Assistant Professor [IEEE-Style Short Biography, Full CV
Deptatment of Electrical & Computer Engineering,
University of California, Santa Barbara (UCSB)
Deptartment of Computer Science (joint
Department of Mathematics, (joint appointment,
effective in 07/2019), UCSB
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
zhengzhang [AT] ece [dot] ucsb [dot] edu,
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
Checklist for paper writing: I have prepared a
detailed checklist to help science/engineering graduate
students improving their paper writing.
To prospective PhD students:
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
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
Our research is supported by both government funding agencies (e.g., NSF,
and industries (e.g., Facebook, Samsung, Xilinx and NVIDIA).
[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
[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
[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
[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
[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.
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.
Best Paper Award
Z. Zhang, T.-W. Weng and L. Daniel,
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,"
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.
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
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
Award at IEEE EPEPS (link
2016: ACM Outstanding PhD Dissertation Award in
Electronic Design Automation (link);
Best Paper Award at International Workshop on Signal and
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 (
Best Paper Nomination at IEEE CICC.
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.
Associate Editor: ACM SIGDA Newsletters
TPC Member: ICCAD (2016-2018), DAC
Award Committee: ACM SIGDA Best
Dissertation Award Committee (2018), DAC Best Paper Award Committee
(2018), ICCAD Best Paper Award Committee (2018)