He/Him
Assistant Professor
Contact
6-213 Kenneth H. Keller Hall
200 Union Street Se
Minneapolis, MN 55455
He/Him
Assistant Professor
Ju Sun’s group works at the intersection of machine learning, numerical optimization, computer vision, and data science, and our recent focus is to develop the theoretical and computational foundations of machine learning in general and deep learning in particular, as well as apply them to tackle challenging scientific, engineering, and medical problems.
Research Division
Robotics and Artificial Intelligence
Current Research
Google Scholar
Group of Learning, Optimization, Vision, healthcarE, and X (GLOVEX)
Recent News
Ju Sun Receives $4.5M in Funding for Medical AI Projects and Beyond
Ju Sun Part of $1.2M Grant to Study Role of AI in Breast Cancer Treatment
Ph.D. in Electrical Engineering, Columbia University (2016)
Bachelors of Engineering in Electrical and Computer Engineering, National University of Singapore (2008)
Ju Sun joined the Department of Computer Science & Engineering as an assistant professor in 2019. He also serves as the director of the Group of Learning, Optimization, Vision, healthcarE, and X and of the Healthcare Computer Vision Program at the University. Sun received his Ph.D. (2016) in electrical engineering from Columbia University. Before joining the University, he served as a research engineer at the National University of Singapore from 2008-11 and as a postdoctoral scholar at Stanford University from 2016-19.
Visit Ju Sun's Experts@Minnesota profile page.
Ju Sun Part of $1.2M Grant to Study Role of AI in Breast Cancer Treatment
Ju Sun Co-Led Project Using AI Technology for COVID-19 Diagnosis
Collaboration with Cisco explores frontier of data technologies
Team receives $1.2 million NSF grant to develop smart e-scooters
Artificial intelligence, real-life results
Robotics faculty members awarded new research grants
Researchers develop algorithm to analyze chest X-rays for COVID-19
CS&E welcomes Assistant Professors Fazzini and Sun
Selected Grants
Federated and imbalanced learning for medical NLP
Sun, J.; CISCO SYSTEMS, INC; 12/7/22 - 12/31/23
CPS: Medium: Smart Tracking Systems for Safe and Smooth Interactions Between Scooters and Road Vehicles
Rajamani, R., Morris, N. L. & Sun, J.; National Science Foundation; 1/1/21 - 12/31/23
FAIR Framework for Physics-Inspired AI in High Energy Physics
Sun, J.; Department of Energy; 1/1/20 - 12/31/23
Shen, L., Chen, C., Zou, F., Jie, Z., Sun, J., Liu, W. (2023). A Unified Analysis of AdaGrad With Weighted Aggregation and Momentum Acceleration. IEEE Transactions on Neural Networks and Learning Systems. [1-9].
DOI: 10.1109/TNNLS.2023.3279381
Peng, L., Luo, G., Walker, A., Zaiman, Z., Jones, E.K., Gupta, H., Kersten, K., Burns, J.L., Harle, C.A., Magoc, T., Shickel, B., Steenburg, S.D., Loftus, T., Melton, G.B., Gichoya, J.W., Sun, J., Tignanelli, C.J. (2023). Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals. Journal of the American Medical Informatics Association. 30 (1), [54-63].
DOI: 10.1093/jamia/ocac188
Final published version
Sun, J., Peng, L., Li, T., Adila, D., Zaiman, Z., Melton-Meaux, G.B., Ingraham, N.E., Murray, E., Boley, D., Switzer, S., Burns, J.L., Huang, K., Allen, T.L., Steenburg, S.D., Gichoya, J.W., Kummerfeld, E., Tignanelli, C.J. (2022). Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study. Radiology: Artificial Intelligence. 4 (4), [e210217].
DOI: 10.1148/ryai.210217
Barmherzig, D.A., Sun, J. (2022). Towards practical holographic coherent diffraction imaging via maximum likelihood estimation. Optics Express. 30 (5), [6886-6906].
DOI: 10.1364/OE.445015
Manekar, R., Tayal, K., Zhuang, Z., Lai, C.H., Kumar, V., Sun, J. (2021). Breaking symmetries in data-driven phase retrieval. Optics InfoBase Conference Papers. [CTh4A.4].
Taha, B., Li, T., Boley, D., Chen, C.C., Sun, J. (2021). Detection of Isocitrate Dehydrogenase Mutated Glioblastomas through Anomaly Detection Analytics. Neurosurgery. 89 (2), [323-328].
DOI: 10.1093/neuros/nyab130
Tayal, K., Manekar, R., Zhuang, Z., Yang, D., Kumar, V., Hofmann, F., Sun, J. (2021). Phase retrieval using single-instance deep generative prior. Optics InfoBase Conference Papers. [JW2A.37].
Taha, B., Boley, D., Sun, J., Chen, C. (2021). Potential and limitations of radiomics in neuro-oncology. Journal of Clinical Neuroscience. [206-211].
DOI: 10.1016/j.jocn.2021.05.015
Taha, B., Boley, D., Sun, J., Chen, C.C. (2021). State of Radiomics in Glioblastoma. Neurosurgery. 89 (2), [177-184].
DOI: 10.1093/neuros/nyab124
Cheung, S.C., Shin, J.Y., Lau, Y., Chen, Z., Sun, J., Zhang, Y., Müller, M.A., Eremin, I.M., Wright, J.N., Pasupathy, A.N. (2020). Dictionary learning in Fourier-transform scanning tunneling spectroscopy. Nature communications. 11 (1), [1081].
DOI: 10.1038/s41467-020-14633-1
Barmherzig, D.A., Sun, J. (2020). Low-photon holographic phase retrieval. Optics InfoBase Conference Papers. [JTu4A.6].
Barmherzig, D.A., Sun, J., Candes, E.J., Lane, T.J., Li, P.N. (2019). Dual-Reference Design for Holographic Phase Retrieval. In 2019 13th International Conference on Sampling Theory and Applications, SampTA 2019. (2019 13th International Conference on Sampling Theory and Applications, SampTA 2019.). Institute of Electrical and Electronics Engineers Inc..
DOI: 10.1109/SampTA45681.2019.9030848
Barmherzig, D.A., Sun, J., Li, P.N., Lane, T.J., Candès, E.J. (2019). Holographic phase retrieval and reference design. Inverse Problems. 35 (9), [094001].
DOI: 10.1088/1361-6420/ab23d1
Bai, Y., Jiang, Q., Sun, J. (2019). Subgradient descent learns orthogonal dictionaries. Paper presented at 7th International Conference on Learning Representations, ICLR 2019.
Barmherzig, D., Sun, J. (2018). 1D phase retrieval and spectral factorization. In Laser Applications to Chemical, Security and Environmental Analysis, LACSEA 2018. (Optics InfoBase Conference Papers. Part F103-LACSEA 2018.). Optica Publishing Group (formerly OSA).
DOI: 10.1364/LACSEA.2018.JTh1A.4
Sun, J., Qu, Q., Wright, J. (2018). A Geometric Analysis of Phase Retrieval. Foundations of Computational Mathematics. 18 (5), [1131-1198].
DOI: 10.1007/s10208-017-9365-9
Lu, T., Wu, K., Yang, Z., Sun, J. (2018). High-speed channel modeling with deep neural network for signal integrity analysis. In 2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017. (1-3). (2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017. 2018-January.). Institute of Electrical and Electronics Engineers Inc..
DOI: 10.1109/EPEPS.2017.8329733
Lu, T., Sun, J., Wu, K., Yang, Z. (2018). High-Speed Channel Modeling with Machine Learning Methods for Signal Integrity Analysis. IEEE Transactions on Electromagnetic Compatibility. 60 (6), [8245823]. [1957-1964].
DOI: 10.1109/TEMC.2017.2784833
Barmherzig, D., Sun, J., Li, P.N., Lane, T.J. (2018). On block-reference coherent diffraction imaging. In Computational Optical Sensing and Imaging, COSI 2018. (Optics InfoBase Conference Papers. Part F99-COSI 2018.). Optica Publishing Group (formerly OSA).
DOI: 10.1364/COSI.2018.CTH1B.1
Nguyen, T., Lu, T., Sun, J., Le, Q., We, K., Schut-Aine, J. (2018). Transient Simulation for High-Speed Channels with Recurrent Neural Network. In EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems. (303-305). (EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems.). Institute of Electrical and Electronics Engineers Inc..
DOI: 10.1109/EPEPS.2018.8534232