Zheng Zhang's Homepage

	Assistant Professor [IEEE-Style Short Biography, Full CV (PDF)] Deptatment of Electrical & Computer Engineering, University of California, Santa Barbara (UCSB) Deptartment of Computer Science (joint appointment), UCSB Department of Mathematics, (joint appointment, effective in 07/2019), UCSB
	Education Ph.D in EECS, Massachusetts Institute of Technology M.Phil in EE, The University of Hong Kong B. Eng in EE, Huazhong University of Science & Technology
	Contact Email: zhengzhang [AT] ece [dot] ucsb [dot] edu, Phone: 805-893-7294 Address: 4109 Harold Frank Hall, University of California, Santa Barbara, CA 93106

Post-doc Positions Available: Bayesian inference for AI or AI fairness. please check here for details.

Checklist for paper writing: I have prepared a detailed checklist to help science/engineering graduate students improving their paper writing.

To prospective PhD students: Please read this document if you are thinking about pursuing a PhD degree. The skill sets required for PhD research are very different from those required for undergraduate study. In undergraduate study, a student learns existing knowledge that were created by others (probably a few hundred years ago). A PhD student is expected to create new knowledge. A student doesn't have to be super smart or to have a perfect GPA in order to be an excellent PhD student, but he/she may need to be self-motivated for scientific research, curious about unknown/new fields, open-minded to different opinions, and persistent when facing research challenges (or even failures).

RESEARCH INTERESTS

We work on two topics of mathematical/computational data science: uncertainty quantification and tensor computation. We currently focus on the following application fields:

Design automation methodology of electronic and photonic integrated circuits (IC) under various uncertainties;
Algorithms and algorithm/hardware co-design of high-dimensional, safe and robust machine learning;
Quantum computing and the design automation aspects of quantum circuits.

Our research is supported by both government funding agencies (e.g., NSF, DOE) and industries (e.g., Facebook, Samsung, Xilinx and NVIDIA).

RECENT NEWS:

[Graduation] 03/22/2022: Our PhD student Cole Hawkins finished his PhD defense on Feb 28, and received his PhD degree in mathematics. Cole is the first PhD graduate from our group, and he will join Amazon as a research scientist to work on AI topics. Congratulations, Dr. Hawkins!!!
[Paper] 12/16/2021: Zichang's paper "PoBO: A polynomial bounding method for chance-constrained yield-aware optimization of photonic ICs" is accepted by IEEE Trans. CAD.
[Award] 11/01/2021: I received the IEEE CEDA (Council of Electronic Design Automation) Ernest S. Kuh Early Career Award. This award recognizes our group's contributions towards the fundamental stochastic computation methods for design automation under various uncertainties.
[Paper] 09/23/2021: Cole's paper "Towards compact neural networks via end-to-end training: a Bayesian tensor approach with automatic rank determination" is accepted by SIAM Journal on Mathematics of Data Science.
[Award] 06/25/2021: I received the ACM SIGDA Outstanding New Faculty Award.
[Paper] 06/23/2021: Zichang's paper "High-dimensional uncertainty quantification via tensor regression with rank determination and adaptive sampling" is accepted by IEEE Trans. Components, Packaging and Manufacturing Technology.
[Grant] 05/05/2021: We received a medium-scale research grant "Analog EDA-Inspired Methods for Efficient and Robust Neural Network Design" from NSF. Co-PIs of this project include Prof. Tsui-Wei Weng (UCSD and IBM) and Prof. Luca Daniel (MIT), who are our long-term collaborators.
[Paper] 1/12/2021: Zhuotong's paper "Towards robust neural networks via close-loop control" is accepted by ICLR 2021. This is a joint work with Prof. Qianxiao Li at NUS. This paper proposed a close-loop control method to improve the robustness of neural networks against various types of uncertainties and attacks.
[Award] 10/07/2020: Zichang's paper "High-dimensional uncertainty quantification via active and rank-adaptive tensor regression" received the best student paper award at IEEE EPEPS 2020.
[Grant] 09/10/2020: We received a research grant from DOE ASCR to investigate quantum-inspired Bayesian sampling and its application in machine learning and uncertainty quantification. DOE news.
[Paper] 07/23/2020: Chunfeng's paper "Active Subspace of Neural Networks: Structural Analysis and Universal Attacks" is accepted for publications in SIAM Journal on Mathematics of Data Science. This is a joint work with Prof. Ivan Oseledets and his students.
[Award] 07/12/2020: We received a Facebook Research Award to investigate software/hardware co-design of machine learning.
[Award] 05/04/2020: Our paper "Stochastic collocation with non-Gaussian correlated process variations: Theory, algorithms and applications" (first author: Chunfeng Cui) received the yearly Best Journal Paper Award from IEEE Trans. Components, Packaging and Manufacturing Technology.
[Award] 07/27/2019: Chunfeng Cui is selected as a Rising Star in EECS (Electrical Engineering and Computer Science). She will attend the Rising Stars workshop at University of Illinois, Urbana-Champaign.

SELECTED PUBLICATIONS

Z. Chen, Q. Li and Z. Zhang, "Self-healing robust neural networks via closed-loop control," arXiv preprint: arXiv:2206.12963, July 2022.
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.
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, the conference version received the best paper award at EPEPS'2020).
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), May 2021.
Z. Chen*, Q. Li* and Z. Zhang, "Towards robust neural networks via close-loop control," International Conference on Learning Representation (ICLR) 2021 (*Equally contributing authors)
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, Best Paper Award
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, vol. 7, no. 5, pp. 687-697, May 2017. Best Paper Award
Z. Liu and Z. Zhang, "Quantum-inspired Hamiltonian Monte Carlo for Bayesian sampling," submitted to Journal of Machine Learning Research (arXiv:1912.01937)
Z. Zhang, K. Batselier, H. Liu, L. Daniel and N. Wong, "Tensor computation: A new framework for high-dimensional problems in EDA," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 4, pp. 521-536, April. 2017. Invited Keynote Paper
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. Donald O. Pederson TCAD Best Paper Award
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, vol. 34, no. 1, pp. 63-76, Jan. 2015.

More publications...

ACADEMIC AWARDS

2021: ACM SIGDA Outstanding New Faculty Award (link); IEEE CEDA Ernest S. Kuh Early Career Award (link).
2020: Best Paper Award of IEEE Trans. on Components, Packaging and Manufacturing Technology (link to paper); Facebook Research Award; Best Student Paper Award at EPEPS (by PhD advisee Zichang He, link to paper).
2019: NSF CAREER Award; Rising Stars in Computational and Data Sciences (by my advisee Chunfeng Cui); Rising Stars in EECS (by my advisee Chunfeng Cui).
2018: Best Paper Award of IEEE Transactions on Components, Packaging and Manufacturing Technology (link to ppaer); Best Conference Paper Award at IEEE EPEPS (link to paper).
2016: ACM Outstanding PhD Dissertation Award in Electronic Design Automation (link); Best Paper Award at International Workshop on Signal and Power Integrity.
2015: MIT Microsystems Technology Labs (MTL) Doctoral Dissertation Seminar Award (link).
2014: Donald O. Pederson Best Paper Award of IEEE Transactions on CAD of Integrated Circuits and Systems ( link); Best Paper Nomination at IEEE CICC.
2011: Li Ka-Shing Prize (best M.Phil/Ph.D thesis award) from the University of Hong Kong (link); best paper nominations at ICCAD2011 and ASP-DAC2011.

ACADEMIC SERVICES

Associate Editor: ACM SIGDA Newsletters (2018-2019);
TPC Member: ICCAD (2016-2018), DAC (2017, 2018);
Award Committee: ACM SIGDA Best Dissertation Award Committee (2018), DAC Best Paper Award Committee (2018), ICCAD Best Paper Award Committee (2018)