About

Dr. Xin Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University. He directs the Smart Power, Energy, and Decision-making (SPEED) Lab at TAMU ECE, where his research lies at the intersection of control, optimization, and AI for human-cyber-physical systems, with particular applications to power systems. The SPEED lab aims to develop scalable data-driven decision-making theories, algorithms, and tools to enhance the intelligence, reliability, and sustainability of modern power and energy systems. Dr. Chen received his Ph.D. in electrical engineering from Harvard University, working with Prof. Na Li, and holds a master’s degree in electrical engineering and two bachelor’s degrees in engineering and economics from Tsinghua University. Prior to joining TAMU, he was a Postdoctoral Associate affiliated with the MIT Energy Initiative at MIT. His research contributions include developing models and algorithms for grid stability, power system control, and optimization, with recent projects involving AI-driven models for data centers, large load modeling, and grid analysis. Dr. Chen has received numerous awards, including the NSF CAREER Award, and actively contributes to the academic community through organizing workshops, serving on technical program committees, and leading collaborative projects at the forefront of power system research.

Research topics

• Computer Science
• Embedded system
• Systems engineering
• Engineering
• Computer Security
• Manufacturing engineering
• Composite material
• Materials science
• Optoelectronics
• Software engineering
• Control engineering
• Mechanical engineering
• Nanotechnology
• Optics

Selected publications

• Digital twins-based smart manufacturing system design in Industry 4.0: A review

  Journal of Manufacturing Systems · 2021 · 700 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Security
  • Engineering
  DOI

• Interfacial Laser‐Induced Graphene Enabling High‐Performance Liquid−Solid Triboelectric Nanogenerator

  Advanced Materials · 2021 · 295 citations

  • Materials science
  • Nanotechnology
  • Optoelectronics

  from the impact of a water droplet 105 µL from a height of 25 cm. Importantly, the device exhibits superior cyclability and operational stability under high humidity and various pH conditions. The facile process developed can be extended to realize various functional devices.

  DOI

• Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system

  Journal of Manufacturing Systems · 2020 · 299 citations

  Senior authorCorresponding
  • Computer Science
  • Engineering
  • Control engineering
  DOI

Frequent coauthors

• Jian Gao

  Inner Mongolia Electric Power Survey & Design Institute (China)

  111 shared

• Yun Chen

  Jiangsu University

  86 shared

• Yunbo He

  51 shared

• Hui Tang

  University of Warwick

  40 shared

• Ching‐Ping Wong

  Georgia Institute of Technology

  40 shared

• Maoxiang Hou

  Guangdong University of Technology

  33 shared

• Zhijun Yang

  32 shared

• Jiewu Leng

  Guangdong University of Technology

  28 shared

Labs

• S mart P ower, E n e rgy, and D ecision-making (SPEED) LabPI

Awards & honors

• Outstanding Student Paper Award (2021)

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