Yan Luo

Orcid: 0000-0001-5135-0316

Affiliations:
  • Harvard University, Harvard Ophthalmology AI Laboratory, Cambridge, MA, USA
  • University of Minnesota Twin Cities, Department of Computer Science and Engineering, Minneapolis, MN, USA (PhD 2022)
  • National University of Singapore, Department of Electrical and Computer Engineering, Singapore (former)


According to our database1, Yan Luo authored at least 27 papers between 2015 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2025
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA.
CoRR, August, 2025

Learning to Predict Gradients for Semi-Supervised Continual Learning.
IEEE Trans. Neural Networks Learn. Syst., February, 2025

Equitable artificial intelligence for glaucoma screening with fair identity normalization.
npj Digit. Medicine, 2025

2024
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization.
IEEE Trans. Medical Imaging, July, 2024

RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps.
Medical Image Anal., 2024

Impact of Data Distribution on Fairness Guarantees in Equitable Deep Learning.
CoRR, 2024

FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation.
CoRR, 2024

TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction.
CoRR, 2024

FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification.
Proceedings of the Computer Vision - ECCV 2024, 2024

FairCLIP: Harnessing Fairness in Vision-Language Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma.
IEEE J. Biomed. Health Informatics, September, 2023

Learning to Minimize the Remainder in Supervised Learning.
IEEE Trans. Multim., 2023

FairSeg: A Large-scale Medical Image Segmentation Dataset for Fairness Learning with Fair Error-Bound Scaling.
CoRR, 2023

Harvard Eye Fairness: A Large-Scale 3D Imaging Dataset for Equitable Eye Diseases Screening and Fair Identity Scaling.
CoRR, 2023

Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization.
CoRR, 2023

Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images.
CoRR, 2022

2021
Direction Concentration Learning: Enhancing Congruency in Machine Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

Learning to Predict Trustworthiness with Steep Slope Loss.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
G-Softmax: Improving Intraclass Compactness and Interclass Separability of Features.
IEEE Trans. Neural Networks Learn. Syst., 2020

n-Reference Transfer Learning for Saliency Prediction.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
G-softmax: Improving Intra-class Compactness and Inter-class Separability of Features.
CoRR, 2019

Visual Attention in Multi-Label Image Classification.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2015
Multi-Camera Saliency.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Foveation-based Mechanisms Alleviate Adversarial Examples.
CoRR, 2015

Label Consistent Quadratic Surrogate model for visual saliency prediction.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015


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