About

Hui Chen is the Nomura Professor of Finance and a Professor of Finance at the MIT Sloan School of Management. His research focuses on asset pricing and its connections with corporate finance, with particular interest in the interactions between the macro economy, credit risk, and corporate financing or investment decisions. His recent projects include applying business cycle models to explain corporate financing behavior and corporate bond pricing, as well as analyzing the effects of incomplete markets on entrepreneurial financing and investments. Chen's current research emphasizes the impact of financial frictions on asset pricing and corporate decisions, and explores the intersections of economics and machine learning. Notably, he studies how financial distress influences price competition among firms and how machine learning tools can be integrated with economic models to improve computational efficiency.

Research topics

• Computer Science
• Physics
• Telecommunications
• Optics
• Artificial Intelligence
• Engineering
• Electronic engineering
• Computer hardware

Selected publications

• Photonic matrix multiplication lights up photonic accelerator and beyond

  Light Science & Applications · 2022 · 513 citations

  • Computer Science
  • Computer Science
  • Telecommunications

  Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerators are designed to accelerate specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for computing resources and capacity. Photonic matrix multiplication has much potential to expand the domain of telecommunication, and artificial intelligence benefiting from its superior performance. Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors. In this review, we first introduce the methods of photonic matrix multiplication, mainly including the plane light conversion method, Mach-Zehnder interferometer method and wavelength division multiplexing method. We also summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years. Finally, we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

  DOI

• Deep-learning-enabled self-adaptive microwave cloak without human intervention

  Nature Photonics · 2020 · 558 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science
  DOI

Frequent coauthors

• Chao Qian

  Jinhua Academy of Agricultural Sciences

  452 shared

• Bin Zheng

  Zhejiang University

  324 shared

• Xiao Lin

  Zhejiang University

  309 shared

• Er‐Ping Li

  State Key Laboratory of Modern Optical Instruments

  282 shared

• Zhixiang Fan

  Jinhua Academy of Agricultural Sciences

  261 shared

• Yihao Yang

  260 shared

• Yuetian Jia

  Zhejiang University-University of Edinburgh Institute

  214 shared

• Fei Gao

  Zhejiang University

  183 shared

Labs

• MIT Sloan Climate StrategyPI

Education

• PhD

  Zhejiang University

  2005

Awards & honors

• Warga Award (2019)

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• Phillip A. Sharph-227