Publications

Paper citations: [Google Scholar] [Semantic Scholar] [arXiv Personal Page] [ORCID]

• Preprints
• Journals
• Conferences and Workshops
• Dissertation
• Unpublished Reports

Preprints

Deep Learning with Constraints: Applications and Algorithms.
    Chuan He, Ryan Devera, Wenjie Zhang, Ying Cui, Zhaosong Lu, Ju Sun. 2024.

Neural Topology Optimization with Principled Constrained Optimization.
    Ryan Devera, Buyun Liang, Binyao Guo, Qizhi He, Ju Sun. 2024.

A Strong Baseline for Removing Invisible Watermarks on Images via Deep Image Prior.
    Hengyue Liang, Taihui Li, Ju Sun. 2024.

Direct Metric Optimization for Imbalanced Classification.
    Le Peng, Yash Travadi, Chuan He, Ying Cui, Ju Sun. 2024.

Regression with High-Dimensional Targets.
    Tiancong Chen, Hengkang Wang, Chuan He, Ying Cui, Ju Sun. 2024.

Optimization and Optimizers for Adversarial Robustness. [arXiv]
    Hengyue Liang, Buyun Liang, Le Peng, Ying Cui, Tim Mitchell, Ju Sun. Under review for International Journal of Computer Vision (IJCV), 2023.

Phase Retrieval: the Role of Overparametrization and New Methods.
    Zhong Zhuang, Yash Travadi, Zhihui Zhu, Ju Sun. In preparation for SIAM Journal on Mathematics of Data Science (SIMODS), 2023.

NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning. [arXiv] (an expanded version with detailed examples on constrained deep learning [arXiv])
    Buyun Liang, Tim Mitchell, Ju Sun.

Inverse Problems, Deep Learning, and Symmetry Breaking. [arXiv]
    Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar, Ju Sun. Mar 2020.

Dual-Reference Design for Holographic Coherent Diffraction Imaging. [arXiv]
    David Barmherzig, Ju Sun, Po-Nan Li, TJ Lane, and Emmanuel J. Candès. Feb 2019.

Subgradient Descent Learns Orthogonal Dictionaries. [arXiv]
    Yu Bai, Qijia Jiang, Ju Sun. 2018.

Journals

Selective Classification Under Distribution Shifts. [arXiv]
    Hengyue Liang, Le Peng, Ju Sun. Transactions on Machine Learning Research (TMLR), 2024.

Risk Prediction of Heart Diseases in Breast Cancer Patients: A Deep Learning Approach with Longitudinal Electronic Health Records Data.
    Sicheng Zhou, Anne Blaes, Chetan Shenoy, Ju Sun, Rui Zhang. iScience, 2024.

Federated Learning with Convex Global and Local Constraints. [arXiv]
    Chuan He, Le Peng, Ju Sun. Transactions on Machine Learning Research (TMLR), 2024.

Unraveling the Multiple Chronic Conditions Patterns Among People with Alzheimer’s Disease and Related Dementia–A Machine Learning Approach to Incorporate Synergistic Interactions. [paper]
    Pui Ying Yew, Ryan Devera, Yue Liang, Razan A. El Khaifa, Ju Sun, Nai-Ching Chi, Ying-Chyi Chou, Peter J. Tonellato, Chih-Lin Chi. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 2024.

An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing. [arXiv] [medRxiv]
    Le Peng, Gaoxiang Luo, Sicheng Zhou, Jiandong Chen, Ziyue Xu, Rui Zhang, Ju Sun. npj Digital Medicine, 2024.

Interpretable Deep Learning Methods for Multiview Learning. [arXiv] [paper]
    Hengkang Wang, Han Lu, Ju Sun, Sandra Safo. BMC Bioinformatics, 25(1):1–30, 2024.

Blind Image Deblurring with Unknown Kernel Size and Substantial Noise. [arXiv] [paper]
    Zhong Zhuang, Taihui Li, Hengkang Wang, Ju Sun. International Journal of Computer Vision (IJCV), 132(2):319–348, 2024.

Early Stopping for Deep Image Prior. [arXiv] [openreview]
    Hengkang Wang, Taihui Li, Zhong Zhuang, Tiancong Chen, Hengyue Liang, Ju Sun. Transactions on Machine Learning Research (TMLR), 2023.

A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records. [arXiv] [paper]
    Sicheng Zhou, Nan Wang, Liwei Wang, Ju Sun, Anne Blaes, Hongfang Liu, Rui Zhang. Computational and Structural Biotechnology Journal, 22:32–40, 2023.

Automated Quantification of Eye Tics using Computer Vision and Deep Learning Techniques. [paper (open access)]
    Christine Conelea, Hengyue Liang, Megan DuBois, Brittany Raab, Mia Kellman, Brianna Wellen, Suma Jacob, Sonya Wang, Ju Sun, Kelvin Lim. Movement Disorders, 39(1):183–191, 2024.

Effects of Personalized Exercise Prescriptions and Social Media through m-Health on Cancer Survivors’ Physical Activity and Quality of Life. [paper]
    Zan Gao, Suryeon Ryu, Wanjiang Zhou, Kaitlyn Adams, Mohamed Hassan, Rui Zhang, Anne Blaes, Julian Wolfson, Ju Sun. Journal of Sport and Health Science. 12(6):705–714, 2023.

A Unified Analysis of AdaGrad with Weighted Aggregation and Momentum Acceleration. [arXiv] [paper]
    Shen Li, Congliang Chen, Fangyu Zou, Zequn Jie, Ju Sun, Wei Liu. IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2023.

Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective. [arXiv]
    Yash Travadi, Le Peng, Xuan Bi, Ju Sun, Mochen Yang. Statistics and Its Interface. 2023.

Evaluation of Federated Learning Variations for COVID-19 Diagnosis using Chest Radiographs from 42 US and European Hospitals. [paper (open access)]
    Le Peng, Gaoxiang Luo, Andrew Walker, Zachary Zaiman, Emma K. Jones, Hemant Gupta, Kristopher Kersten, John L. Burns, Christopher A. Harle, Tanja Magoc, Benjamin Shickel, Scott D. Steenburg, Tyler Loftus, Genevieve B. Melton, Judy Wawira Gichoya, Ju Sun, Christopher J Tignanelli. Journal of the American Medical Informatics Association (JAMIA), 30(1):54–63, 2023.

A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals. [medRxiv] [arXiv] [paper]
    Ju Sun, Le Peng, Taihui Li, Dyah Adila, Zach Zaiman, Genevieve B. Melton, Nicholas Ingraham, Eric Murray, Daniel Boley, Sean Switzer, John L. Burns, Kun Huang, Tadashi Allen, Scott D. Steenburg, Judy Wawira Gichoya, Erich Kummerfeld, Christopher Tignanelli. Radiology: Artificial Intelligence, 4(4), 2022.

Towards Practical Holographic Coherent Diffraction Imaging via Maximum Likelihood Estimation. [paper (open access)][arXiv]
    David A. Barmherzig, Ju Sun. Optics Express, 30(5):6886–6906, 2022.

Potential and Limitations of Radiomics in Neuro-oncology. [Paper]
    Birra Taha, Daniel Boley, Ju Sun, Clark C. Chen. Journal of Clinical Neuroscience, 90:206–211, 2021.

State of Radiomics in Glioblastoma. [Paper]
    Birra Taha, Daniel Boley, Ju Sun, Clark C. Chen. Neurosurgery, 89(2):177–184, 2021.

Detection of Isocitrate Dehydrogenase Mutated Glioblastomas through Anomaly Detection Analytics. [Paper]
    Birra Taha, Taihui Li, Daniel Boley, Clark C. Chen, Ju Sun. Neurosurgery, 89(2):323–328, 2021.

Dictionary Learning in Fourier Transform Scanning Tunneling Spectroscopy. [arXiv] [Paper]
    Sky Cheung, John Shin, Yenson Lau, Zhengyu Chen, Ju Sun, Yuqian Zhang, John Wright, and Abhay Pasupathy. Nature Communications, 11, 1081, 2020.

Holographic Phase Retrieval and Reference Design. [arXiv] [Paper]
    David Barmherzig, Ju Sun, Po-Nan Li, TJ Lane, and Emmanuel J. Candès. Inverse Problems, 35(9):094001, 2019.

High-Speed Channel Modeling with Machine Learning Methods for Signal Integrity Analysis. [Draft][Paper]
    Tianjian Lu, Ju Sun, Ken Wu, Zhiping Yang. IEEE Trans. Electromagnetic Compatibility, 60(6):1957–1964, 2018.

A Geometrical Analysis of Phase Retrieval. [extended abstract][arXiv] [Paper]
    Ju Sun, Qing Qu, John Wright. Foundations of Computational Mathematics, 18(5):1131–1198, 2018.

Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture. [summary][Arxiv] [Paper][Codes][Arxiv-Full Report]
    Ju Sun, Qing Qu, John Wright. IEEE Trans. Information Theory, 63(2): 853 - 884, 2017.

Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method. [summary][Arxiv] [Paper] [Codes][Arxiv-Full Report]
    Ju Sun, Qing Qu, John Wright. IEEE Trans. Information Theory, 63(2): 885 – 914, 2017.

Finding a Sparse Vector in a Subspace: Linear Sparsity Using Alternating Direction. [arXiv] [Paper] [Codes]
    Qing Qu, Ju Sun, John Wright. IEEE Trans. Information Theory, 62(10): 5855 - 5880, 2016.

Efficient Point-to-Subspace Query in $\ell^1$ with Application to Robust Object Instance Recognition. [arXiv] [paper]
    Ju Sun, Yuqian Zhang, John Wright. SIAM J. Imaging Sciences (SIIMS), 7(4):2105 - 2138, 2014.

Robust Recovery of Subspace Structures by Low-Rank Representation. [arXiv] [paper] [Codes¹]
    Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, and Yi Ma. IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), 35(1):171 - 184, 2013.

Conferences and Workshops

DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models. [arXiv]
    Hengkang Wang, Xu Zhang, Taihui Li, Yuxiang Wan, Tiancong Chen, Ju Sun. Conference on Neural Information Processing Systems (NeurIPS), 2024.

What’s Wrong with End-to-End Learning for Phase Retrieval?. [arXiv]
    Wenjie Zhang, Yuxiang Wan, Zhuang Zhong, Ju Sun. Machine Learning for Scientic Imaging at Electronic Imaging, 2024.

Blind Image Deblurring with Unknown Kernel Size and Substantial Noise. [arXiv]
    Zhuang Zhong, Taihui Li, Hengkang Wang, Ju Sun. NeurIPS’23 Workshop on Deep Learning and Inverse Problems, 2023.

Phase Retrieval Using Double Deep Image Priors. [arXiv]
    Zhuang Zhong, David Yang, Felix Hofmann, David Barmherzig, Ju Sun. NeurIPS’23 Workshop on Deep Learning and Inverse Problems, 2023.

Federated Learning with Convex Global and Local Constraints. [arXiv] [openreview]
    Chuan He, Le Peng, Ju Sun. NeurIPS’23 Workshop on Optimization for Machine Learning, 2023.

Rethink Transfer Learning in Medical Image Classification. [arXiv] [Project website]
    Le Peng, Hengyue Liang, Gaoxiang Luo, Taihui Li, Ju Sun. British Machine Vision Conference (BMVC, Oral), 2023.

A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. [paper]
    Le Peng, Sicheng Zhou, Jiandong Chen, Rui Zhang, Ziyue Xu, Ju Sun. International Workshop on Federated Learning for Distributed Data Mining (in KDD 2023), 2023.

Implications of Solution Patterns on Adversarial Robustness. [arXiv]
    Hengyue Liang, Buyun Liang, Le Peng, Ying Cui, Tim Mitchell, Ju Sun. The 3rd Workshop of Adversarial Machine Learning on Computer Vision: Art of Robustness (in conjunction with CVPR 2023), 2023.

Deep Random Projector: Accelerated Deep Image Prior. [paper]
    Taihui Li, Hengkang Wang, Zhong Zhuang, Ju Sun. Computer Vision and Pattern Recognition (CVPR), 2023.

Random Projector: Efficient Deep Image Prior.
    Taihui Li, Zhong Zhuang, Hengkang Wang, Ju Sun. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.

Robust Autoencoders for Collective Corruption Removal. [arXiv]
    Taihui Li, Hengkang Wang, Le Peng, XianE Tang, Ju Sun. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.

Optimization for Robustness Evaluation beyond $\ell_p$ Metrics. [arXiv]
    Hengyue Liang, Buyun Liang, Ying Cui, Tim Mitchell, Ju Sun. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.

Practical Phase Retrieval Using Double Deep Image Priors. [arXiv]
    Zhong Zhuang, David Yang, Felix Hofmann, David Barmherzig, Ju Sun. Computational Imaging XXI at Electronic Imaging, 2023.

Imbalanced Classification in Medical Imaging via Regrouping. [arXiv]
    Le Peng, Yash Travadi, Rui Zhang, Ying Cui, Ju Sun. NeurIPS Workshop on Medical Imaging Meets NeurIPS, 2022.

Optimization for Robustness Evaluation beyond $\ell_p$ Metrics. [arXiv]
    Hengyue Liang, Buyun Liang, Ying Cui, Tim Mitchell, Ju Sun. NeurIPS Workshop on Optimization for Machine Learning, 2022.

NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning. [arXiv]
    Buyun Liang, Tim Mitchell, Ju Sun. NeurIPS Workshop on Optimization for Machine Learning, 2022.

Self-Validation: Early Stopping for Single-Instance Deep Generative Priors. [arXiv] [Project website]
    Taihui Li, Zhong Zhuang, Hengyue Liang, Le Peng, Hengkang Wang, Ju Sun. British Machine Vision Conference (BMVC), 2021.

Phase Retrieval using Single-Instance Deep Generative Prior. [arXiv]
    Kshitij Tayal, Raunak Manekar, Zhong Zhuang, David Yang, Vipin Kumar, Felix Hofmann, Ju Sun. OSA Imaging and Applied Optics Congress, 2021.

Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval. [paper] [poster]
    Kshitij Tayal, Chieh-Hsin Lai, Raunak Manekar, Zhong Zhuang, Vipin Kumar, Ju Sun. NeurIPS 2020 Workshop on Deep Learning and Inverse Problems, 2020.

Deep Learning Initialized Phase Retrieval. [paper] [poster]
    Raunak Manekar, Zhong Zhuang, Kshitij Tayal, Vipin Kumar, Ju Sun. NeurIPS 2020 Workshop on Deep Learning and Inverse Problems, 2020.

Rethink Autoencoders: Robust Manifold Learning. [paper]
    Taihui Li, Rishabh Mehta, Zecheng Qian, Ju Sun. ICML workshop on Uncertainty and Robustness in Deep Learning, 2020.

Phase Retrieval via Second-Order Nonsmooth Optimization. [paper]
    Zhong Zhuang, Gang Wang, Yash Travadi, Ju Sun. ICML workshop on Beyond First Order Methods in Machine Learning, 2020.

End-to-End Learning for Phase Retrieval. [paper]
    Raunak Manekar, Kshitij Tayal, Vipin Kumar, Ju Sun. ICML workshop on ML Interpretability for Scientific Discovery, 2020.

Inverse Problems, Deep Learning, and Symmetry Breaking. [paper]
    Kshitij Tayal, Chieh-Hsin Lai, Raunak Manekar, Vipin Kumar, Ju Sun. ICML workshop on ML Interpretability for Scientific Discovery, 2020.

Low-Photon Holographic Phase Retrieval. [paper]
    David A. Barmherzig, Ju Sun. OSA Imaging and Applied Optics Congress 2020.

Dual-Reference Design for Holographic Phase Retrieval. [arXiv] [Paper]
    David Barmherzig, Ju Sun, Emmanuel J. Candès, TJ Lane, and Po-Nan Li. International Conference on Sampling Theory and Application (SampTA), 2019.

Subgradient Descent Learns Orthogonal Dictionaries. [Paper] [arXiv]
    Yu Bai, Qijia Jiang, Ju Sun. International Conference on Learning Representations (ICLR), 2019.

When Nonsmoothness Meets Nonconvexity.
    Yu Bai, Qijia Jiang, Ju Sun, Emmanuel Candès.

• Invited talk at Allerton Conference on Communication, Control, and Computing 2018. [Slides]

1D Phase Retrieval and Spectral Factorization. [draft] [paper] [slides]
    David A. Barmherzig, Ju Sun. OSA Imaging and Applied Optics Congress 2018.

On Block-Reference Coherent Diffraction Imaging. [draft] [paper] [slides]
    David A. Barmherzig, Ju Sun, T.J. Lane, and Po-Nan Li. OSA Imaging and Applied Optics Congress 2018.

A Local Analysis of Block Coordinate Descent for Gaussian Phase Retrieval. [arXiv][Codes]
    David Barmherzig, Ju Sun. NIPS Workshop on Optimization for Machine Learning, 2017.

A Geometrical Analysis of Phase Retrieval. [extended abstract][arXiv]
    Ju Sun, Qing Qu, John Wright. International Symposium on Information Theory (ISIT), 2016.

When Are Nonconvex Problems Not Scary?. [arXiv] [poster]
    Ju Sun, Qing Qu, John Wright. NIPS Workshop on Non-convex Optimization for Machine Learning: Theory and Practice, 2015.

Complete Dictionary Recovery over the Sphere. [summary] [Extended Abstract] [Arxiv]
    Ju Sun, Qing Qu, John Wright.

• Short Abstract in International Conference on Sampling Theory and Applications (SampTA), 2015.
• Short Abstract in Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS) [Best Student Paper Award], 2015.
• Extended Abstract in International Conference on Machine Learning (ICML), 2015.

Finding a Sparse Vector in a Subspace: Linear Sparsity Using Alternating Direction. [arXiv] [paper] [poster] [Codes]
    Qing Qu, Ju Sun, John Wright. Advances in Neural Information Processing Systems (NIPS), 2014.

Efficient Point-to-Subspace Query in $\ell^1$: Theory and Applications in Computer Vision [arXiv].
    Ju Sun, Yuqian Zhang, John Wright. NIPS Workshop on Big Learning, 2012.

Efficient Point-to-Subspace Query in $\ell^1$ with Application to Robust Face Recognition.[arXiv][Poster][Video Highlight]
    Ju Sun, Yuqian Zhang, John Wright. European Conference on Computer Vision (ECCV), 2012.

Closed-Form Solutions to a Category of Nuclear Norm Minimization Problems. [arXiv]
    Guangcan Liu, Ju Sun, Shuicheng Yan. NIPS Workshop on Low-Rank Methods for Large-Scale Machine Learning, 2010.

Robust Low-Rank Subspace Segmentation with Semi-Definite Guarantees. [arXiv] [Slides]
    Yuzhao Ni, Ju Sun, Xiaotong Yuan, Shuicheng Yan, Loong-Fah Cheong. ICDM Workshop on Optimization Based Methods for Emerging Data Mining Problems (OEDM), 2010.

Randomized Locality Sensitive Vocabularies for Bag-of-Features Model. [PDF]
    Yadong Mu, Ju Sun, Shuicheng Yan, Loong-Fah Cheong. European Conference on Computer Vision (ECCV), 2010.

Activity Recognition Using Dense Long-Duration Trajectories. [PDF] [Slides]
    Ju Sun, Yadong Mu, Shuicheng Yan, Loong-Fah Cheong. International Conference on Multimedia & Expo (ICME), 2010 (Invited paper to special session).

Hierarchical Spatio-Temporal Context Modeling for Action Recognition. [PDF]
    Ju Sun, Xiao Wu, Shuicheng Yan, Loong-Fah Cheong, Tat-Seng Chua, Jintao Li. IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Oral), 2009.

3D Ordinal Constraint in Spatial Configuration for Robust Scene Recognition. [PDF]
    Ching-Lik Teo, Shimiao Li, Loong-Fah Cheong, Ju Sun. IEEE International Conference on Pattern Recognition (ICPR), 2008.

Dissertation

When Are Nonconvex Optimization Problems Not Scary? [PDF] [Official Version]

Unpublished Reports

Selective Image Super-Resolution. [arXiv]
    Ju Sun, Qiang Chen, Shuicheng Yan, Loong-Fah Cheong. Mar 2010.