Learning, Discovery, Reasoning Group @ JHU

The LDR Group at JHU conducts interdisciplinary research in AI, with a focus on Machine Learning, Computer Vision, and Healthcare & Biomedical Applications. We develop AI systems that learn from complex, multimodal, and longitudinal data, discover meaningful biological and clinical patterns, and ultimately reason for informed decision-making.

Our long-term goal is to improve healthcare outcomes by:
-> Developing foundational methods for complex biomedical data;
-> Translating methodological advances into tools that support diagnosis, prognosis, treatment planning, and clinical decision-making.

Our work spans the full translational spectrum, from foundational machine learning and algorithm development to clinical validation and real-world deployment. We publish across leading venues in machine learning (e.g., NeurIPS, ICLR, ICML), computer vision (e.g., CVPR, ICCV, ECCV), and medical imaging (e.g., MICCAI, IPMI, IEEE TMI).

★★ If you are interested in joining us, please check out our openings ★★

Peirong at CVPR 2026 Tutorial — ***Peirong @ CVPR 2026 Tutorial — Physics-informed Learning for Interpretable Medical AI***

LDR Group photo — ***We welcome fluffy members!***

News

[Upcoming MICCAI 2026 Challenge — FOMO] We will host the 2nd FOMO Challenge (foundation model challenge for Brain MRI) at MICCAI 2026. To joined our challenge, check out FOMO26’s official website here.

2026

08.08 ~ Congratulations to Yi-Chen (Matthew) for his paper accepted at MICCAI 2026 as an ORAL presentation: “HemoPIC: A Physics-Informed Cerebral Hemodynamics Digital Twin for Brain Perfusion'' [pdf][code]

06.18 ~ Congratulations to Jun for her paper accepted at ECCV 2026: “Physics-Grounded Disentangled Flow Modeling for Brain Disease Progression Trajectory'' [pdf][code]

06.07 ~ Peirong served as Medical Vision Oral Session Chair at CVPR 2026.

06.04 ~ We hosted “Recent Advances in AI for Medical Imaging: Progress, Challenges, and Future Directions'' at CVPR 2026 Tutorial, check our latest progress on “Physics-Driven Learning for Interpretable Medical AI'' [slides]

05.20 ~ Peirong was recognized as Outstanding Area Chair at CVPR 2026.

04.01 ~ Peirong served as Area Chair at NeurIPS 2026.

01.26 ~ Peirong served as Area Chair at MICCAI 2026.

2025

12.01 ~ Check out our new work, on unifying the bidirectional generation and editing of pathology-healthy brain imaging [pdf][code]

09.18 ~ We received 4 x RTX PRO 6000 Blackwell GPU NVIDIA Academic Grant to support our research on physics-informed deep learning.

08.20 ~ Peirong served as Area Chair at ICLR 2026 and CVPR 2026.

07.20 ~ Check out our preprint for FOMO-60k (a large-scale heterogeneous 3D 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]

07.01 ~ Peirong joined JHU as an Assistant Professor in the Dept. of Electrical and Computer Engineering (ECE) and Data Science and Artificial Intelligence Institute (DSAI).

04.01 ~ We will host FOMO - the first foundation model challenge for Brain MRI - at MICCAI 2025. To joined our challenge, check out FOMO's official website here.

03.01 ~ Peirong served 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]

Older news (2018–2024)

2024

09.09 ~ Peirong was named as a Rising Star in Data Science by UCSD, UChicago, and Stanford.

08.16 ~ Peirong was named as a Rising Star in EECS by MIT.

07.12 ~ Peirong 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 ~ Peirong started 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 ~ Peirong joined as a postdoctoral researcher at Harvard Medical School and Massachusetts General Hospital.

2022

05.13 ~ Peirong joined 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 ~ Peirong joined 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 ~ Peirong 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 ~ Peirong 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]

Research Topics

The topics below represent several of our current research directions. Student projects are designed collaboratively based on interests, technical background, and clinical relevance.