Yunrui Zhang 张芸睿

Logo ECE Ph.D. @ Cornell University (2029, expected)
Logo B.Eng., Tsinghua University (2024)

Hi! I am Yunrui Zhang, a third-year Electrical and Computer Engineering Ph.D. student at Cornell University advised by Prof. Amy Kuceyeski and Prof. Mert Sabuncu. I am currently a research intern at Siemens Healthineers, where I work on multimodal AI for medical imaging.
I received my Bachelor's degree from 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 at the intersection of AI/ML and healthcare, with a focus on functional magnetic resonance imaging (fMRI) 🧠, brain-behavior mapping, and surgical robotics 🦾.
During my leisure time, I enjoy classic Chinese literature & poetry 📜, fiction 📖, piano 🎹 and musicals 🎶.

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Education
  • Cornell University

    Cornell University

    Ph.D. in Electrical and Computer Engineering Aug. 2024 - Present

  • Tsinghua University

    Tsinghua University

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

Honors & Awards
  • Irwin Jacobs Fellowship, Cornell University 2024
  • Tsinghua Innovation Award of Science and Technology 2023, 2022
  • First Prize in Beijing Challenge Cup 2023
  • Tsinghua Award of Outstanding Public Service 2022
Experience
  • Siemens Healthineers

    Siemens Healthineers

    AI Research Intern May. 2026 - Present

  • University of Illinois at Urbana-Champaign

    University of Illinois at Urbana-Champaign

    Summer Research Intern Jul. 2023 - Sep. 2023

News
2026
Started summer internship as Multimodal AI Medical Imaging Research Intern at Siemens Healthineers!
May 18
Abtract accepted as poster presentation to OHBM2026!
Feb 12
2025
The preprint of "View, engage, predict: enhancing brain-behavior mapping with naturalistic movie-watching fMRI" is available on bioRxiv now! Check it out at https://doi.org/10.1101/2025.07.28.666907 !
Aug 01
Passed Cornell ECE PhD Q exam!
May 01
Abtract accepted as poster presentation to OHBM2025!
Feb 11
2024
Joined Prof. Amy Kuceyeski and Prof. Mert Sabuncu's lab as a PhD student @ Cornell ECE!
Aug 26
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 view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI
A view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI

Yunrui Zhang, Emily S. Finn, Mert R. Sabuncu, Amy Kuceyeski

Nature Communications Biology , 2026

How does the human brain respond while watching movies, and what can this reveal about individual traits? In this work, we show that certain movie clips can predict cognitive scores and sex from fMRI functional connectivity more effectively than five times as much resting-state data. For cognition prediction, a movie clip’s predictive power is strongly linked to how well it synchronizes brain activity across people and to the amount of social and human-related content. These effects were not observed for sex prediction.

[Code] [Paper]

A view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI
A view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI

Yunrui Zhang, Emily S. Finn, Mert R. Sabuncu, Amy Kuceyeski

Nature Communications Biology , 2026

How does the human brain respond while watching movies, and what can this reveal about individual traits? In this work, we show that certain movie clips can predict cognitive scores and sex from fMRI functional connectivity more effectively than five times as much resting-state data. For cognition prediction, a movie clip’s predictive power is strongly linked to how well it synchronizes brain activity across people and to the amount of social and human-related content. These effects were not observed for sex prediction.

[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]

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