Junshen Xu  
(徐 俊燊)

prof_pic.jpg

Room 36-776A

50 Vassar St.

(MIT building 36)

Cambridge, MA 02139

Hi! I am currently a fifth-year Ph.D. student at MIT majoring in Electrical Engineering and Computer Science. My research focuses on machine learning and developing robust and efficient algorithms driven by clinical problems. Applications include motion-robust 3D rendering of the human brain, real-time quality assessment in MR scans as well as pose estimation and motion characterization of fetuses. I am advised by Prof. Elfar Adalsteinsson and collaborate closely with Prof. Polina Golland and Prof. P. Ellen Grant.

I also did summer internships at Google and Meta, working on automated Ads bidding and large-scale video recommendation systems respectively.

Prior to MIT, I received my Bachelor’s degree from Tsinghua University in 2018. I also spent a summer as a research assistant at Stanford, where I was advised by Prof. John Pauly and Prof. Greg Zaharchuk.

news

Apr 25, 2023 We just released NeSVoR v0.2.0. Check it out!
Apr 19, 2023 I successfully defended my doctoral thesis today!
Mar 16, 2023 Our paper entitled “Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI” was accepted by MRM!
Feb 21, 2023 Our paper entitled “Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space” was accepted by MIDL 2023!
Jan 9, 2023 Our paper entitled “NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI” was accepted by IEEE TMI!

selected publications

  1. ×
    NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI
    Junshen XuDaniel MoyerBorjan GagoskiJuan Eugenio Iglesias, and 3 more authors
    IEEE Transactions on Medical Imaging, 2023
  2. ×
    SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI
    Junshen XuDaniel MoyerP. Ellen GrantPolina Golland, and 2 more authors
    In Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
  3. ×
    Automated Detection and Reacquisition of Motion-Degraded Images in Fetal HASTE Imaging at 3 T
    Borjan Gagoski*Junshen Xu*, Paul Wighton, M. Dylan Tisdall, and 6 more authors
    Magnetic Resonance in Medicine, 2022
  4. ×
    Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
    Junshen Xu*Molin Zhang*Esra Abaci Turk, Larry Zhang, and 4 more authors
    In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
  5. ×
    Multi-Scale Neural ODEs for 3D Medical Image Registration
    Junshen Xu, Eric Z. Chen, Xiao Chen, Terrence Chen, and 1 more author
    In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021