Peirong Liu
Postdoc @ Harvard Medical School & Massachusetts General Hospital | PhD in CS @ UNC-Chapel Hill
★★ Starting in Fall 2025, I will join the Department of Electrical and Computer Engineering (ECE) and the Data Science and AI Institute (DSAI) at the Johns Hopkins University (JHU) as an assistant professor. I have multiple openings for PhD / Postdoc / Intern, please refer to our LDR Group for application details. ★★
My name is Peirong Liu (刘沛榕), I am a postdoctoral researcher at Harvard Medical School & Massachusetts General Hospital, hosted by Dr. Juan Eugenio Iglesias. I received my PhD in Computer Science from UNC-Chapel Hill in 2023, where I was beyond fortunate to work with my incredible advisor, Dr. Marc Niethammer. 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 broadly lie in AI for Healthcare, at an intersection of machine learning (ML), computer vision (CV), and medical image computing (MIC), aiming to advance foundational theories for learning and representation, and establish general frameworks that support complex real-world systems.
- ML/CV Theory & Algorithms: Physics-informed Deep Learning; Spatiotemporal Modeling; Representation Learning; Generative Modeling
- Interdisciplinary MIC: Modality-agnostic Multi-task foundation Models in Medical Imaging; Image Generation, Reconstruction, Segmentation, Registration
- Clinical Applications: Brain Perfusion; Stroke Diagnosis; Lesion Detection and Segmentation; Low-field MRI
[My Research Overview] - Motivated by real-world applications, I leverage my interdisciplinary expertise in machine learning, computer vision, and mathematics, to support various application areas. I strive for foundational approaches that provide interpretability and efficiency, ultimately applying them for practical challenges.
News
2025
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.
02.26 ~ Our work UNA was accepted at CVPR 2025, see you in June in the capital of country music!
01.24 ~ Check out our new work UNA: “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]
01.16 ~ Serve as Area Chair at MICCAI 2025
2024
09.09 ~ Named as a Rising Star in Data Science @ UCSD & UChicago & Stanford
08.16 ~ Named as a Rising Star in EECS @ 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]
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]
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
Selected Publications
Peirong Liu, Ana Lawry Aguila, Juan E. Iglesias. “Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization”. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [pdf][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), 2024. [pdf][code]
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), 2024. [pdf][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), 2022. (Oral - 4%) [pdf][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), 2021. (Oral - 3.7%) [pdf][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. [pdf][code]
Peirong Liu, Yueh Z. Lee, Stephen R. Aylward, Marc Niethammer. “PIANO: Perfusion Imaging via Advection-Diffusion”. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020. (Oral, Early Accept - 13%) [pdf][code]
Peirong Liu, Zhengwang Wu, Gang Li, Pew-Thian Yap, Dinggang Shen. “Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces”. Information Processing in Medical Imaging (IPMI), 2019. (Oral - 10%) [pdf][code]
Selected 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
Services
Meta Reviewer (Area Chair) @ MICCAI
Conference Reviewer @ NeurIPS | ICLR | ICML | CVPR | ECCV | ICCV | MICCAI | IPMI | AAAI | MIDL | ISBI | WiCV
Journal Reviewer @ IEEE TMI | Medical Image Analysis | Computer Graphics Forum | Frontier in Radiology
Volunteer Research Mentor @ Talaria