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Peng Li
Professor
of Electrical & Computer Engineering, IEEE Fellow
Education: Ph.D. in ECE, Carnegie
Mellon University
Current Research:
Neuromorphic Computing, Applied Machine Learning,
Hardware Machine Learning Systems, VLSI Circuits
& Computer-Aided Design
Bio
I received the Ph.D. degree in electrical and
computer engineering from Carnegie
Mellon University, Pittsburgh, PA in 2003 and the M.
Eng. degree in systems engineering and B.Eng. degree in
information science & engineering from Xi’an Jiaotong University,
Xi’an, China, in 1997 and 1994, respectively.
I
have been a Professor of ECE at UCSB since July 2019,
where I'm affiliated with the Computer
Engineering (CE) Program. From August 2004 to June
2019, I was with the Department
of Electrical and Computer Engineering at Texas A&M University
as an assistant, associate, and then a full professor. I
was was a member of Faculty
of Neuroscience and the Graduate Faculty
of School of
Graduate Studies, Health
Science Center at Texas A&M.
My
research interests are in neuromorphic (a.k.a
brain-inspired) computing, applied machine learning,
circuits and architectures for hardware machine learning
systems, VLSI circuits and computer-aided design. I
am a co-editor of the book, entitled "Simulation
and Verification of Electronic and Biological Systems"
(Springer, 2011), co-edited one additional book, and
authored and co-authored over 200 journal and conference
publications, and seven book chapters.
My
work has been recognized by various distinctions including
the ICCD
Best Paper Award in 2020, the ICCAD
Ten Year Retrospective Most Influential Paper Award in
2019, four IEEE/ACM Design
Automation Conference (DAC) Best Paper Awards
in 2003, 2008, 2011, and 2016, the ISCAS Honorary Mention
Best Paper Award from the Neural Systems and Applications
Technical Committee of IEEE Circuits and Systems (CAS)
Society at IEEE
International Symposium on Circuits and Systems (ISCAS) in
2016, the IEEE/ACM William J. McCalla ICCAD Best
Paper Award from IEEE/ACM
International Conference on Computer-Aided Design (ICCAD)
in 2012, the US National Science Foundation CAREER Award
in 2008, two Inventor Recognition Awards from Microelectronics
Advanced Research Corporation (MARCO) in 2006
and 2007, two Semiconductor
Research Corporation (SRC) Inventor
Recognition Awards in 2001 and 2004. At the 50th IEEE/ACM
Design Automation Conference in 2013, I was
honored with the Best Paper Hat Trick Award for receiving
the prestigious DAC best paper award three times,
the DAC Prolific Author Award, and the DAC Top 10 Author
in Fifth Decade Award. I recieved several Best Paper Award
nominations from ICCAD.
At Texas A&M, I was recongnized with the 2018-2019
Eugene E. Webb Faculty Fellow, 2013-2014 William and
Montine P. Head Fellow, 2011-2012 TEES Fellow Award from
the College
of Engineering, and the ECE Outstanding Professor
Award in 2008.
I
was an Associate Editor for IEEE
Transactions on Computer-Aided Design of Integrated
Circuits and Systems from 2008 to 2013 and an
associate editor for IEEE
Transactions on Circuits and Systems II from
2008 to 2016. I have been on the committees of many
international conferences and workshops such as DAC,
ISLPED, IJCNN, ICCAD, ICCD, ISQED, ISCAS, TAU
and FAC, ICCAD Best Paper Award Committee, and
the selection committee for ACM Outstanding Ph.D.
Dissertation Award in Electronic Design Automation. He
served as the technical program committee chair for the
ACM TAU Workshop in 2009 and general chair for the 2010
Workshop. I was a member of the Executive Committee and
the Vice President for Technical Activities of IEEE
Council on Electronic Design Automation (CEDA) from
Jan. 2016 to Dec. 2017.
I
have consulted for Intel Corporation and several
Silicon-Valley startup companies. I am a Fellow of the
IEEE and a member of the ACM.
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Announcements/News
- Our
TMLR'24 paper "Extreme
Risk Mitigation in Reinforcement Learning using Extreme
Value Theory" was invited to be presented at
ICLR'25. Congratulations to Karthik and Yu!
- 2024
ICCAD McCarthy Best Paper Award Nomination: Our
ICCAD'24 paper "Spiking Transformer Hardware Accelerators
in 3D Integration" was nominated for the ICCAD McCarthy
Best Paper Award. Congratulations to Boxun!
- ICML'24
Spotlight Paper: Our paper “High-Dimensional
Bayesian Optimization via Semi-Supervised Learning with
Optimized Unlabeled Data Sampling" was selected as a
Spotlight (3.5% acceptance rate) out of 9,473 submissions
by the International Conference on Machine Learning (ICML)
in 2024. Congratulations to Yuxuan and Yu !
- ICCD'20
Best Paper Award: Our paper “Reconfigurable
Dataflow Optimization for Spatiotemporal Spiking Neural
Computation on Systolic Array Accelerators" received the
Best Paper Award in Processor Architecture Track from the
International Conference on Computer Design (ICCD).
Congratulations to Jeong-Jun!
- NeurIPS'20
spotlight paper: Our paper “Temporal Spike
Sequence Learning via Backpropagation for Deep Spiking
Neural Networks” has been accepted as a Spotlight Paper by
NeurIPS'20 (280 spotlights
out of 9,454 paper submissions). Congratulations to Wenrui
!
- ICCAD
Ten-Year Retrospective Most Influential Paper
Award for the paper co-authored with Yenpo Ho,
and Garng Huang, titled "Nonvolatile memristor memory:
device characteristics and design implications", published
at ICCAD 10 years
ago in Nov. 2009.
- Highly
motivated undergraduate and graduate student researchers,
visiting scholars and students are invited to join Li's
group.
- Post-doctoral
researchers are sought after to work on
brain-inspired computing (a.k.a. neuromorphic computing),
anomaly detection, robust learning under system
uncertainty, and hardware machine learning systems.
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