computers, 14 pages.

[J32] W. Jiang, K. Zhang, C. Y. Lin, F. Xing and Z. Zhang, "Sparse Tucker tensor decomposition on a hybrid FPGA/CPU platform," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems, vol. 40, no. 9, pp. 1864-1873, Sept. 2020.

[C24] Z. He and Z. Zhang, "High-dimensional uncertainty quantification via active and rank-adaptive tensor regression," IEEE Electrical Performance of Electronic Packaging and Systems (EPEPS), San Jose, Oct. 2020. (Best Student Paper Award)

[J31].  C. Cui, K. Zhang, T. Daulbaev, J. Gusak, I. Oseledets and Z. Zhang, "Active subspace of neural networks: Structural analysis and universal attacks," SIAM Journal on Mathematics of Data Science, vol. 2, no. 4, pp. 1096-1122, 2020.  arXiv:1910.13025

[J30]. C. Cui*, K. Liu* and Z. Zhang, "Chance-constrained and yield-aware optimization of photonic IC with non-Gaussian correlated process variations," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 12, pp. 4958-1970, Dec. 2020 (* equally contributing authors) Matlab codes

2019

[J29] Z. Chen, L. J. Gomez, S. Zheng, A. C. Yucel, Z. Zhang and V. Okhmatovski, "Sparsity-aware pre-corrected tensor train algorithm for fast solution of 2-D scattering problems and current flow modeling on unstructured meshes," IEEE Trans. Microwave Theory and Techniques, vol. 67, no. 12, pp. 4833-4847, Dec. 2019.

[C23] C. Cui*, C. Hawkins* and Z. Zhang, "Tensor methods for generating compact uncertainty quantification and deep learning models," International Conf. Computer Aided Design (ICCAD), 6 pages, Westminster, CO, Nov. 2019. (Invited Special Session Paper, * equally contributing authors).

[C22] Z. He, W. Cui, C. Cui, T. Sherwood and Z. Zhang, "Efficient uncertainty modeling for system design via mixed integer programming," International Conf. Computer Aided Design (ICCAD), 8 pages, Westminster, CO, Nov. 2019.

[C21] K. Zhang, X. Zhang and Z. Zhang, "Tucker tensor decomposition on FPGA," International Conf. Computer Aided Design (ICCAD), 8 pages, Westminster, CO, Nov. 2019. (arXiv:1907.01522)

[J28] C. Cui and Z. Zhang, "High-dimensional uncertainty quantification of electronic and photonic IC with non-Gaussian correlated process variations," accepted by IEEE Trans. CAD of Integrated Circuits and Systems (TCAD), 12 pages. (arXiv:1902.00004)

[C20] C. Cui and Z. Zhang, "Recent advancements of uncertainty quantification with non-Gaussian correlated process variations," IEEE MTT-S Conf. Numerical Electromagnetic & Multiphysics Modeling & Optimization (NEMO), 3 pages, Cambridge, MA, May 2019. (Invited Paper).

[J27] J. Luan and Z. Zhang, "Prediction of multi-dimensional spatial variation data via Bayesian tensor completion," IEEE Trans. CAD of Integrated Circuits and Systems (TCAD), vol. 39, no. 2, pp. 547-551, Feb. 2020.

[J26] G. Gruosso, G. Gajani, Z. Zhang, L. Daniel and P. Maffezzoni, "Uncertainty-aware computational tools for power distribution networks including electrical vehicle charging and loads profiles," IEEE Access, vol. 7, pp. 9357-9367, Jan. 2019.

[J25] C. Cui and Z. Zhang, "Stochastic collocation with non-Gaussian correlated process variations: Theory, algorithms and applications," IEEE Trans. Components, Packaging and Manufacturing Technology, vol. 9, no. 7, pp. 1362-1375, July 2019. (arXiv:1808.09720) Matlab codes, Yearly Best Paper Award, selected as a Popular Paper

[C19] A. Wahba, Li-C. Wang, Z. Zhang and N. Sumikawa, "Wafer pattern recognition using Tucker decomposition," IEEE VLSI Test Symp., 6 pages, Monterey, CA, April 2019.

[J24] G. Gruosso, P. Maffezzoni, Z. Zhang and L. Daniel, "Probabilistic load flow methodology for distribution networks including load uncertainty," International Journal of Electrical Power and Energy Systems, vol. 106, pp. 392-400, March 2019.

2018

[C18] C. Cui, M. Gershman and Z. Zhang, "Stochastic collocation with non-Gaussian correlated random parameters via a new quadrature rule," IEEE Electrical Performance of Electronic Packaging and Systems (EPEPS), San Jose, Oct. 2018. (Best Conference Paper Award)

[C17] C. Hawkins and Z. Zhang, "Variational Bayesian inference for robust streaming tensor factorization and completion," IEEE Intl. Conf. Data Mining (ICDM), Singapore, Nov. 2018. (acceptance rate=19.9%).

[C16] C. Cui and Z. Zhang, "Uncertainty quantification of electronic and photonic ICs with non-Gaussian correlated process variations," ACM Intl. Conf. Computer-Aided Design (ICCAD), 8 pages, Nov. 2018. (acceptance rate=24.7%)

[J23] P. Maffezzoni, Z. Zhang, S. Levantino and L. Daniel, "Variation-aware modeling of integrated capacitors based on floating random walk extraction," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 37, no. 10, pp. 2180-2184 , Aug. 2018.

2017

[J22] Z. Zhang, L. Daniel, K. Batselier, H. Liu and N. Wong, "Tensor computation: A new framework for high-dimensional problems in EDA,"  IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 36, no. 4, pp. 521-536, April 2017. Invited Keynote Paper, Popular Paper

[J21] N. Petra, C. Petra, Z. Zhang, E. Constantinescu and M. Anitescu, "A Bayesian approach for parameter estimation with uncertainty for dynamic power systems," IEEE Trans. Power Systems, vol. 32, no. 4, pp. 2735-2743, July 2017. (arXiv, PDF)

[J20] Z. Zhang, T.-W. Weng and L. Daniel, "Big-data tensor recovery for high-dimensional uncertainty quantification of process variations," IEEE Trans. Components, Packaging and Manufacturing Technology (T-CPMT), vol. 7, no. 5, pp. 687-697, May 2017. Invited Paper, Popular Paper, Best Paper Award

2016

[J19] P. Maffezzoni, B. Bahr, Z. Zhang and L. Daniel, "Analysis and design of Boolean associative memories made of resonant oscillator arrays," IEEE Trans. Circuits and Systems I: Regular Papers (TCAS-1), vol. 63, no. 11, pp. 1964-1973, Nov. 2016

[J18] P. Maffezzoni, B. Bahr, Z. Zhang and L. Daniel, "Reducing phase noise in multi-phase oscillators," IEEE Trans. Circuits and Systems I: Regular Papers (TCAS1), vol. 63, no. 3, pp. 379-388, March 2016.

[C14] Z. Zhang, T.-W. Weng and L. Daniel, "A big-data approach to handle process variations: uncertainty quantification by tensor recovery," IEEE Workshop on Signal and Power Integrity, 4 pages, Turin, Italy,  May 2016. (IEEE, arXiv) Best Oral Paper Award.

2015

[T2] Z. Zhang, "Uncertainty quantification of integrated circuits and microelectromechanical systems,"  PhD Dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, June 2015. ACM Outstanding PhD Dissertation Award in Electronic Design Automation, MIT MTL Doctoral Dissertation Award.

[R1] Z. Zhang, H. D. Nguyen, K. Turitsyn and L. Daniel, "Probabilistic power flow computation via low-rank and sparse tensor recovery,"  arXiv:1508.02489 (arXiv, PDF)

[J17] P. Maffezzoni, B. Bahr, Z. Zhang and L. Daniel, "Oscillator array models for associative memory and pattern recognition," IEEE Trans. Circuits and Systems I: Regular Papers (TCAS1), vol. 62, no. 6, pp. 1591-1598, June 2015. (IEEE)

[J16] T.-W. Weng*, Z. Zhang*, Z. Su, Y. Marzouk, A. Melloni and L. Daniel, "Uncertainty quantification of silicon photonic devices with correlated  and non-Guassian random parameters," Optics Express, vol. 23, no. 4, pp. 4242-4254, Feb. 2015. (OE manuscript, *equally contributing authors)

[J15] Z. Zhang, X. Yang, I. V. Oseledets, G. E. Karniadakis and L. Daniel, "Enabling high-dimensional hierarchical uncertainty quantification by ANOVA and tensor-train decomposition," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 34, no. 1, pp. 63-76, Jan. 2015 (arXiv preprint, IEEE) Popular Paper

[J14] P. Maffezzoni, B. Bahr, Z. Zhang and L. Daniel, "Analysis and design of weakly-coupled oscillator arrays based on phase-domain macromodels," IEEE Trans. CAD of Integrated Circuits and Systems (TCAD), vol. 34, no. 1, pp. 77-85, Jan. 2015

2014

[J13] Z. Zhang*, N. Niloofar*, T. Klemas and L. Daniel, "Maximum-entropy density estimation for MRI stochastic surrogate models," IEEE Antennas and Wireless Propagation Letters (AWPL), vol. 13,  pp. 1656-1659, 2014. (IEEE, *equally contributing authors)

[J12] Z. Zhang, T. A. El-Moselhy, I. M. Elfadel and L. Daniel, "Calculation of generalized polynomial-chaos basis functions and Gauss quadrature rules in hierarchical uncertainty quantification," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 33, no. 5, pp. 728-740, May 2014. (IEEE, arXiv, Errata) Popular Paper

[J11] Z. Zhang, M. Kamon and L. Daniel, "Continuation-based pull-in and lift-off simulation algorithms for microelectromechanical devices," IEEE/ASME Journal of Microelectromechanical Systems (JMEMS), vol. 23, no. 5, pp. 1084-1093, Oct. 2014 (IEEE)

[J10] P. Maffezzoni, Z. Zhang and L. Daniel, "A study of deterministic jitter in crystal oscillators," IEEE Trans. Circuits and Systems I: Regular Papers (TCAS1), vol. 61, no. 4, pp. 1044-1054, April 2014 (IEEE) TCAS1 Popular Paper

[J9] Z. Zhang and N. Wong, "Canonical projector techniques for analyzing descriptor systems," Int. Journal of Control, Automation and Systems (IJCAS), vol. 12, no. 1, pp. 71-83, Feb. 2014 (PDF)

[C13] T.-W. Weng,  Z. Zhang, Z. Su and L. Daniel, "Fast stochastic simulation of silicon waveguide with non-Gaussian correlated process variations," Asia Communication and Photonics Conf. (ACP), Shanghai, China, Nov. 2014

[C12] Z. Zhang, X. Yang, G. Marucci, P. Maffezzoni, I. M. Elfadel, G. Karniadakis and L. Daniel, "Stochastic testing simulator for integrated circuits and MEMS: Hierarchical and sparse techniques," IEEE Custom Integrated Circuits Conf. (CICC), 8 pages, San Jose, CA., Sept. 2014. (IEEE, arXiv) Invited Paper, Best Paper Nomination

2013

[J8] Z. Zhang, T. A. El-Moselhy, P. Maffezzoni, I. Elfadel and L. Daniel, "Efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits," IEEE Trans. Circuits and Systems II: Express Briefs (TCAS2), vol. 60, no.10,  pp. 687-691, Oct. 2013. (IEEE, arXiv)

[J7] Z. Zhang, T. A. El-Moselhy, I. M. Elfadel and L. Daniel, "Stochastic testing method for transistor-level uncertainty quantification based on generalized polynomial chaos," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 32, no. 10, pp. 1533-1545, Oct. 2013 (IEEE, arXiv)), Donald O. Pederson Best Paper Award, Popular Paper

[C11] Z. Zhang, I. M. Elfadel and L. Daniel, "Uncertainty quantification for integrated circuits: Stochastic spectral methods," IEEE/ACM Int. Conf. Computer-Aided Design (ICCAD), pp. 803-810, San Jose, CA, Nov. 2013. (IEEE, arXiv) Invited Paper

[C10] M. Kamon, S. Maity, D. Dereus, Z. Zhang, S. Cunningham, S. Kim, J. McKillop, A. Morris, G. Lorenz and L. Daniel,"New simulation and experimental methodology for analyzing pull-in and release in MEMS switches," IEEE Int. Conf. Solid-State Sensors, Actuators and Microsystems (Transducers and Eurosensors XXVII), pp. 2373-2376, Barcelona, Spain, June 2013. (IEEE)

2012

[J6] X. Wang, Z. Zhang, Q. Wang and N. Wong, "Gramian-based model order reduction of parameterized time-delay systems," Int. Journal of Circuit Theory and Applications (IJCTA), Dec. 2012 (PDF)

[J5] Y. Wang, Z. Zhang, C.-K. Koh, G. Shi, G. K. Pang and N. Wong, "Passivity enforcement for descriptor systems via matrix pencil perturbation," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 31, no. 4, pp. 532-545, April 2012 (IEEE)

2011

[C9] Z. Zhang, I. M. Elfadel and L. Daniel, "Model order reduction of fully parameterized systems by recursive least square optimization," IEEE/ACM Int. Conf. Computer-Aided Design (ICCAD), pp. 523-530, San Jose, CA, Nov. 2011. (IEEE) William J. McCalla ICCAD Best Paper Award Nomination

[C8] Z. Zhang, X. Hu, C.-K. Cheng and N. Wong, “A block-diagonal structured model reduction scheme for power grid networks,” IEEE/ACM Design, Automation and Test in Europe (DATE), pp. 44-49, Grenoble, France, Mar. 2011. (IEEE)

[C7] X. Wang, Q. Wang, Z. Zhang, Q. Chen and N. Wong, “Balance truncation for time-delay systems via approximate gramians,” IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 55-60, Yokohama, Japan, Jan. 2011. (IEEE)

[C6] Z. Zhang, Q. Wang, N. Wong and L. Daniel, “A moment-matching scheme for the passivity-preserving model order reduction of indefinite descriptor systems with possible polynomial parts,” IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 49-54, Yokohama, Japan, Jan. 2011. (IEEE), Best Paper Award Nomination

2010

[T1] Z. Zhang, "Passivity assessment and model order reduction for linear time-invariant descriptor systems in VLSI circuit simulation," MPhil Thesis, Department of Electrical and Electronic Engineering, the University of Hong Kong, Li Ka-Shing Prize (University-Wide Best Thesis Award).

[J4] Z. Zhang and N. Wong, “Passivity check of S-parameter descriptor systems via S-parameter generalized Hamiltonian methods,” IEEE Trans. Advanced Packaging (TADVP), vol. 33, no. 4, pp. 1034-1042, Nov. 2010 (IEEE)

[J3] Z. Zhang and N. Wong, “An efficient projector-based passivity test for descriptor systems,” IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 29, no. 8, pp. 1203-1214, Aug. 2010 (IEEE)

[J2] N. Wong and Z. Zhang, “Discussion of ‘a half-size singularity test matrix for fast and reliable passivity assessment of rational models’,”  IEEE Trans. Power Delivery (TPWRD), vol. 25, no. 2, pp. 1212-1213, April 2010 (IEEE)

[J1] Z. Zhang and N. Wong, “Passivity test of immittance descriptor systems based on generalized Hamiltonian methods”,  IEEE Trans. Circuits and Systems II: Express Briefs (TCAS2), vol. 57, no. 1, pp. 61-65, Jan 2010 (IEEE)

[C4] Y. Wang, Z. Zhang, C.-K. Koh, G. K.-H. Pang and N. Wong, "PEDS: Passivity enforcement for descriptor systems via Hamiltonian-symplectic matrix pencil perturbation," IEEE/ACM Int Conf. on Computer-Aided Design (ICCAD), pp. 800-807, San Jose, CA, Nov. 2010. (ACM)

[C3] C. Y. Lin, Z. Zhang, N. Wong and H. K.-H. So, "Power-delay and energy-delay tradeoffs in sparse matrix-matrix multiplication on FPGAs," in Proc. Int. Workshop on Highly Efficient Accelerators and Reconfigurable Technologies (HEART), Jun. 2010.

[C2] Z. Zhang and N. Wong, "An extension of the generalized Hamiltonian method to S-parameter descriptor systems ," IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC), pp.43-47, Taipei, Jan. 2010. (ACM)