I am currently an Associate at McKinsey & CompanyI received my Ph.D in ECE from the University of Michigan, Ann Arbor in 2020, M.S. in EE from the University of California, Los Angeles (UCLA) in 2015 and my B.S. in EE from National Tsing Hua University (NTHU) in 2012. My past research focuses on domain-specific computing architectures and paradigms for accelerating machine learning, deep learning, computer vision and robotics.

In 2018, I took a gap year from my Ph.D. to serve as a Technical Founding Partner of Iluvatar CoreX, an AI chip and systems startup based in the Silicon Valley and Greater China. My experiences and expertise encapsulate R&D, corporate strategy, product management and investment in the field of AI and the semiconductor industry.


University of Michigan, Ann Arbor

Ph.D. in Electrical and Computer Engineering

Sep 2016 - Sep 2020

Ann Arbor, MI, USA

University of California, Los Angeles

M.S. in Electrical Engineering

Sep 2013 - May 2015

Los Angeles, CA, USA

National Tsing Hua University

B.S. in Electrical Engineering

Sep 2008 - Jun 2012

Hsinchu, TW


McKinsey & Company


Nov 2020 - Present

Shanghai, CN


     Corporate Strategy Manager Intern

July 2020 - Sep 2020

San Jose, CA, USA

     Corporate Strategy Intern

Apr 2020 - Jun 2020

Iluvatar CoreX (天数智芯)

Technical Founding Partner, Head of Advanced Technologies Group

Dec 2017 - Jan 2019

San Jose, CA, USA

Nvidia Research

Research Intern

Jun 2017 - Sep 2017

Santa Clara, CA, USA

Intel Labs

     Senior Research Scientist

     Graduate Technical Intern

May 2015 - May 2016

Santa Clara, CA, USA

Sep 2014 - Dec 2014

Silicon Image

ASIC Design Intern

Jun 2014 - Sep 2014

Sunnyvale, CA, USA


SoC Design Intern

Jun 2010 - Sep 2010

Hsinchu, TW

COPi Group

Co-Founder, General Manager

Apr 2020 - Present

New York, NY, USA


Wolverine Venture Fund

Investment Associate

Oct 2019 - Jan 2020

Ann Arbor, MI, USA


Oct 2017

$50,000 Startup Award

TUS International Limited Gap Fund

$100,000 Startup Award

Michigan Translational Research and Commercialization (MTRAC)

Jun 2017

Sep 2016 - Sep 2020

Rackham Graduate Fellowship

University of Michigan, Ann Arbor


SNAP: A 1.67-21.55TOPS/W Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network

Inference in 16nm CMOS

​J-F. Zhang, C-E Lee, C. Liu, S. Shao, S. Keckler, Z. Zhang

2019 Symposium on VLSI Circuits (VLSIC), Kyoto, Japan, 2019

A 135mW 1.70TOPS Sparse Video Sequence Inference SoC for Action Classification

T.Chen, C-E Lee, Z. Zhang

Journal of Solid-State Circuits (JSSC) , 2019

Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA

C. Fu, S. Zhu, H. Su, C-E Lee, J. Zhao

International Symposium on Field-Programmable Gate Arrays (FPGA) 2019, Monterey Bay, US, 2019

Stitch-X: An Accelerator Architecture for Exploiting Unstructured Sparsity in Deep Neural Networks

C-E Lee, S. Shao, J-F Zhang, A. Parashar, J. Emer, S. Keckler, Z. Zhang

Conference on Systems and Machine Learning (SysML), Stanford, US, 2018

A 127mW 1.63TOPS Sparse Spatio-Temporal Cognitive SoC for Action Classification and Motion Tracking in Videos

C-E Lee, T.Chen, Z. Zhang

2017 Symposium on VLSI Circuits (VLSIC), Kyoto, Japan, 2017.

High-performance Spiking Neural Net Accelerators for Embedded Computer Vision Applications 

JK. Kim, P, Knag, T. Chen, C. Liu, C-E Lee, Z. Zhang

IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (S3S), San Jose, US, 2017

An Error-Compensated Piecewise Linear Logarithmic Arithmetic Unit for Phong Lighting Acceleration

C-E Lee, M.D. Ercegovac

IEEE Proc. 49th Asilomar Conference on Signals, Systems & Computers (ACSSC), Asilomar, US, 2015

Reconfigurable and Selectively-Adaptive Signal Processing for Multi-Mode Wireless Communication

F. Sheikh, O. Andersson, C-E Lee, F. Xue, C-H Chen, A. Vaidya, A. Sharma, T. Tetzlaff

IEEE International Workshop on Signal Processing Systems (SiPs), Hangzhou, China, 2015


Methods and Apparatus for Similar Data Reuse in Dataflow Processing Systems (pending

Two-Level, Fine-Grained Programmable Architecture to Accelerate Deep Learning Workloads (pending)

Methods and Apparatus for Designing Flexible Dataflow Processor for Artificial Intelligent Devices (US20200042868A1)

Deep Neural Network Accelerator With Fine-Grained Parallelism Discovery (US20190370645A1)
Sparse Video Inference Processor For Action Classification And Motion Tracking (US20180349764A1)
Methods and Devices for Self-Interference Cancelation (US20190229884A1)
RLS-DCD Adaptation Hardware Accelerator for Interference Cancellation in Full-Duplex Wireless Systems (US20170085252A1)

Energy Efficient Polynomial Kernel Generation in Full-Duplex Radio Communication (US20160380653A1)


lchingen [at] outlook [dot] com

Associate at McKinsey & Company

Ph.D in Electrical & Computer Engineering