Download - CV_ZhuoWang
Tel: +1(609)937-7476, Email: [email protected]
Website: http://scholar.princeton.edu/zhuowang
F210, E-Quad, Olden Street, Princeton, NJ, 08544, U.S.
Biography
Gender: Male; Date of Birth: Apr. 6, 1989; Place of Birth: Hebei, China
Education
05/13 – 08/16 (exp.) Ph. D Candidate, Princeton University, US
09/11 – 05/13 M. A., Electrical Engineering, Princeton University, US GPA: 3.844/4
09/07 – 07/11 B.S., Microelectronics, Peking University, China GPA: 3.86/4
Research Experience
02/12 till now Princeton Integrated Circuits and Systems Group
Advisor: Prof. Naveen Verma
Exploiting statistical and machine-learning techniques for achieving hardware resilience and relaxing
system energy constraints.
Designing and implementing embedded sensing systems for analyzing physically-complex signals.
Designing algorithms, architectures and circuits for emerging systems beyond CMOS IC, such as large-
area electronics and carbon-nanotube circuits.
09/08 – 06/11 Nano Devices and Integrated Circuits Research Group of Peking University
Advisor: Prof. Yunyi Fu
Investigating few-layer graphene, fabrication, characterization, electroluminescence devices, etc.
Teaching Experience
09/12 – 01/13 TA, ELE/COS462: Design of VLSI Circuits, Princeton University
02/13 – 05/13 TA, ELE404: Electronic Circuits for Biomedical Application, Princeton University
Professional Skills
Skills
Expertise: CMOS IC design, machine learning, embedded sensing systems, biomedical signal
processing, image processing, FPGA
Knowledgeable: large area electronics, carbon-nanotube circuits, electronic devices, statistics
Programming Language
Expertise: Verilog, Matlab, C
Knowledgeable: Python, Java, C++, x86 Assembly Language, Latex, Bash, VHDL
Hardware and System Design Tools
Xilinx: ISE Design Suite, MicroBlaze Soft Processor, Virtex-5 FPGA
TI: CCS, MSP430 Micro-processor
Synopsys: DC, VCS, Synplify, HSPICE, Nano Sim, ICC
Cadence: Virtuoso, Spectra
Operating Systems
Unix, Windows
Journal Publications
1. Z. Wang, J. Zhang, N. Verma, ‘Reducing Quantization Error in Low-energy FIR Filter Accelerators’, Journal of
Signal Processing Systems (JSPS), in preparation. (invited)
2. W. Rieutort-Louis, T. Moy, Z. Wang, S. Wagner, J. Sturm, N. Verma, ‘A Large-Area Image Sensing and Detection
Zhuo Wang
Zhuo Wang
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System Based on Embedded Thin-Film Classifiers’, IEEE Journal of Solid State Circuits (JSSC), submitted.
(invited)
3. Z. Wang, R. E. Schapire, N. Verma, ‘Error-Adaptive Classifier Boosting (EACB): Leveraging Data-driven Training
towards Hardware Resilience for Signal Inference’, IEEE Transactions on Circuits and Systems I (TCAS-I), vol. 62,
no. 4, pp. 1136-1145, Apr. 2015.
4. Z. Wang, K. H. Lee, N. Verma, ‘Overcoming Computational Errors in Low-power Sensing Platforms through
Embedded Machine-learning Kernels,’ IEEE Transactions on Very Large Scale Integration Systems (TVLSI), in
press.
5. Z. Wang, K. H. Lee, N. Verma, ‘Hardware Specialization in Low-power Sensing Applications to Address Energy and
Resilience’, Journal of Signal Processing Systems (JSPS), vol. 78, no. 1, pp. 49-62, Jan. 2015. (invited)
Conference Publications
1. J. Zhang, L. Huang, Z. Wang, N. Verma, ‘A Seizure-detection IC Employing Machine Learning to Overcome Data-
conversion and Analog-processing Non-idealities’, IEEE Custom Integrated Circuits Conference (CICC), to appear.
2. Z. Wang, J. Zhang, N. Verma, ‘Reducing Quantization Error in Low-energy FIR Filter Accelerators’, IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2015.
3. J. Zhang, Z. Wang, N. Verma, ‘A Matrix-Multiplying ADC (MMADC) Implementing a Machine-learning Classifier
Directly within Data Conversion’, IEEE International Solid-State Circuits Conference (ISSCC), Feb. 2015.
4. W. Rieutort-Louis, T. Moy, Z. Wang, S. Wagner, J. Sturm, N. Verma, ‘A Large-area Image Sensing and Detection
System Based on Embedded Thin-film Classifiers’, IEEE International Solid-State Circuits Conference (ISSCC),
Feb. 2015.
5. Z. Wang, N. Verma, ‘Enabling Hardware Relaxations through Statistical Learning’, IEEE Annual Allerton
Conference on Communication, Control, and Computing (Allerton), Oct. 2014. (invited)
6. Z. Wang, R. E. Schapire, N. Verma, ‘Error-Adaptive Classifier Boosting (EACB): Exploiting Data-driven Training
for Highly Fault-tolerant Hardware,’ IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), May 2014. (Best Student Paper Award Nomination)
7. K. H. Lee, Z. Wang, N. Verma, ‘Hardware Specialization of Machine-learning Kernels: possibilities for applications
and possibilities for the platform design space,’ IEEE Workshop on Signal Processing Systems (SiPS), Oct. 2013.
(invited)
Patents
1. A Matrix-Multiplying ADC Implementing a Machine-Learning Classifier Directly with Data Conversion, US Patent
application submitted, patent pending
2. A Thin-Film Sensing and Classification System, US Patent application submitted, patent pending
3. Nanoscale point light source based on graphene and preparation method thereof, CN Patent 102,082,159, filed Oct.
2010, issued Jul. 2012
4. Array of graphene-based nano-scale point sources, CN Patent 102,034,845, filed Oct. 2010, issued Jun. 2012
Workshop/Meeting Presentations
1. Z. Wang, G. Hills, S. Mitra, N. Verma, ‘Fault Tolerance over Large Digital Architectures,’ SONIC Annual Review
Symposium, Oct. 2014.
2. J. Zhang, Z. Wang, N. Verma, ‘Low-resolution acquisition and processing of sensor-data features for statistical-
learning kernels,’ SONIC Annual Review Symposium, Oct. 2014.
3. W. Rieutort-Louis, T. Moy, Z. Wang, N. Verma, ‘Large-scale acquisition and sampling over distributed thin-film
sensory inputs,’ SONIC Annual Review Symposium, Oct. 2014.
4. Z. Wang, K. H. Lee, N. Verma, ‘Highly-scalable, Hardware-specialized Boosting Classification System,’ C-FAR
Zhuo Wang
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Annual Review Symposium, Oct. 2013.
5. G. Hills, Z. Wang, N. Verma, S. Mitra, ‘Embedded Machine Learning to Overcome Carbon Nanotube Variations,’
SONIC Annual Review Symposium, Oct. 2013.
6. J. Zhang, Z. Wang, K. H. Lee, N. Verma, ‘Energy Reduction through Low-precision Analog Enabled by Statistical
Signal Processing and Machine Learning Functions,’ SONIC Annual Review Symposium, Oct. 2013.
7. Z. Wang, K. H. Lee, N. Verma, ‘Embedded Machine Learning Kernel for Hardware Fault Resilience,’ SONIC
Student Research e-Symposium, June 2013.
Academic & Social Services
09/12 till now Systems On Nanoscale Information fabriCs (SONIC) research member
09/12 till now Center for Future Architectures Research (C-FAR) research member
01/13 till now IEEE student member
08/13 till 08/14 Treasurer of Distinguished Citizens Society Princeton University Chapter (DCSPU)
04/12 – 03/13 Secretary General of Association of Chinese Students and Scholars at Princeton University
09/07 – 06/11 Commissary in charge of studies of Class 2011, Yuanpei College, Peking University
Liaison man in charge of discipline of EECS, Yuanpei College, Peking University
Honors & Awards
2015 Princeton University Honorific Fellowship
2014 Best Student Paper Award nomination at ICASSP conference
NSF Travel Grant
2012 Princeton University Fellowship
2011 Best Undergraduate Dissertation Award, by Peking University (awarded to 10 out of 360 students in EECS
Department)
2010 Excellence in Learning Award, by Peking University
Elite Student Scholarship, by Hainan Airlines Company Limited
2009 President's Undergraduate Research Fellowship, by Peking University
2008 Founder Scholarship, by Peking University Founder Group Company Limited
National Physics Competition for Undergraduate Students, the Second Prize
2007 Guanghua Dingli Scholarship for Freshman, by Guanghua Dingli Educational Group
Ranking 6th
out of 560,000 students in National College Entrance Examination in Hebei Province, China
References
Advisor Collaborator & Co-author
Naveen Verma, Ph. D. Robert Schapire, Ph. D.
Associate Professor of Electrical Engineering Principal Researcher
Princeton University Microsoft Research
[email protected] [email protected]
Collaborator & Co-author Thesis Committee Member
James Sturm, Ph. D. Peter Ramadge, Ph. D.
Professor of Electrical Engineering Professor of Electrical Engineering
Princeton University Princeton University