Tel: +1(609)937-7476, Email: zhuow@princeton.edu

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

nverma@princeton.edu schapire@cs.princeton.edu

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

sturm@princeton.edu ramadge@princeton.edu