Peirong Liu
Assistant Professor in ECE & DSAI @ Johns Hopkins University
AI for Healthcare | Generative AI | Computer Vision | Medical Imaging
My name is Peirong Liu (刘沛榕), I am an assistant professor of Electrical and Computer Engineering (ECE) at the Johns Hopkins University, affiliated with the Data Science and Artificial Intelligence (DSAI) Institute.
Before joining Johns Hopkins, I was a postdoctoral researcher at Harvard Medical School & Massachusetts General Hospital, hosted by Dr. Juan Eugenio Iglesais. I received my PhD in Computer Science from UNC-Chapel Hill in 2023, where I was beyond fortunate to work with Dr. Marc Niethammer. During PhD, I also spent two wonderful summers as a research intern at Meta AI in New York City. I was recognized as a Rising Star in EECS by MIT, and a Rising Star in Data Science by UCSD, UChicago and Stanford.
My research interests lie in AI for Healthcare, with a focus on Generative AI, Computer Vision, and Medical Image Analysis. For more information, please check out my Group page.
News
2025
09.18 ~ Awarded NVIDIA Academic Grant on Physics-informed Deep Learning with 4 x RTX PRO 6000 Blackwell GPU.
08.20 ~ Serve as Area Chair at ICLR 2026 and CVPR 2026
07.20 ~ Check out our preprint for FOMO-60k (a large-scale heterogeneous 3D brain MRI dataset) here.
07.16 ~ One paper accepted at MICCAI Deep Generative Models Workshop (DGM4MICCAI) 2025: “Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization'' [pdf]
04.01 ~ We will host FOMO - the first foundation model challenge for Brain MRI - at MICCAI 2025. To join our challenge, check out FOMO's official website here.
03.01 ~ Serve as Area Chair at MICCAI 2025
02.26 ~ One paper accepted at CVPR 2025: “Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization'' [pdf][code]
01.22 ~ One paper accepted at ICLR 2025: “Hierarchical uncertainty estimation for learning-based registration in neuroimaging'' [pdf][code]
2024
09.09 ~ Named as a Rising Star in Data Science by UCSD, UChicago, and Stanford.
08.16 ~ Named as a Rising Star in EECS by MIT.
07.12 ~ Received the NIH Award at MICCAI 2024.
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]
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]
2023
08.13 ~ Join as a postdoctoral researcher in Athinoula A. Martinos Center for Biomedical Imaging at Harvard Medical School and Massachusetts General Hospital.
06.15 ~ Successfully defended my dissertation at UNC-Chapel Hill, I am officially a PhD now!
2022
05.13 ~ Join 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][code]
2021
09.28 ~ One paper accepted at NeurIPS 2021: “Accurate Point Cloud Registration with Robust Optimal Transport'' [pdf][code]
07.13 ~ One paper accepted at ICCV 2021: “Local Temperature Scaling for Probability Calibration'' [pdf][code]
05.13 ~ One paper accepted at IEEE TMI: “Perfusion Imaging: An Advection Diffusion Approach'' [pdf][code]
03.22 ~ Join 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][code]
2020
08.15 ~ Received the Student Travel Award at MICCAI 2020.
06.13 ~ One paper accepted at MICCAI 2020: “Fluid Registration Between Lung CT and Stationary Chest Tomosynthesis Images'' [pdf][code]
05.13 ~ One paper Early accepted as Oral at MICCAI 2020: “PIANO: Perfusion Imaging via Advection-Diffusion'' [pdf][code]
2019
06.02 ~ Received the IPMI Scholarship at IPMI 2019.
02.26 ~ One paper accepted as Oral at IPMI 2019: “Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces'' [pdf][code]
2018
08.13 ~ Join as a PhD student in the Dept. of Computer Science at UNC-Chapel Hill.
Selected Awards
2025 ~ Academic Grant Award @ NVIDIA
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
Recent Services
Board Member @ Women in MICCAI (WiM)
Meta Reviewer (Area Chair) @ ICLR | CVPR | MICCAI
Conference Reviewer @ NeurIPS | ICLR | ICML | CVPR | ECCV | ICCV | MICCAI | IPMI | AAAI | MIDL | ISBI | WiCV
Journal Reviewer @ Nature Communications | IEEE Transactions on Medical Imaging | Medical Image Analysis | Computer Graphics Forum
Volunteer Research Mentor @ Talaria Summer Institute
Selected Publications (Full List ->)
Peirong Liu, Ana Lawry Aguila, Juan Eugenio Iglesias
CVPR, 2025
paper / code
Xiaoling Hu, Karthik Gopinath, Peirong Liu, Malte Hoffmann, Koen Van Leemput, Oula Puonti,
Juan Eugenio Iglesias
ICLR, 2025
paper / code
Peirong Liu, Oula Puonti, Annabel Sorby-Adams, William Taylor Kimberly, Juan Eugenio Iglesias
MICCAI, 2024
paper / code
Peirong Liu, Oula Puonti, Xiaoling Hu, Daniel C. Alexander, Juan Eugenio Iglesias
ECCV, 2024
paper / code
Peirong Liu, Yueh Z. Lee, Stephen R. Aylward, Marc Niethammer
CVPR, 2022 (Oral - 4%)
paper / code
Peirong Liu, Lin Tian, Yubo Zhang, Stephen R. Aylward, Yueh Z. Lee, Marc Niethammer
CVPR, 2021 (Oral - 3.7%)
paper / code
Zhengyang Shen*, Jean Feydy*, Peirong Liu, Ariel Hernán Curiale, Ruben San José Estépar,
Raúl San José Estépar, Marc Niethammer
NeurIPS, 2021
paper / code
Zhipeng Ding, Xu Han, Peirong Liu, Marc Niethammer
ICCV, 2021
paper / code
Peirong Liu, Yueh Z. Lee, Stephen R. Aylward, Marc Niethammer
IEEE Transactions on Medical Imaging (TMI), 2021
paper / code
Peirong Liu, Yueh Z. Lee, Stephen R. Aylward, Marc Niethammer
MICCAI, 2020 (Oral, Early Accept - 13%)
paper / code
Peirong Liu, Zhengwang Wu, Gang Li, Pew-Thian Yap, Dinggang Shen
IPMI, 2019 (Oral - 10%)
paper / code