Peirong Liu
  
  

Peirong Liu

Researcher @ Harvard Medical School & Massachusetts General Hospital | CS PhD @ UNC-CH

               CV

My name is Peirong Liu (刘沛榕), I am a postdoctoral researcher at Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School & Massachusetts General Hospital, working with Dr. Juan Eugenio Iglesias. I obtained my PhD in Computer Science at UNC-Chapel Hill, where I was beyond fortunate to work with my incredible advisor Dr. Marc Niethammer on computer vision and medical imaging. During PhD, I also spent two summers as a research intern at Meta AI's computer vision team in New York City.

My research interest lies in AI for Healthcare, at an intersection of machine learning (ML), computer vision (CV), data science (DS), and medical image computing (MIC). I have been focusing on:

  • ML/CV Theory & Algorithms: Physics-driven learning for time-varying dynamic systems
  • Interdisciplinary MIC Research: Modality-agnostic foundation models for imperfect data
  • Clinical Applications: Perfusion image analysis, stroke detection and diagnosis, low-field MRI

I am on the 2024-2025 academic job market. Please feel free to reach out!


News

2024

09.09 ~ Named as a Rising Stars in Data Science @ UCSD & UChicago & Stanford

08.16 ~ Named as a Rising Stars in EECS @ MIT

07.12 ~ Received the NIH Award at MICCAI

07.01 ~ One paper accepted at ECCV 2024, “Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging'' [pdf][code]

06.20 ~ Start as a volunteer research mentor for Talaria Summer Institute

06.17 ~ One paper accepted at MICCAI 2024, “PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI'' [pdf][code]

06.01 ~ Serve as a reviewer for NeurIPS 2024

02.02 ~ One paper accepted as oral at ISBI 2024, “Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI'' [pdf][FreeSurfer Toolbox]

01.13 ~ Serve as a reviewer for ECCV 2024

2023

11.13 ~ Serve as a reviewer for ICLR 2024 and CVPR 2024

08.13 ~ I start as a postdoctoral researcher at Harvard Medical School and Massachusetts General Hospital

06.15 ~ I successfully defended my dissertation at UNC-Chapel Hill, I am officially a PhD now!

03.13 ~ Serve as a reviewer for NeurIPS 2023

02.13 ~ Serve as a reviewer for ICCV 2023 and MICCAI 2023

2022

11.13 ~ Serve as a reviewer for CVPR 2023

10.02 ~ Serve as a reviewer for ISBI 2023

05.13 ~ Joining as a Research Intern at Meta AI’s Computer Vision team for Summer 2022

03.02 ~ One paper accepted as oral at CVPR 2022, “Deep Decomposition for Stochastic Normal-Abnormal Transport'' [pdf]

02.26 ~ Serve as a reviewer for ECCV 2022

02.13 ~ Serve as a reviewer for MICCAI 2022

2021

11.13 ~ Serve as a reviewer for CVPR 2022

09.28 ~ One paper accepted at NeurIPS 2021, “Accurate Point Cloud Registration with Robust Optimal Transport'' [pdf]

07.13 ~ One paper accepted at ICCV 2021, “Local Temperature Scaling for Probability Calibration'' [pdf]

05.13 ~ One paper accepted at IEEE TMI, “Perfusion Imaging: An Advection Diffusion Approach'' [pdf]

04.13 ~ Serve as a reviewer for ICCV 2021

03.22 ~ Joining as a Research Intern at Facebook AI’s Computer Vision team for Summer 2021

03.13 ~ One paper accepted as oral at CVPR 2021, “Discovering Hidden Physics Behind Transport Dynamics'' [pdf]

2020

08.15 ~ Awarded the MICCAI Student Travel Award, Lima, Peru

06.13 ~ One paper accepted at MICCAI 2020, “Fluid Registration Between Lung CT and Stationary Chest Tomosynthesis Images'' [pdf]

05.13 ~ One paper early accepted as oral at MICCAI 2020, “PIANO: Perfusion Imaging via Advection-Diffusion'' [pdf]

2019

06.02 ~ Awarded the IPMI Scholarship, Hongkong, China

02.26 ~ One paper accepted as oral at IPMI 2019, “Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces'' [pdf]

2018

08.13 ~ Joined as a PhD student in the Dept. of Computer Science at UNC-Chapel Hill

06.13 ~ Graduated with B.S. in Mathematics from Shanghai University

01.13 ~ Nominated for Outstanding Graduate of Shanghai, China

2017

11.13 ~ Awarded the National Fellowship (top 1%) from Shanghai University

10.13 ~ Awarded the Top Grade Scholarship (top 3%), Outstanding Student, Academic Innovation Scholarship, and Leadership Scholarship from Shanghai University

09.13 ~ Awarded the Baogang Fellowship (top 4) from Shanghai University

05.13 ~ Awarded the President’s List (top 10) from Shanghai University

03.13 ~ Received the Finalist Winner (36/8843) in U.S. Mathematical Contest In Modeling (MCM)

2016

11.13 ~ Received the Third Prize in Shanghai Mathematics Competitions (Math Major)

09.13 ~ Awarded the Top Grade Scholarship (top 3%), Outstanding Student, Academic Innovation Scholarship, and Leadership Scholarship from Shanghai University

03.13 ~ Received the Second Prize in U.S. Mathematical Contest In Modeling (MCM)

2015

09.13 ~ Awarded the Top Grade Scholarship (top 3%), Outstanding Student, and Public Service Scholarship from Shanghai University

Expand News →


Selected Publications

Peirong Liu, Oula Puonti, Xiaoling Hu, Daniel C. Alexander, Juan E. Iglesias. “Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging”. European Conference on Computer Vision (ECCV), Milan, 2024. [code]

Peirong Liu, Oula Puonti, Annabel Sorby-Adams, William T. Kimberly, Juan E. Iglesias. “PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI”. Medical Image Computing and Computer Assisted Intervention (MICCAI), Marrakesh, 2024. [code]

Peirong Liu, Yueh Z. Lee, Stephen R. Aylward, Marc Niethammer. “Deep Decomposition for Stochastic Normal-Abnormal Transport”. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, 2022. (Oral - 4%) [code]

Peirong Liu, Lin Tian, Yubo Zhang, Stephen R. Aylward, Yueh Z. Lee, Marc Niethammer. “Discovering Hidden Physics Behind Transport Dynamics”. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, 2021. (Oral - 3.7%) [code]

Peirong Liu, Yueh Z. Lee, Stephen R. Aylward, Marc Niethammer. “Perfusion Imaging: An Advection Diffusion Approach”. IEEE Transactions on Medical Imaging (IEEE TMI), 2021. [code]

Full List →


Honors and Awards

2024 ~ Rising Stars in Data Science @ UCSD & UChicago & Stanford

2024 ~ Rising Stars in EECS @ MIT

2024 ~ NIH Award @ MICCAI

2020 ~ Student Travel Award @ MICCAI

2019 ~ IPMI Scholarship @ IPMI

2018 ~ National Scholarship @ Ministry of Education of China

2018 ~ Outstanding Graduate @ Ministry of Education of China

2017 ~ Presidential Scholarship (Top 10) @ Shanghai University

2017 ~ Baogang Scholarship (Top 4) @ Shanghai University

2017 ~ Finalist Winner (Top 0.4%, 36/8843) @ Mathematical Contest In Modeling (MCM)

2017 ~ Top Grade Scholarship (Top 3%) @ Shanghai University

2016 ~ Top Grade Scholarship (Top 3%) @ Shanghai University

2015 ~ Top Grade Scholarship (Top 3%) @ Shanghai University


Professional Service

Conference Reviewer @ NeurIPS | ICLR | CVPR | ECCV | ICCV | AAAI | MICCAI | IPMI | ISBI | WiCV

Journal Reviewer @ IEEE TMI | Computer Graphics Forum | Frontier in Radiology | PLOS ONE

Volunteer Research Mentor @ Talaria