Yunrui Zhang 张芸睿

Incoming ECE Ph.D. Student @ Cornell University
Logo B.Eng., Tsinghua University (2024)

Hi! I am Yunrui Zhang (张芸睿), an incoming ECE Ph.D. student at Cornell University. I obtained my bachelor's degree at Tsinghua University, where I was very fortunate to be advised by Prof. Gangtie Zheng, Prof. Mingguo Zhao and Prof. Rebing Wu.
My research interests lie broadly in the intersection of machine learning and advanced healthcare, including Magnetic Resonance Imaging (MRI)🧠, multi-modal medical imaging and surgical robotics🦾.
During my leisure time, I am an enthusiast of classic Chinese literature & poetry 📜, fiction 📖, piano 🎹 and musicals 🎶.

Photo taken in Jun 2024 with Jida.

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Education
  • Tsinghua University

    Tsinghua University

    B.Eng. in Automation Sep. 2020 - Jun. 2024

Honors & Awards
  • Tsinghua Innovation Award of Science and Technology 2023, 2022
  • First Prize in Beijing Challenge Cup 2023
  • Tsinghua Award of Outstanding Public Service 2022
Experience
  • University of Illinois at Urbana-Champaign

    University of Illinois at Urbana-Champaign

    Summer Research Intern Jul. 2023 - Sep. 2023

News
2024
Graduated from Tsinghua University!
Jun 28
Gave Power Pitch presentation on ISMRM 2024, Singapore!
May 08
Visited The Florey Institute of Neuroscience and Mental Health and the University of Sydney's Brain and Mind Centre in Australia!
Jan 30
2023
Started summer research internship advised by Prof. Fan Lam @ UIUC ECE!
Jul 17
Intelligent Cosmetic Surgical Robot won First Prize in Beijing Challenge Cup!
May 18
Selected Publications (view all )
A (k,t)-RAKI Method for Interpolating Sparse Data in Accelerated MRSI Acquisitions
A (k,t)-RAKI Method for Interpolating Sparse Data in Accelerated MRSI Acquisitions

Yunrui Zhang, Ruiyang Zhao, Zepeng Wang

International Society of Magnetic Resonance in Medicine (ISMRM) 2024 Power Pitch

In this work, we adapted and extended the self-supervised learning-based RAKI method by incorporating the FID dimension into a 3D, complex-valued convolutional network, for MRSI reconstruction. We improved the design by training a single network to handle multi-coil data simultaneously instead of the coil-by-coil interpolation in the original RAKI method. We demonstrate reduced aliasing by the proposed method and consequently improved spatiospectral processing results, using in vivo 1H-MRSI data.

[Code] [Paper]

A (k,t)-RAKI Method for Interpolating Sparse Data in Accelerated MRSI Acquisitions
A (k,t)-RAKI Method for Interpolating Sparse Data in Accelerated MRSI Acquisitions

Yunrui Zhang, Ruiyang Zhao, Zepeng Wang

International Society of Magnetic Resonance in Medicine (ISMRM) 2024 Power Pitch

In this work, we adapted and extended the self-supervised learning-based RAKI method by incorporating the FID dimension into a 3D, complex-valued convolutional network, for MRSI reconstruction. We improved the design by training a single network to handle multi-coil data simultaneously instead of the coil-by-coil interpolation in the original RAKI method. We demonstrate reduced aliasing by the proposed method and consequently improved spatiospectral processing results, using in vivo 1H-MRSI data.

[Code] [Paper]

All publications