Wei Lu

About

Wei Lu is a Professor of Mechanical Engineering at the University of Michigan, serving as the Associate Chair for Facilities and Planning. He holds multiple doctoral degrees, including a Ph.D. in Materials Science and Engineering from Princeton University and a Ph.D. in Solid Mechanics from Tsinghua University, along with a master's and bachelor's degree from Tsinghua University. His research interests encompass energy storage and electrochemistry, simulation of nano/microstructure evolution, mechanics in nano/micro systems, advanced manufacturing, and the mechanical properties and performance of advanced materials in relation to their microstructures. His work has significantly contributed to understanding nanoscale mechanics, interfacial adhesion in 2D materials, and the development of innovative approaches for electric vehicle battery analysis. Wei Lu has been recognized with numerous awards, including the George J. Huebner, Jr. Research Excellence Award, the Creative, Innovative, Daring Award, and the Gustus L Larson Memorial Award from the American Society of Mechanical Engineers. He has also been selected as a fellow for the Public Engagement Faculty Fellowship program and has received prestigious grants such as the Bill Gates Grand Challenges Grants. His research has been featured in prominent publications like Nature Communications and ACS Central Science, highlighting his contributions to nanoscale mechanics and advanced materials.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Electrical engineering
• Physics
• Computer network
• Computer hardware
• Electronic engineering
• Materials science
• Nanotechnology

Selected publications

• Memristive technologies for data storage, computation, encryption, and radio-frequency communication

  Science · 2022 · 648 citations

  Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non-von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications.…

  DOI

• Training Spiking Neural Networks Using Lessons From Deep Learning

  Proceedings of the IEEE · 2023 · 647 citations

  Senior authorCorresponding

  The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate int…

  DOI

• Dynamical memristors for higher-complexity neuromorphic computing

  Nature Reviews Materials · 2022-04-08 · 528 citations

  reviewSenior author
  PublisherDOI

• Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks

  Nature Electronics · 2020-07-06 · 392 citations

  article
  PublisherDOI

• Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

  ACS Nano · 2023 · 254 citations

  Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristiv…

  DOI

Recent grants

• CAREER: Programmable Nanoscale Self-Assembly on Solid Surfaces

  NSF · $406k · 2004–2010

• SHF: Small: Efficient In-Memory Computing Architecture Based on RRAM Crossbar Arrays

  NSF · $400k · 2016–2019

• Spintronics and Quantum Information Processing with 1D Ge/Si Nanowires

  NSF · $240k · 2006–2010

Frequent coauthors

• Xiaojian Zhu

  30 shared

• Run‐Wei Li

  Chinese Academy of Sciences

  30 shared

• Xiaohong Chen

  Hunan Cancer Hospital

  29 shared

• Jason K. Eshraghian

  University of California, Santa Cruz

  28 shared

• Sungho Kim

  Korea Institute of Industrial Technology

  27 shared

• Chao Du

  Xi'an Jiaotong University

  23 shared

• Yuchao Yang

  Beijing Academy of Artificial Intelligence

  23 shared

• Yuting Wu

  23 shared

Labs

• Wei Lu LaboratoryPI

Education

• Ph.D., Physics and Astronomy

  Rice University

  2003

• B.S, Physics

  Tsinghua University

  1996

Awards & honors

• George J. Huebner, Jr. Research Excellence Award, College of…
• Creative, Innovative, Daring (C/I/D) Award, College of Engin…
• Ted Kennedy Family Faculty Team Excellence Award, College of…
• Gustus L Larson Memorial Award, American Society of Mechanic…
• Distinguished Professor Award, Novelis and College of Engine…

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